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--- title: Involvement of acid sensing ion channel (ASIC)-3 in an acute urinary bladder-colon cross sensitization model in rodent authors: - Karim Atmani - Mathieu Meleine - Ludovic Langlois - Moïse Coëffier - Pablo Brumovsky - Anne-Marie Leroi - Guillaume Gourcerol journal: Frontiers in Pain Research year: 2023 pmcid: PMC10030710 doi: 10.3389/fpain.2023.1083514 license: CC BY 4.0 --- # Involvement of acid sensing ion channel (ASIC)-3 in an acute urinary bladder-colon cross sensitization model in rodent ## Abstract ### Introduction Irritable bowel syndrome and bladder pain syndrome are both characterized by pain in response to organ distension. Epidemiologic studies showed that these two syndromes are often overlapped. Such overlap may be due to sharing of common extrinsic innervations between the colorectum and the urinary bladder, where cross-sensitization of the urinary bladder and the colon would occur in response to mechanical distension of either organ. The aim of this project was to develop and characterize a rodent model of urinary bladder-colon sensitization and to assess the role of the acid sensing ion channel (ASIC)-3. ### Methods Double retrograde labelling was performed to identify extrinsic primary afferent neurons innervating both the colon (Fluororuby) and urinary bladder (Fluorogold) in the L6-S1 dorsal root ganglia (DRG) in Sprague Dawley rats. The phenotype of the colon/urinary bladder co-innervating primary afferent neurons was assessed using immunohistochemistry directed against ASIC-3. Cross-organ sensitization was induced in Sprague Dawley rats by using an echography-guided intravesical administration of acetic acid ($0.75\%$) under brief isoflurane anesthesia. Colonic sensitivity was assessed in conscious rats by measuring abdominal contraction during isobaric colorectal distension (CRD). Measurement of urinary bladder and colonic paracellular permeabilities and tissue myeloperoxidase assay were performed. The involvement of ASIC-3 was assessed by use of S1 intrathecal administration of the ASIC-3 blocker, APETx2 (2.2 µM). ### Results Immunohistochemistry showed that $73.1\%$ of extrinsic primary afferent neurons co-innervating the colon and the urinary bladder express ASIC-3. By contrast, extrinsic primary afferent neurons innervating the colon only or the urinary bladder only were positive for ASIC-3 in $39.3\%$ and $42.6\%$, respectively. Echography-guided intravesical administration of acetic acid resulted in colonic hypersensitivity to colorectal distension. This effect started 1 h post-injection and lasted up to 24 h, and was not longer seen after 3 days after injection. No colonic hyperpermeability and no difference in urinary bladder and colon MPO activity was observed between control and acetic acid-treated rats. Colonic sensitization by intravesical acetic acid administration was prevented by S1 intrathecal administration of APETx2. ### Conclusion We developed an acute pelvic cross-organ sensitization model in conscious rat. In this model, cross-organ sensitization is likely to involve S1-L6 extrinsic primary afferents co-innervating the colon and urinary bladder through an ASIC-3 pathway. ## Introduction Experimental and clinical studies on irritable bowel syndrome (IBS) and bladder pain syndrome (BPS) highlight the close relationship between digestive symptoms and urinary symptoms. Indeed, clinical studies point out that a high prevalence of urinary symptoms are found in patients with IBS, including urinary bladder syndrome or overactive urinary bladder. Similarly, among patients with BPS, $52\%$ share IBS criteria, while correspondingly, $40\%$–$60\%$ of IBS patients also fulfill diagnostic criteria for BPS (1–3). The main hypothesis to explain this association relies on alteration of visceral and/or somatic perception. This is supported by the fact that urinary bladder and colon may share common afferent nerves, and the information is conveyed through the same central anatomical structures. Recently, the phenomenon of cross sensitization has been acknowledged and is based on the convergence of neuronal afferent fibers, both at the level of dorsal root ganglia (DRG) and spinal/supraspinal structures [4]. Studies in rats have confirmed that acute urinary bladder sensitization decreases the thresholds of colonic sensitivity associated with colorectal distension (CRD) and, conversely, acute colonic sensitization induces an increase in the frequency of urinary bladder contractions [5]. It was also shown after acute colonic sensitization an increase in sodium channel activity in the C fibers of the pelvic nerve innervating the urinary bladder. However, these same C fibers are blocked by capsaicin injected in the urinary bladder, resulting in inhibition of cross sensitization [6]. These results testify to the direct role of afferent neurons in this mechanism. However, these models were using inflammatory insults, while IBS and BPS are known to be unrelated to visceral inflammatory processes. The target organs of primary afferent endings produced by DRG neurons has been identified in animals by retrograde labelling studies [7, 8]. It was shown that the neuronal bodies of primary afferents targeting the colorectum or the urinary bladder are predominantly present in lumbar and sacral DRGs [9]. In addition, it has been shown that some DRG neurons target both organs, thus transmitting signals arising from both the colorectum and the urinary bladder [10]. For instance, $17\%$ of the neurons in the lumbosacral DRG innervate both the colon and urinary bladder in the mouse [11]. It has been shown that acid receptors such as acid sensing ion channels (ASICs) and transient receptor potential channels (TRPs) are expressed by primary afferent neurons innervating the colon and urinary bladder (12–14). Finally, in acute models of urinary bladder or bowel hypersensitivity, neuronal hyperexcitability has been observed in DRG neurons in response to a nociceptive stimulus [15, 16]. To better characterize mechanisms involved in acute cross-organ sensitization, we aimed to develop a non-inflammatory rat model of colorectal hyperalgesia induced by acidic stimulation of the urinary bladder. Considering the fact that subpopulations of primary afferent neurons have been shown to express acid-sensing ion channels, we investigated whether those co-innervating the colorectum and the urinary bladder do express ASIC-3 channels, and whether ASIC-3 channels may be involved in acid induced acute cross-organ sensitization. ## Animals Male Sprague-Dawley rats (250–350 g, 40–52 days old; Janvier, Le Genest-St-Isle, France) were housed in a temperature-controlled environment (22°C) with a 12-h light/dark cycle. The rats had free access to standard rat chow (RM1 diet; SDS, Witham, Essex, UK) and drinking water. The protocol was approved by the local Committee on the Ethics of Animal Experiments (Ethical agreement Number: N/02-01-$\frac{13}{02}$/01-16) and these experiments were adhered to the IASP and SFN guidelines for research using animal subjects. ## Double retro-labelling Surgery was performed in anaesthetized rats using sodium ketamine (100 mg/kg) and xylazine (Rompun $2\%$; 10 mg/kg), given intraperitoneally (i.p.). After laparotomy, Fluorogold (FG, Hydroxystilbamidine, methanesulfonate, $4\%$, 1 µl, 10 injections; Interchim, Montluçon, France) and Fluororuby (FR, Dextran, Tetramethylrhodamine, 10000 MW, Lysine Fixable, $10\%$, 1 µl, 10 injections; Interchim, Montluçon, France) [17] were injected in the all part of the urinary bladder wall and in the wall of the colorectum segment respectively. Seven days after these injections, the L6 and S1 DRGs were extracted after intracardiac perfusion using Tyrode solution (NaCl 6.8 g; KCl 0.4 g; MgCl2 × 6 H2O 0.15 g; MgSo4 × 7H2O 0.1 g; NaH2PO4 2H2O 0.19 g; Glucose 1 g; NaHCO3 2.2 g; Fill up to 1l with H2O), followed by a fixation solution composed of $4\%$ paraformaldehyde (Sigma) and $0.3\%$ picric acid (Sigma) in 300 ml of 0.1 M phosphate buffer. After overnight incubation in fixation solution, DRGs were further incubated in sucrose ($10\%$, $20\%$ and $30\%$) at 4°C during 24 h each one. This was followed by inclusion in TissueTek®, freezing in isopentan at −40°C, and cryostat sectioning after 24 h at −80°C (LEICACM1950, Ruel-Malmaison, France; 20 µm). A fluorescent microscope was used for identification of colorectum-only, urinary bladder-only and colorectum-urinary bladder co-innervating DRG neurons. ## ASIC3 immunofluorescence The expression of ASIC3 in retrogradely labelled DRG neurons was assessed by immunofluorescence. Tissue sections were first incubated with an anti-ASIC3 antibody from the rabbit (from Alomone, diluted at 1:300) overnight at 4°C. This was followed by 1 h incubation with a goat anti-rabbit secondary antibody conjugated with Alexa-488 (from Invitrogen, diluted at 1:300), three washes in PBS and coversliped using glycerol/DABCO mounting media. Pictures were taken with a Leica photomicroscope at x10 magnification. ## Acetic acid cross-sensitization In anesthetized rats (Isoflurane: $3\%$ in 1.5 L/min of air), an intra-urinary bladder acetic acid instillation ($0.75\%$, 500–700 µl) was performed under ultrasound monitoring. Control rats were injected with saline solution (NaCl $0.9\%$). The use of the ultrasound system allowed us to confirm the injections in the urinary bladder of the rats by a visual control (Supplementary Material). The rat woke up immediately after brief anesthesia and was left undisturbed for at least 1 h before any experiment. ## Colorectal distension A spherical infinitely compliant distension balloon (diameter: 2 cm) was made using a polyethylene bag attached to a 5 mm diameter polyethylene catheter (Dutscher, Brumath, France) drilled in its extremity. The balloon was inserted in the colorectum of anaesthetized rats, secured to the tail with tape and connected to an electronic barostat (G&J Electronics Inc, Toronto, Canada) to perform isobaric graded colorectal distensions (CRD). ## Visceral sensitivity measurement Colorectal sensitivity was measured in awake rats 60 min, 1 day, 3 days and 7 days following acetic acid administration. Visceral pain was assessed by continuous monitoring of pressure changes resulting from abdominal wall contractions induced by CRD. A miniaturized pressure transducer catheter (SPR-524 Mikro-Tip catheter; Millar Instruments, Houston, TX) with a polyethylene balloon lubricated with medical grade lubricant was introduced into the colon such that the middle of the pressure sensor (3.5 F) was 2 cm proximal to the anus. The catheter was then secured to the tail with tape, and colonic contractions were recorded in conscious rats immediately after their placement in the restraint tube. Changes in intracolonic pressure (ICP), reflecting viscero-motor responses, were used as a surrogate marker of colorectal sensitivity [18]. Variation of intracolonic pressures was quantified during graded CRD at 20, 40 and 60 mmHg (Figure 1). Each distension pressure was applied twice for 20s at 4-min intervals. Figure 1: Kinetics of the effect of an acetic acid injection on colonic sensitivity. Colonic sensitivity assessment by CRD, 30 min (A), 1 h (B), 3 days (C) and 7 days (D) after acetic acid injection at 0.75% (n = 6–9 per group; p < 0.05, **p < 0.001, Two-way ANOVA followed by Bonferroni post-hoc test). ## Measurement of urinary bladder and colonic paracellular permeabilities Urinary bladder and distal colon samples were removed and cut along the mesenteric border. Urinary bladder and colonic permeabilities were assessed by measuring FITC-dextran (4 kDa) fluxes in Ussing chambers with an exchange surface of 0.07 cm2 (Harvard Apparatus, Holliston, MA). FITC-dextran (5 mg/ml) was placed in the mucosal side. After 3 h at 37°C, medium from the serosal side was removed and stored at −80°C. The fluorescence level of FITC-dextran (excitation at 485 nm, emission at 535 nm) was measured in 96-well black plate with spectrometer Chameleon V (Hidex Co, Turku, Finland). Values were converted to concentration (mg/ml) using a standard curve [19]. ## Tissue myeloperoxidase assay Myeloperoxidase (MPO) was measured in colonic and in urinary bladder tissues. A piece of colon or of urinary bladder (around 50 mg) was thoroughly washed in PBS and homogenized (50 mg/ml) in $0.5\%$ hexadecyltrimethylammonium bromide (Sigma) in 50 mmol/L PBS, (pH 6.0), freeze-thawed 3 times, sonicated and centrifuged. The MPO was assayed in the supernatant by adding 1 mg/ml of dianisidine dihydrochloride (Sigma) and 5 × $10\%$–5 × $4\%$ H2O2 and the change in optical density measured at 450 nm. Human neutrophil MPO (Sigma) was used as standard. One unit of MPO activity was defined as the amount that degraded 1.0 µmol of peroxide/min at 25°C [20]. ## FR, FG, FR and FR DRG neuron and ASIC-3-expressing DRG neuron quantifications The quantifications of FR neurons, FG neurons, FR and FG neurons and ASIC-3 positive neurons in L6 and S1 DRGs were done on 4–5 sections per DRG using Fiji-ImageJ software ($$n = 5$$–8 rats). The percentage of convergent neurons was calculated from the total sum of single FR and FG labelled cells (taken together as $100\%$). ## Intrathecal injection of an ASIC3 blocker APETx2, an ASIC3 blocker (2.2 µM, 25 µl) [21] or saline solution were administered by acute intrathecal injection (using a 25G needle and an 50 µl Hamilton syringe) between L6 and S1, 1 min before acetic acid injection at $0.75\%$ in the urinary bladder ($$n = 6$$ per group). Colorectal sensitivity was assessed, 60 min after acetic acid injection, as described before. ## Statistical analysis All data were expressed as mean ± SE. Statistical analyses were performed with GraphPad Prism software. For the CRD analysis in model validation, a two-way (Volume and Treatment) ANOVA followed by Bonferroni post-hoc test for multiple comparisons were used. For the assessment of ASIC-3 positive cells in L6 and S1 DRGs, a Kruskal-Wallis test followed by a Dunn post-hoc test for multiple comparisons were used. A p value less than 0.05 was considered statistically significant. ## Development of a cross-organ sensitization model using intravesical injection of acetic acid Intravesical injection of acetic acid at $0.75\%$ under ultrasound monitoring did not induce colonic hypersensitivity within 30 min following the administration since no difference in intraluminal pressure variation in response to colorectal distensions of 20, 40 and 60 mmHg was observed between control and treated animals (Figure 1A). In contrast, 1 h after injection, an increase of the colonic nociceptive response during colorectal distension occurred at 60 mmHg ($p \leq 0.001$; Figure 1B). This hypersensitivity lasted up to 3 days after intravesical injection of acetic acid (Figure 1C). By contrast, 7 days after the injection, the colonic nociceptive response during CRD in treated animals by intravesical acetic acid returned to values comparable to those of control animals (Figure 1D). ## Intestinal permeability Fluorescence quantification on the serosal side of the Ussing chamber showed no significant difference in the passage of FITC-Dextran from the mucous to the serosal side of the colorectal wall of the group receiving intravesical acetic acid compared to the control group (Figure 2A). Figure 2: Intestinal permeability and MPO activity measurements in cross-hypersensitization model induced by acetic acid (0.75%). One hour after acetic acid intra-urinary bladder injection (0.75%) under ultrasound monitoring, the intestinal permeability was assessed by the Ussing chamber technique using FITC-Dextran as a marker of the permeability (A). Urinary bladder (B) and colonic (C) myeloperoxidase activity were measured in the tissues 1 h after acetic acid injection in the urinary bladder (n = 5 per group; Mann–Whitney test). ## Myeloperoxidase activity No difference in MPO activity was observed between control and acetic acid-treated rats, both in urinary bladder and colon samples (Figures 2B,C). ## Identification and ASIC-3 characterization of extrinsic primary afferent neurons co-innervating the urinary bladder and colorectum by double retrograde labelling Injection of FG into the wall of the urinary bladder and FR into the wall of the colorectum in the rat showed that there was an average $9.7\%$ of retrogradely traced neurons (FG only, FG only and FG + FR neurons; Figures 3A–C) within the L6 and S1 DRGs that co-innervating the urinary bladder and colon (Figure 3D). $13.8\%$ innervating the colorectum only and $76.5\%$ innervating the urinary bladder only (Figure 3D). Figure 3: Percentage of urinary bladder neurons-only, colorectal neurons-only and dually-projecting urinary bladder and colonic neurons in L6 and S1 DRGs. Percentage was determined by double retrograde labelling, using fluororuby (FR; A) in red for the colon, fluorogold (FG; B) in blue for the urinary bladder and fluororuby with fluorogold (FR + FG; C) in purple for the colon and the urinary bladder. The arrows show the primary afferent neurons co-innervating the colon and the urinary bladder (scale bar = 100 µm). Quantification was done on six L6 and S1 DRG sections (n = 6–8; D). Retrograde labeling also allowed for the identification of $46.2\%$, $59.8\%$ and $76.1\%$ colorectal (Figure 4A), urinary bladder (Figure 4B) and dually-projecting DRG neurons (Figure 4D), respectively, expressing ASIC-3 ($p \leq 0.0001$ vs. colon only and $p \leq 0.05$ vs. urinary bladder only; Figures 4C–E). Figure 4: ASIC3 expression in dually-projecting urinary bladder/colorectal L6 and S1 DRG neurons. Colorectal primary afferent neurons expressing FR (red, A,D) and urinary bladder primary afferent neurons expressing FG (blue, B,D). ASIC3 positive neurons in green (FITC, C,D). Triple labelling is observed in D, by overlapping of the three fluorochromes (scale bar = 50 µm). The arrow shows a primary afferent neuron co-innervating the colon and the urinary bladder expressing ASIC3. Average of the percentage cells innervating the urinary bladder, the colon and co-innervating the urinary bladder and the colon, expressing ASIC3 in L6 and S1 DRGs (E). Percentage of urinary bladder primary afferent-only neurons expressing ASIC3 [FG(+)], colorectal primary afferent-only neurons expressing ASIC3 [FR(+)], or dually-projecting urinary bladder/colonic primary afferent neurons expressing ASIC3 [FG(+)/FR(+)]; (n = 5; p < 0.05 et ***p < 0.0001 [compare to FR(+)]; #p < 0.05 [compare to FG(+)]; Kruskal-Wallis test followed by Dunn post-hoc test). ## Effect of the ASIC-3 blocker on cross sensitization Blocking of ASIC-3 channels by intrathecal injection of APETx2 (2.2 μM) reduced the viscero-motor responses to CRD (Figure 5). Indeed, a decrease in colonic hypersensitivity assessed 60 min after injection of $0.75\%$ acetic acid in the urinary bladder and intrathecal administration of APETx2 was observed for distensions of 40 mmHg ($p \leq 0.05$) and 60 mmHg ($p \leq 0.01$; Figure 5) compared to control animals. Figure 5: Effect of an ASIC3 blocker by intrathecal injection on cross-organ sensitization. Spinal inhibition of ASIC3 by intrathecal injection of APETx2 decreased the colonic hypersensitivity, assessed 60 min after acetic acid injection in the bladder (n = 6/group; p < 0.05, *p < 0.01). ## Discussion In the present study, we successfully developed a non-inflammatory rat model of urinary bladder/colon cross-organ sensitization. Indeed, ultrasonography-guided administration of acetic acid into the urinary bladder of rat resulted in increased nociceptive responses to colorectal distension that lasted up to 3 days. Moreover, we showed in rats that ASIC-3 expressing primary extrinsic afferent neurons were more likely to co-innervate urinary bladder and colon, and subsequently showed that blocking ASIC-3 prevented acetic acid-induced cross-organ sensitization. To our knowledge, this is the first study showing that ASIC-3 channels may be involved in urinary bladder/colon cross-organ sensitization. Traditionally, intravesical administration of intraluminal irritative agents is performed through laparotomy [6, 22, 23]. In addition, measurement of colonic pain is based on the quantification of muscle contractions of the abdominal wall in response to CRD, a method also named pseudo-affective reflex [24], after surgical insertion of electromyography recording electrodes. However, and as we have previously shown, stress as well as surgery could by themselves promote visceral hypersensitivity [25]. Here we aimed at developing an acute, non-inflammatory model of urinary bladder/colon cross-organ sensitization using mini-invasive techniques. In our model, intravesical administration of acetic acid was performed through percutaneous injection under brief anesthesia using ultrasound guidance. In addition, measurement of intracolonic pressure was performed as a reflection of intra-abdominal pressure, and therefore abdominal contractions to measure colonic viscero-sensitivity [25]. This technique does not require surgery and has therefore the advantage to be minimally invasive and to prevent surgery-induced visceral hyperalgesia [25]. By employing these approaches, we were able to record the transient colonic hypersensitivity (no longer than 3 days in duration) resulting from the instillation of acetic acid. Furthermore, we observed no significant difference in colonic permeability through Ussing chambers. This therefore translates that the colonic permeability was not impaired after an intravesical injection of acetic acid at $0.75\%$. Similarly, no significant difference of MPO activity in the colon and urinary bladder was observed in our model. This suggested that the observed cross-organ sensitization model was neither related to inflammatory processes nor to colonic hyperpermeability. We also show in this study that an average of $9.7\%$ of urinary bladder and colon primary afferent neurons in the L6 and S1 DRG co-innervate the urinary bladder and colon in Rat. These values agree with previous descriptions by Christianson and collaborators [2006] [26], also in Rat. In another study higher proportions of neurons co-innervating the urinary bladder and colon were shown [27]. Indeed, these authors have shown that 14.0 ± $2.5\%$ of neurons in L6 DRG and 14.3 ± $1.9\%$ of neurons in the S1 DRG innervate both the colon and the urinary bladder in rats. However, the animals used in the later study were female rats [27]. Whether there is gender difference in the proportion in neurons co-innervating the urinary bladder and the colon remains to be confirmed, although this may explain to some extent the higher prevalence of IBS and BPS in women. To the best of our knowledge, this may be the first study addressing the neurochemical phenotype of dually projecting colorectum-urinary bladder DRG neurons. Here we show for the first time that neurons co-innervating the urinary bladder and the colon express ASIC-3 protein, and that the proportion of such neurons expressing this channel was larger than what could be observed in urinary bladder-only or colorectum-only DRG neurons. ASIC-3 has been shown to participate in mechanisms of visceral hypersensitivity. Thus, ASIC-3 appears to be involved in the induction of chronic colorectal hypersensitivity due to intracolonic zymosan in mouse, by sensitization of mechanoreceptors in the absence of inflammation [28]. It has also been proposed that ASIC-3 contributes to the development of functional hypersensitivity, observed in patients with IBS [29]. In another study, an increase in ASIC2a and ASIC3 mRNAs in the urinary bladder of BPS patients was observed, suggesting involvement of these channels in increased pain and hyperalgesia [30]. In our present study, we also show that ASIC-3 seems to participate in mechanisms of urinary bladder-colorectal cross-organ sensitization. Rats given intrathecal APETx2, show that this ASIC-3 blocker could potentially prevent the occurrence of colonic hypersensitivity induced by vesical insult. However, there are some limitations of APETx2. In fact, we cannot exclude an effect of this blocker on the voltage-gated sodium (NaV) channel, NaV1.8 [31]. On the other hand, multiple studies have showed that intrathecal injection diffuses in DRGs by using dye [32] or isotope [33]. In addition, Marger et al. observed that intrathecal injection is much better than an intra-organ injection or an intraperitoneal injection [34]. By injecting APETx2 intrathecally it was not intended to target central nervous system (CNS) cells, since ASIC-3 are not expressed in the CNS [35, 36]. The aim was rather to target DRG cells as did other groups [37]. Furthermore, it was showed in a genetic model of ASIC3 knockout in mice an increase in visceral nociceptive mechanical threshold [38, 39]. These data suggest that ASIC3 may also play a role in colorectal mechanosensation under non-pathophysiological conditions. Interestingly, Jones et al. have also demonstrated that amiloride, an ASIC non-specific blocker applied directly on mucosal fields of a specific class of stretch-sensitive colonic afferents (muscular-mucosal afferents) in ex-vivo preparations obtained from control mice, did not affect mechanosensitivity [39]. Nevertheless, these data were obtained from an ex-vivo model and amiloride do not have the same pharmacological action as APETx2 [40]. In contrast, in a non-visceral study, it was demonstrated that APETx2 maintains the ASIC3 current amplitude unaltered [41]. In our study, we believe that APETx2 potentially acts as a preventive molecule of cross-organ sensitization through indirect inhibition of AIC3 upregulation in DRGs as it was observed in several studies [42, 43]. Nevertheless, this statement remained to be demonstrated in our model. In conclusion, we present here an alternative, minimally invasive model of urinary bladder insult that allows for instillation of different types of molecules without exposing the animal to excessive stress derived from surgical interventions. We also show that dually projecting colorectal and urinary bladder DRG neurons express ASIC-3, and this acid sensing channel may be relevant in the mechanisms of cross-organ sensitization between pelvic organs; this could represent a potential interesting therapeutic target for the future treatment of IBS/BPS syndromes which are characterized by the presence of chronic pain in most patients. However, as far as we know in the literature, there is no chronic non-inflammatory urinary bladder-colon cross sensitization model. Taken this fact into consideration, acute model is not a perfect model but remains the best one. Acute cross-organ sensitization in the context of IBS/BPS can also be seen as a possible trigger to enter both diseases (rather than a mechanism of sustained pain in the long run): indeed, $10\%$ of IBS and/or BPS have inflammatory/chemical insult triggering their symptoms, including gastroenteritis, cystitis, IBD, and even stress that can impair the intestinal/urothelial permeability [44]. Nevertheless, these results will require further confirmation and expansion, including if, for example, dually-projecting DRG neurons also express vanilloid receptors 1 (TRPV1). In an indirect fashion, it has been shown that the instillation of 4,6-trinitrobenzenesulphonic acid (TNBS) into the colon results in an increase of TRPV1 in S1 DRG neurons, and that urinary bladder nociceptive responses could be enhanced in part by TRPV1 activation in the urinary bladder [27]. This and other molecules remain to be studied in non-inflammatory models of cross-organ sensitization. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. ## Ethics statement The animal study was reviewed and approved by Local Committee on the Ethics of Animal Experiments (Ethical agreement Number: N/02-01-$\frac{13}{02}$/01-16). ## Author contributions GG and A-ML designed the research study; KA, MM and LL performed the research; KA, MM, LL, and GG analysed the data; KA, and GG drafted the manuscript; KA, MM, LL, PB, MC, A-ML and GG critically reviewed the manuscript for important intellectual content; All authors approved the final version of the article, including the authorship list; GG accepts official responsibility for the overall integrity of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpain.2023.1083514/full#supplementary-material. ## References 1. Alagiri M, Chottiner S, Ratner V, Slade D, Hanno PM. Interstitial cystitis: unexplained associations with other chronic disease and pain syndromes. 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--- title: Machine learning algorithms assisted identification of post-stroke depression associated biological features authors: - Xintong Zhang - Xiangyu Wang - Shuwei Wang - Yingjie Zhang - Zeyu Wang - Qingyan Yang - Song Wang - Risheng Cao - Binbin Yu - Yu Zheng - Yini Dang journal: Frontiers in Neuroscience year: 2023 pmcid: PMC10030717 doi: 10.3389/fnins.2023.1146620 license: CC BY 4.0 --- # Machine learning algorithms assisted identification of post-stroke depression associated biological features ## Abstract ### Objectives Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD. ### Methods Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed. ### Results Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD ($r = 0.653$, $P \leq 0.001$) and FERM3 ($r = 0.728$, $P \leq 0.001$) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score. ### Conclusion A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings. ## Introduction Stroke remains the second leading cause of death and may lead to long-term disability in adults (GBD 2019 Stroke Collaborators, 2021; Sun et al., 2021). After the acute stage, most of stroke patients suffer from physical and mental disabilities of varying degrees, including hemiplegia, reduced energy, and disturbed sleep (Zhang et al., 2013; Dong et al., 2021). Previous studies have shown that about 30–$40\%$ of stroke patients develop post-stroke depression (PSD) which is a mood disorder characterized by depression and anhedonia, and is associated with decreased rehabilitation motivation, reduced quality of life, poor functional outcome, as well as increased cost of treatment and burden of family caregiver (Li et al., 2020). One meta-analysis concluded that a hazard ratio for post-stroke depression and all-cause mortality was 1.59 (Cai et al., 2019). However, PSD is often concealed due to unrecognized depressive symptoms and their decreased willingness of treatment attendance (Klinedinst et al., 2012). Diagnosis of PSD is currently based on clinical symptoms, and there is no reliable objective parameter. Therefore, it is necessary to explore the new diagnostic and therapeutic biomarkers for PSD in subacute period of stroke. There is accumulating evidence that PSD and metabolism are intimately related. Compared with non-PSD, stroke patients with PSD have higher glutamate levels in the frontal lobe (Wang et al., 2012). Previous studies found that a high level of homocysteine has been identified as the qualifiable risk factor for ischemic stroke, and elevated serum level of homocysteine is also significantly associated with depression (Li et al., 2017; Zhou et al., 2018). Jiang et al. [ 2021] demonstrated that gut microbiome may participate in the development of PSD, the discriminating fecal metabolites were mainly involved in lipid metabolism, amino acid metabolism, carbohydrate metabolism and nucleotide metabolism. These results indicated that metabolism plays an important role in the pathological process of PSD. Recently with the assistant of advanced sequencing technologies and machine learning algorithms, intelligent hub gene and signaling pathway detection becomes realistic. Several studies based on weighted gene co-expression network analysis (WGCNA) have reported changes in relevant key pathways and differential expression of key related genes in post-stroke patients (Li et al., 2020; Wang et al., 2020; Lin et al., 2021). Furthermore, Liu et al. [ 2022] used WGCNA combined with the random forest model and the least absolute shrinkage and selection operator (LASSO) analysis to identify 10 key genes in patients with Alzheimer’s Disease. However, these techniques have not been widely applied in the investigation of metabolism biomarkers of PSD. Upon the above concerns, this study employed multiple bioinformatic approaches to find possible biomarkers. Firstly, three gene expression profiles of stroke were obtained from GEO database. Differentially expressed genes (DEGs) were detected. WGCNA was constructed to identify disease related module genes. Then, correlation analysis was performed to obtain metabolism related genes. Interaction analysis was performed to obtain candidate hub genes. Subsequently, signature genes were identified by LASSO and random forest analysis. Gene set enrichment analysis was applied on signature genes. Finally, a diagnosis model was built in PSD cohort. In general, the findings of this research may assist in the diagnosis and treatment of PSD as well as increase our understanding of etiology of PSD. ## Data sources and processing Three datasets (GSE140275, GSE122709, GSE180470) were downloaded from Gene Expression Omnibus (GEO).1 The GSE140275 dataset contained six patients, including three healthy controls (HC) and three stroke patients. The GSE122709 dataset included five HC and ten stroke patients. GSE180470 dataset included three HC and three stroke patients. Expressions of three datasets were all derived from human blood tissue. “ Limma” and “edgeR” package in R software was used to investigate differentially expressed genes (DEGs) (Robinson et al., 2010; Ritchie et al., 2015), which was specified as “P-value < 0.05 and log2 (fold change) > 1 or log2 (fold change) < –1.” For visualization, the volcano plots were generated to show DEGs, while the top 25 upregulated and the top 25 downregulated DEGs were displayed by heatmaps. ## Functional enrichment analysis Functional enrichment analysis was conducted to evaluate major biological attributes of DEGs, specifically including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using “ClusterProfiler” package in R software. Threshold was set at P-value < 0.05. GO categories comprised biological processes (BP), molecular functions (MF), and cellular components (CC) (Zhu et al., 2022). ## Weighted gene co-expression network analysis (WGCNA) Based on scale-free topology criterion, co-expression network in GSE122709 dataset was constructed using “WGCNA” package in R software to identify co-expression gene modules (Langfelder and Horvath, 2008). Briefly, genes with read counts less than 10 and “NA” were filtered out, top 5,000 variant genes were selected. Pearson’s correlations between each gene pair were calculated to build an adjacency matrix. Afterward, a “soft” threshold power (β) was estimated according to the criteria of scale-free topology to construct a biologically important scale-free network. Dynamic Tree Cut algorithm was then used to identify gene modules (Lin et al., 2021). Module membership (MM) and gene significance (GS) were estimated to connect modules with clinical characteristics. Hub gene modules were designated as those with the highest Pearson module membership correlation and P-value < 0.05 (Liu et al., 2021). ## Screening for candidate hub genes Based on R software, “WGCNA” package was used for correlation analysis for genes in GSE122709 and seven genes associated with metabolism with the following parameters: \| R\| > 0.5, $P \leq 0.001.$ Then, we identified candidate hub genes by the intersection of DEGs, key module genes and metabolism related genes. Finally, results were visualized by Venn diagram via online tool Venny 2.1.02 (He et al., 2021). ## Identification for signature genes in patients with stroke We screened candidate hub genes by the intersection of DEGs, key module genes and metabolism related genes. Subsequently, two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify hub gene. LASSO, a dimension reduction approach, shows superiority in evaluating high-dimensional data in comparison to regression analysis (Kang et al., 2021). The “glmnet” package was used to implement LASSO analysis with a turning/penalty parameter utilizing a 10-fold cross-validation. Furthermore, the “random forest” package was used for performing the random forest analysis which determined the optimal number of variables by computing average error rate of candidate hub genes (Mantero and Ishwaran, 2021). A random forest tree model was built and the importance scores of each candidate hub genes were identified. Genes with importance value >0.25 were determined. The intersection genes of LASSO and random forest analysis were used to pick signature genes of patients with stroke. ## Establishment of nomogram The “rms” package was applied for incorporating signature genes to establish a nomogram. The “score” is the score of the relevant item below, and the “total score” is the sum of all the elements above. Calibration curves were used for assessing the predictive power of the model. Clinical usefulness of nomogram was evaluated by decision curve analysis, which determines clinical practicability of nomogram by quantifying the net benefits under different threshold probabilities in the validation set. Furthermore, we performed clinical impact curves to evaluate clinical utility of the model (Xu et al., 2021). ## Curve analysis of receiver operating characteristics (ROC) The “pROC” package was applied to create Receiver Operating Characteristic (ROC) curves to determine the area under the curve (AUC) for screening signature genes and evaluating their diagnostic value (Robin et al., 2011). AUCs of 0.5–0.7 were considered with low diagnostic accuracy, 0.7–0.9 were considered with moderate accuracy, and >0.9 indicates high accuracy. ## Gene set enrichment analysis (GSEA) To functionally investigate the biological significance of signature genes, GSEA (version 4.1.0) was performed in different subgroups. KEGG gene sets were chosen as the gene set database (Subramanian et al., 2005). Normalized enrichment score (NES) and false discovery rate (FDR) were used to determine if differences were statistically significant and cut-off values were FDR < 0.25, $P \leq 0.05$, and \| NES\| > 1. ## PSD validation cohort This was a cohort study enrolled at the First Affiliated Hospital of Nanjing Medical University from September 2020 to April 2022. It was approved by the Committee of Institutional Ethics (Institutional Review Board, 2018-SR-339) and all participants provided written informed consent prior to participation. Patients eligible for inclusion in the study were: [1] aged older than 18 years; [2] diagnosed with ischemic stroke on brain MRI; [3] with stable vital signs (Luft et al., 2004; Upreti et al., 2019). Patients were excluded if [1] presence of severe cognitive impairment; [2] participated in other clinical trials within 6 months (Shi et al., 2021; Yu et al., 2022). All participants underwent an initial clinical assessment, including the collection of clinical and demographic information. Depression symptoms in post-stroke patients were evaluated by the Hamilton Depression Rating Scale 17-item (HAMD) at 1 month after stroke by a trained neurologist (Lin et al., 2020; Qiao et al., 2020). A score of 0–7 was considered normal, while a HAMD score ≥8 is indicative of depression. Stroke severity was measured using the National Institute of Health Stroke Scale (NIHSS) (He et al., 2020). Modified Rankin Scale (mRS) was used to estimate the functional disability (Liu et al., 2018). Independence and level of activities of daily life (ADL) were evaluated with the Barthel index (Kamal et al., 2020). For research purposes, a blood sample (10 ml) was taken from each subject for further ELISA assessment when they completed the HAMD assessment. ## ELISA analysis Concentration of signature genes in serum of stroke patients were measured using ELISA kit (antibodies-online, Philadelphia, PA, USA). Briefly, 100 μL standard or sample were added to each well and incubated for 90 min at 37°C. After washing two times, 100 μL Biotin-labeled antibody working solution was added and incubated for 60 min for 37°C. After plates were washed three times. A total of 100 μL SABC Working Solution was added and incubated for 30 min at 37°C. Subsequently, 90 μL TMB Substrate Solution was added and incubated 20 min at 37°C. After the incubation, 50 μL stop solution was added into each well to stop the reaction. Finally, Absorbance value at 450 nm was read immediately and calculation (Kaida et al., 2013; Zhou et al., 2015). ## Statistical analysis All statistical analyses in our study were implemented using R software (version 4.1.2). The difference between the two groups was analyzed by Student’s t-test. The correlation between genes in GSE122709 and metabolism related genes was determined using Pearson’s correlation test. All statistical P-values were two-sided, and statistical significance was considered with P-value < 0.05. ## Results Detailed procedure of our study is shown in Figure 1. FIGURE 1: Flow chart. GEO, Gene Expression Omnibus; WGCNA, weighted gene co-expression network analysis; PSD, post-stroke depression; ROC, receiver operating characteristic; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis. ## Identification of DEGs between HC and stroke patients To identify potential DEGs, expression profiles of GSE140275 and GSE122709 in GEO database were performed using “Limma” package with $P \leq 0.05$ and \| logFC\| > 1 as threshold. A total of 1,724 DEGs were screened in GSE140275 including 861 upregulated genes and 863 downregulated genes (Supplementary Table 1). A total of 7,731 DEGs were obtained, of which 3,516 genes presented upregulation and 4,215 genes presented downregulation in GSE122709 (Supplementary Table 2). The volcano plots were demonstrated in Figures 2A, E. The heatmap showed the top 25 upregulated and top 25 downregulated DEGs between healthy control and stroke patients (Figures 2B, F). FIGURE 2: DEGs screening and functional enrichment analysis. (A) Volcano plot of differentially expressed genes in GSE140275. (B) Heatmap of differentially expressed genes in GSE140275. (C) KEGG pathway analyses of upregulated mRNAs in GSE140275. (D) GO functional analyses of upregulated mRNAs in GSE140275. (E) Volcano plot of differentially expressed genes in GSE122709. (F) Heatmap of differentially expressed genes in GSE122709. (G) KEGG pathway analyses of downregulated mRNAs in GSE140275. (H) GO functional analyses of downregulated mRNAs in GSE140275. (I) KEGG pathway analyses of mRNAs in GSE122709. (J) GO functional analyses of mRNAs in GSE122709. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. ## Functional enrichment analysis of DEGs in GSE140275 Functional enrichment analysis was carried out to investigate the biological functions of DEGs in GSE140275. Among upregulated DEGs, KEGG enrichment analysis demonstrated that “autophagy,” “porphyrin metabolism,” and “glycine, serine and threonine metabolism” were highly enriched (Figure 2C); GO analysis showed that multiple metabolic pathways were also significantly enriched in biological processes, such as “monoacylglycerol metabolic process,” “acylglycerol metabolic process,” and “glycerolipid metabolic process” (Figure 2D). The results of KEGG showed downregulated DEGs were especially enriched in “ribosome,” “protein export,” and “T cell receptor signaling pathway” (Figure 2G). Additional GO analysis suggested downregulated DEGs were significantly enriched in “structural constituent of ribosome” in MFs, “ribosome” in CCs, and “regulation of leukocyte mediated immunity” in BPs (Figure 2H). Similarly, KEGG pathways analysis of GSE122709 showed that “porphyrin metabolism”, and “glycine, serine and threonine metabolism” were significantly enriched (Figures 2I, J), indicating that metabolism played an important role in stroke. ## Construction of the weighted gene co-expression network The GSE122709 dataset (five HC and 10 stroke patients) was obtained for WGCNA analysis to identify modules of highly correlated genes. A scale-free co-expression network was constructed with the soft threshold to 20 and the mean connectivity is relatively favorable (Figures 3A, B). We selected 0.25 as clustering height limit to merge the strongly associated modules (Figure 3C). Subsequently, 24 signature modules were identified and labeled with different colors (Figure 3D). The correlation between modules was computed, and the results were showed in Figure 3E. In addition, transcription correlation analysis was performed and demonstrated that there was no substantive connection between modules (Figure 3F). Finally, we calculated the correlation between each module and clinical features. Results indicated that the MEroyalblue module was negatively correlated with HC (r = –0.83, $$P \leq 1$$e–04) and positively correlated with stroke ($r = 0.83$, $$P \leq 1$$e–04), while the Megrey module was negatively correlated with stroke (r = –0.93, $$P \leq 5$$e–07) and positively correlated with healthy control ($r = 0.93$, $$P \leq 5$$e–07) (Figure 3G and Supplementary Table 3). Therefore, Meroyalblue and Megrey modules were identified as clinically meaningful modules. FIGURE 3: Construction of WGCNA co–expression network. (A) Scale-free fit index. (B) Mean connectivity. (C) Clustered dendrograms. (D) Clustering dendrogram of genes, various colors represent different modules. (E) Correlation heatmap between modules. Red and blue represent positive and negative correlations, respectively. (F) Clustering dendrogram of module feature genes. (G) Heatmap of module–trait correlations. Red and green represent positive and negative correlations, respectively. HC, healthy control; WGCNA, weighted gene co-expression network analysis. ## Identification of metabolism related candidate hub genes Based on KEGG pathway analysis in GSE140275, we extracted porphyrin metabolism and glycine, serine and threonine metabolism related genes (ALAS2, FECH, COX10, GCAT, HMBS, PGAM2, and AOC2). Correlation analysis between seven genes and all genes in GSE122709 dataset was conducted. A total of 8,188 metabolism related genes were identified (\| r\| ≥ 0.5, P ≤ 0.001). The heatmap of correlation analysis were shown in Figure 4A. Subsequently, we interacted DEGs in GSE122709, genes in Meroyalblue and Megrey modules, and metabolism related genes, 554 common genes were obtained as metabolism related candidate hub genes (Figure 4B). Functional enrichment analysis revealed that metabolism related candidate hub genes were enriched in “oxidative phosphorylation,” “ATP synthesis coupled electron transport,” “cell-substrate junction,” and “carbohydrate transmembrane transporter activity” (Figures 4C, D). FIGURE 4: Generation of metabolism related candidate hub genes. (A) Correlation heatmap between seven metabolism related genes and DEGs in GSE122709. Red represents positive correlations, and blue represents negative correlations. (B) Venn diagram to identify candidate hub genes between metabolism related genes, key modules genes and DEGs. (C) KEGG analysis of candidate hub genes. (D) GO analysis of candidate hub genes. WGCNA, weighted gene co-expression network analysis; DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; BP, biological processes; CC, cellular components; MF, molecular functions. ## Selection of signature genes via machine learning algorithms Least absolute shrinkage and selection operator and random forest algorithms were applied to identify signature genes from 554 metabolism related candidate hub genes. For LASSO analysis, nine signature genes were selected from statistically significant univariate variables (Figures 5A, B and Supplementary Table 4). For random forest analysis, we set importance value to 0.25 as the threshold and 130 signature genes were determined (Figures 5C, D and Supplementary Table 5). The interaction analysis of LASSO and random forest indicated that two signature genes were finally screened out, including succinate dehydrogenase complex subunit D (SDHD) and fermitin family member 3 (FERMT3) (Figure 5E). Finally, correlation analysis of two signature genes and metabolism related genes (ALAS2, FECH, COX10, GCAT, HMBS, PGAM2, and AOC2) demonstrated that SDHD and FERMT3 were significantly correlated with metabolism (Figure 5F). FIGURE 5: Selection of signature genes. (A) Parameter selection was performed through LASSO regression. (B) Elucidation of LASSO coefficient profiles for selected factors. (C) Random forest error rate versus the number of classification trees. (D) The top 20 relatively important genes. (E) Venn diagram to identify signature genes between LASSO and random forest. (F) Heatmap of correlation analysis between two signature genes and metabolism related genes (ALAS2, FECH, COX10, GCAT, HMBS, PGAM2, and AOC2). LASSO, the least absolute shrinkage and selection operator. ## Validation of signature genes We further investigated the role of SDHD and FERMT3. The expression of SDHD and FERMT3 was verified in GSE140275 and GSE122709. The results showed that SDHD was substantially upregulated in the stroke group, while the same trend was seen in expression of FERMT3 (Figures 6A, B). To further confirm the reliability of our results, validation dataset (GSE180470) was used to validate expression of SDHD and FERMT3. SDHD and FERMT3 were highly expressed in the stroke group (Figure 6C), suggesting that these genes may play a significant role in stroke. FIGURE 6: Validation and GSEA analysis of signature genes. (A) Expression level of SDHD and FERMT3 in GSE122709. (B) Expression level of SDHD and FERMT3 in GSE140275. (C) Expression level of SDHD and FERMT3 in GSE180470. (D) GSEA analysis of SDHD. (E) GSEA analysis of FERMT3. P < 0.05; **P < 0.001. HC, healthy control; GSEA, gene set enrichment analysis. ## GSEA analysis of signature genes Gene set enrichment analysis was performed for evaluating signaling pathways involved in the signature genes. The results showed that SDHD was significantly correlated with “emotional lability,” “depression,” and “abnormal fear anxiety related behavior” (Figure 6D). Meanwhile, “depression,” “emotional blunting,” and “abnormal fear anxiety related behavior” were detected for FERMT3 (Figure 6E). The results indicated that SDHD and FERMT3 played a key role for diagnosis of psychosocial state in stroke patients. ## Diagnostic efficacy of signature genes in PSD patients Based on GSEA analysis of two signature genes (SDHD and FERMT3), we found that they have a significant correlation with depression. Therefore, we collected 81 stroke patients who were assigned into the PSD group (mean HAMD score = 14.74) and non-PSD group (mean HAMD score = 3.41). There was no difference in baseline clinical features between groups (Supplementary Table 6). Meanwhile, expression of serum SDHD and FERMT3 in all patients were detected by ELISA kit. SDHD and FERMT3 presented higher expression in the PSD group than the non-PSD group (Figures 7A, B), indicating their potential roles in diagnosis of depression in stroke patients. To predict diagnostic performance of signature genes in stroke patients with depression, the nomogram model for the signature genes (SDHD and FERMT3) was built using “rms” package (Figure 7C). Calibration curves demonstrated that the difference between the real and predicted depression risks was very minimal, indicating the nomogram model enabled an accurate estimation (Figure 7D). In addition, decision curves analysis demonstrated that the nomogram provided a greater clinical benefit (Figure 7E). The ROC curve also showed that the model was able to help clinicians accurately diagnose depression of stroke patients (Figure 7F). Additionally, correlation analysis between two signature genes and several clinical traits (HAMD, NIHSS, BI, and mRS) indicated that SDHD ($r = 0.653$, $P \leq 0.001$) and FERM3 ($r = 0.728$, $P \leq 0.001$) were positively related HAMD, while SDHD also displayed a negative association with Barthel index (r = –0.224, $$P \leq 0.044$$) (Figures 7G, H). FIGURE 7: Performance of signature genes in PSD patients. (A) Expression level of SDHD in PSD and non-PSD groups. (B) Expression level of FERMT3 in PSD and non-PSD groups. (C) Nomograms for the prediction of the PSD risk. (D) Scatter diagram of calibration plot for internal verification of the nomogram model. (F) DCA curves of the nomogram model. (E) ROC curves of the nomogram model. (G) Correlation between expression of SDHD and four clinical traits (HAMD, NIHSS, BI, and mRS). (H) Correlation between expression of FERMT3 and four clinical traits (HAMD, NIHSS, BI, and mRS). PSD, post-stroke depression; DCA, decision curve analysis; ROC, receiver operating characteristic; AUC, area under the curve; HAMD, Hamilton Depression Rating Scale 17-item; NIHSS, National Institute of Health Stroke Scale; BI, Barthel index; mRS, Modified Rankin Scale. P < 0.05.* ## Discussion In this study, we included three datasets (GSE140275, GSE122709, GSE180470) with 27 patients for data analysis. We first screened 1,724 DEGs in GSE140275 including 861 upregulated genes and 863 downregulated genes. Subsequent KEGG enrichment analysis showed “porphyrin metabolism” and “glycine, serine and threonine metabolism” were highly enriched. Recent research reveals that stroke causes systemic complications, including hyperlipemia, high blood viscosity, dysfunctional gut microbiota, and a leaky gut (Yamashiro et al., 2017; Chen et al., 2019a). Chen et al. ( 2019b) demonstrated that stroke would cause gut microbiota dysbiosis, translocation of gut microbiota, and disruption to the gut barrier. And supplementation of short chain fatty acids (SCFAs), especially butyric acid, could remodel the gut microbiota and treat stroke (Chen et al., 2019a). Moreover, with the development of biology, metabolomics was applied to explore biomarkers and mechanisms of stroke by identifying metabolic alterations. Several studies reported the increase in ketone bodies levels in rats with stroke compared with sham group (Chen et al., 2019c; Wang et al., 2019). Fu et al. [ 2019] reported a decrease in β-hydroxybutyric acid level in serum but an increase in brain tissue in stroke rats, providing more energy for brain. These studies suggest that metabolism features strongly correlate with prevention, diagnosis and treatment of stroke. Based on the role of metabolism in stroke, we extracted seven genes related to the “porphyrin metabolism” and “glycine, serine and threonine metabolism” pathways, including ALAS2, FECH, COX10, GCAT, HMBS, PGAM2, and AOC2. We then performed correlation analysis between these genes enriched in these two pathways in GSE140275 and all genes in GSE122709 to identify metabolism related genes. A total of 8,188 metabolism related genes were identified. Nevertheless, with the help of advanced bioinformatic approaches genetic information could be further derived. Weighted gene co-expression network analysis (WGCNA) is a frequently applied method to identify co-expression pattern at whole transcriptome level. Wang et al. [ 2019] performed WGCNA analysis to investigate co-expression modules related with osteosarcoma and found genes in brown module might be related with carcinogenesis of osteosarcoma. In addition, there were several studies screened key module genes related to stroke by WGCNA analysis (Fan et al., 2022; Zheng et al., 2022). However, metabolism related pathways and key genes in stroke are seldomly identified. Therefore, we performed WGCNA analysis of GSE122709 to identify 24 gene modules. No significant correlation between dividing modules was found. Module-traits relationship analysis indicated that Meroyalblue and Megrey modules were significantly associated with stroke disease. After this step, we interacted DEGs in GSE122709, genes in Meroyalblue and MEgrey modules, and metabolism related gene and showed 554 metabolism related candidate hub genes. Nonetheless, a single WGCNA analysis had significant limitations and inaccuracies (Tzimas et al., 2019). Currently, studies applied WGCNA were normally combined with multiple machine learning algorithms to identify biomarkers for disease prognosis and diagnosis. Zhao et al. [ 2022] identified four core genes (BTN3A2, CYFIP2, ST8SIA1, and TYMS) as biomarker for diagnosis of rheumatoid arthritis via comprehensive analysis of WGCNA, LASSO, random forest, and support vector machine analysis. By WGCNA, LASSO, and random forest algorithms, Fan et al. [ 2022] obtained five signature genes (UPP1, S100A9, KIF1B, S100A12, SLC26A8) and emerged remarkable diagnostic performance in pediatric septic shock. In the current study, LASSO regression analysis and random forest algorithms found two signature genes, then three validation datasets, including GSE140275, GSE122709, and GSE180470, confirmed that SDHD and FERMT3 were highly expressed in the stroke group. SDHD, one subunit of succinate dehydrogenase (SDH), dual roles in respiration by transferring electrons from succinate to ubiquinone in the mitochondrial electron transport chain (ETC) and catalyzing oxidation of succinate to fumarate in the mitochondrial Krebs cycle (Cecchini, 2003; Sun et al., 2005). Researchers reported mutations of SDHD in patients with hereditary pheochromocytomas and hereditary paragangliomas (Baysal et al., 2000). In vitro experiment performed by Bandara et al. [ 2021] demonstrated that mutation of SDHD via CRISPR/Cas9 approach could suppress glycolysis and overall ATP synthesis in HEK293. Overexpression of SDHD could significantly suppressed cell proliferation in vitro and tumor growth of HCC cells in vivo (Yuan et al., 2022). FERMT3 is a member of the kindlin family of binding proteins containing the FERM domain (Rognoni et al., 2016). FERMT3 mediates integrin activation and integrin-ligand binding. Therefore, FERMT3 is closely related to various biological activities, including cell adhesion, spreading, cell survival, proliferation and differentiation (Rognoni et al., 2016). Mutations of FERMT3 gene could cause leukocyte adhesion deficiency type III (LAD III) (Kuijpers et al., 2009). Liu et al. [ 2021] performed RNA sequencing in patients with triple-negative breast cancer and identified FERMT3 as protective gene in compound kushen injection treatment. Nonetheless, correlations of FERMT3 and SDHD with stroke have not been previously reported. Post-stroke depression (PSD), the most common psychiatric problem after stroke, is an independent risk factor of stroke mortality (D’Anci et al., 2019). PSD is closely associated with worse outcomes of physical and cognitive recovery, functioning, and health related quality of life (Villa et al., 2018). It is worth noting that PSD might halt or impede rehabilitation treatments. However, the complex pathophysiology of PSD is still only partly known till now. The current evidence indicates genetic factors as major aetiopathological predictors for PSD. Yang et al. [ 2010] reported that IL-18 level in serum on day 7 after admission might predict the risk of PSD. Plasma levels of glutamate and glutamate oxaloacetate transaminase at admission were also reported to be closely related PSD within 3 months (Cheng et al., 2014). To further probe the role of hub genes in stroke, we performed a GSEA analysis of signature genes. The results demonstrated that SDHD and FERMT3 were significantly enriched in depression. Then we validated our findings in stroke patients with and without depression. We found increase expression levels of SDHD and FERMT3 in stroke patients with depression, compatible with our previous research inferences. In addition, based on the two signature genes (SDHD and FERMT3) that we identified, we successfully established a PSD diagnosis for evaluating diagnosis value of SDHD and FERMT3 in our PSD cohort. Nomogram model showed great predictive ability and clinical usefulness. Meanwhile, AUC values of SDHD and FERMT3 were 0.896 and 0.964. Our results suggested that SDHD and FERMT3 might play essential roles in diagnosis of PSD. Finally, we performed correlation analysis of two signature genes and several clinical traits. We found that the SDHD and FERM3 were positively correlated with depression, which suggested that SDHD and FERMT3 had certain therapeutic predictive value in PSD. Moreover, SDHD was also found a negative correlation with activities of daily living in this study. Considering the feature of this parameters, it suggested that these two signature genes may also serve as biomarkers to monitor the mental functional prognosis in patients with PSD (van Hulsteijn et al., 2013). The present study also has certain shortcomings. Firstly, we collected data from public databases with small samples. There could have been a selection bias. Large datasets of stroke patients are limited, so we tried to minimize the bias of our results by validating signature genes across multiple datasets. Secondly, the metabolism related-pathways and -hub genes in stroke lack literature support and required further confirmation. Thirdly, although two metabolism related signature genes were identified as potential predictors for PSD, larger patient cohorts should be examined in the future to validate the correlation between two signature genes (SDHD and FERMT3) with PSD. Then further in vivo or in vitro studies should be carried out to validate diagnostic value and potential therapeutic value. ## Conclusion In conclusion, we identified two signature genes (SDHD and FERMT3) in peripheral blood of stroke patients by machine learning. SDHD and FERMT3 were found to be significantly associated with depression, and were identified as diagnostic and therapeutic signatures by our stroke cohorts with and without PSD, which could be a valuable reference for future clinical practice. ## Data availability statement The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The studies involving human participants were reviewed and approved by the Committee of Institutional Ethics of the First Affiliated Hospital of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YD, YZhe, BY, and RC designed the current study. XZ, XW, and ShuweiW collected the clinical information. YZha and SongW completed data downloading and processing. XZ, ZW, and YZha performed bioinformatics analysis. QY performed ELISA testing. YZhe, XZ, and YZha drafted the manuscript. YD supervised and modified the drafting process. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1146620/full#supplementary-material ## References 1. Bandara A., Drake J., Brown D.. 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--- title: Single-cell RNA-Seq analysis of diabetic wound macrophages in STZ-induced mice authors: - Jiaxu Ma - Ru Song - Chunyan Liu - Guoqi Cao - Guang Zhang - Zhenjie Wu - Huayu Zhang - Rui Sun - Aoyu Chen - Yibing Wang - Siyuan Yin journal: Journal of Cell Communication and Signaling year: 2022 pmcid: PMC10030741 doi: 10.1007/s12079-022-00707-w license: CC BY 4.0 --- # Single-cell RNA-Seq analysis of diabetic wound macrophages in STZ-induced mice ## Abstract The crucial role of macrophages in the healing of chronic diabetic wounds is widely known, but previous in vitro classification and marker genes of macrophages may not be fully applicable to cells in the microenvironment of chronic wounds. The heterogeneity of macrophages was studied and classified at the single-cell level in a chronic wound model. We performed single-cell sequencing of CD45 + immune cells within the wound edge and obtained 17 clusters of cells, including 4 clusters of macrophages. One of these clusters is a previously undescribed population of macrophages possessing osteoclast gene expression, for which analysis of differential genes revealed possible functions. We also analysed the differences in gene expression between groups of macrophages in the control and diabetic wound groups at different sampling times. We described the differentiation profile of mononuclear macrophages, which has provided an important reference for the study of immune-related mechanisms in diabetic chronic wounds. ### Supplementary information The online version contains supplementary material available at 10.1007/s12079-022-00707-w. ## Background The clinical treatments for chronic wound remain ineffective, and the dramatic increase in healthcare costs has created a heavy financial strain, with total US Medicare treatment costs for wound-related care ranging from $28.1 to $96.8 billion in 2018 statistics, in which diabetic ulcers and surgical wound were the most costly (Nussbaum et al. 2018). From the 2017–2020 US reported statistics, the 5-year survival rate for diabetic foot was $30.5\%$, which was similar to the 5-year survival rate for cancer combined of $31\%$, while in terms of care, diabetes care was more expensive, and a significant amount of funding was consumed for lower extremity care (Armstrong et al. 2020). The ageing population has a high prevalence of chronic wound, and ageing is becoming an important worldwide healthcare and demographic issue (OECD 2014). Projections indicate that in 2050, there will be more older people aged 60 years or older than adolescents aged 10–24 years (2.1 billion versus 2 billion) (Rudnicka et al. 2020). The global market for advanced wound care is expected to reach US$18.7 billion by 2027, growing at a compound annual growth rate (CAGR) of $6.6\%$ during the analysis period 2020–2027 (Sen 2021). Diabetic patients and obese individuals are at high risk of chronic wounds. Chronic inflammation is also an important cause of chronic wound in diabetes due to the long-term effects of high sugar and free fatty acids, which lead to a chronic inflammatory state in a variety of tissues. Chronic inflammation in diabetes can be manifested by the recruitment of immune cells such as macrophages and neutrophils to tissues and the release of proinflammatory cytokines (Pahwa et al. 2021). The current classification of macrophages as an important part of immune cells has revealed some differences in vitro and in vivo, and the previous markers for typing these cells are not well suited for the current study (Orecchioni et al. 2019). Single-cell sequencing, a new technology that has emerged in recent years (Saliba et al. 2014), provides a way to classify and functionally study cell populations in specific microenvironments independent of prior experience (Hedlund and Deng 2018; Stuart and Satija, 2019). Using the BD Rhapsody single-cell platform (Fan et al. 2015; Mair et al. 2020), we performed unbiased analysis of CD45 + immune cells from the skin of STZ-induced C57BL/6J mice and wild-type mice in a whole skin wound model to discover the distribution of the different populations of immune cells, as well as the genetic profile of wound-associated macrophages and analysed their temporal genetic differences. ## Animals In this study, male C57BL/6J mice weighing 20–25 g were used. All experimental protocols were approved by the ethics committee of Shandong Qianfoshan Hospital, Shandong University, and all experiments were performed according to the approved protocols. Mice were housed under controlled temperature (26 ± 1.5 °C) and humidity ($60\%$ ± 5) with a 12-h light/dark schedule. ## Development of the diabetic mouse model Streptozotocin (120 mg/kg, Sigma-Aldrich, USA, S0130) was administered to 4-week-old C57BL6J mice after 12 h of fasting. Blood glucose was measured after 7 days of stabilization, and mice with glucose levels ≥ 16.5 mmol/L (300 mg/dL) were considered diabetic and were used for the operation. ## Wound sampling Diabetic mice ($$n = 20$$) and control mice ($$n = 20$$) were anesthetized with isoflurane inhalation, and then the hair on the back was removed to create a full skin wound 5 mm in diameter, which was observed daily. The randomly selected mice ($$n = 5$$) were separately sacrificed on day 1, day 3, day 5 and day 7, and wound tissue was obtained. The wounds obtained from each group of randomly selected mice ($$n = 5$$) were mixed and used for the subsequent preparation of single-cell suspensions. ## Preparation of single-cell suspensions The digestion solution was composed of collagenase II (3.5 mg/mL, Solarbio, China), DNase I (0.02 mg/mL, Solarbio, China), hyaluronidase (30 U/mL, Solarbio, China), $5\%$ FBS-1640 (Gibco, USA), and DPBS (Gibco, USA). Samples were washed, the digestion solution was added to the tissue, and the tissue was cut and incubated at 37 °C for 1 h. The digestion solution was passed through a 40-µm cell strainer and then centrifuged at 500 x g for 5 min. The reaction was terminated by adding erythrocyte lysate for 5 min and centrifuging twice at 300 x g for 5 min. ## Flow sorting A portion of the cell suspension was centrifuged at 300 x g for 5 min. 100 µL of wash solution was used to resuspend the cell precipitate, 2 µL of APC anti-mouse CD45 antibody (BioLegend, USA) was added for every 1× 10 6 cells according to the total number of cells, and the mixture was incubated at 4 °C and protected from light for a total incubation time of 30 min. At 15 min of incubation time, 1 µL of calcein AM (BD, USA) was added for every 100 µL of cell suspension. After incubation, the cells were washed twice by centrifugation at 300 x g for 5 min; the supernatant was discarded, and the cells were resuspended and mixed with the appropriate amount of washing solution. A small amount of the suspension was stained with AO/PI, and the number of cells sorted, the cell viability and the clumping rate were recorded; the cells were centrifuged at 300 x g for 5 min and resuspended in sample buffer for BD quality control. ## Single-cell RNA sequencing The transcriptomic information of sorted CD45 + cells was captured by BD Rhapsody system. Single-cell was randomly distributed across > 200,000 microwells through a limited dilution approach. Beads with oligonucleotide barcodes were added to saturation so that a bead was paired with a cell in a microwell. The cells were lysed in the microwell to hybridize mRNA molecules to barcoded capture oligos on the beads. Beads were collected into a single tube for reverse transcription and ExoI digestion. Upon cDNA synthesis, each cDNA molecule was tagged on the 5′ end (that is, the 3′ end of a mRNA transcript) with a unique molecular identifier (UMI) and cell barcode indicating its cell of origin. Whole transcriptome libraries were prepared using the BD Rhapsody single-cell whole-transcriptome amplification (WTA) workflow including random priming and extension (RPE), RPE amplification PCR and WTA index PCR. The libraries were quantified using a High Sensitivity DNA chip (Agilent) on a Bioanalyzer 2200 and the Qubit High Sensitivity DNA assay (Thermo Fisher Scientific). Sequencing was performed by illumina sequencer (Illumina, San Diego, CA) on a 150 bp paired-end run. ## Single-cell RNA statistical analysis scRNA-seq data analysis was performed by NovelBio Co.,Ltd. with NovelBrain Cloud Analysis Platform. We applied fastp with default parameter filtering the adaptor sequence and removed the low quality reads to achieve the clean data. UMI-tools was applied for Single Cell Transcriptome Analysis to identify the cell barcode whitelist. The UMI-based clean data was mapped to mouse genome (Ensemble version 92) utilizing STAR mapping with customized parameter from UMI-tools standard pipeline to obtain the UMIs counts of each sample. Cells contained over 200 expressed genes and mitochondria UMI rate below $20\%$ passed the cell quality filtering and mitochondria genes were removed in the expression table. Seurat package (version: 2.3.4, https://satijalab.org/seurat/) was used for cell normalization and regression based on the expression table according to the UMI counts of each sample and percent of mitochondria rate to obtain the scaled data. PCA was constructed based on the scaled data with top 2000 high variable genes and top 10 principals were used for tSNE construction and UMAP construction. Utilizing graph-based cluster method (resolution = 0.8), we acquired the unsupervised cell cluster result based the PCA top 10 principal and we calculated the marker genes by FindAllMarkers function with wilcox rank sum test algorithm under following criteria:1. lnFC > 0.25; 2. pvalue < 0.05; 3. min.pct > 0.1. In order to identify the cell type detailed, the clusters of same cell type were selected for re-tSNE analysis, graph-based clustering and marker analysis. ## Cell-cycle discrimination analyses We used cell cycle-related genes, including a previously defined core set of 43 G1/S and 54 G2/M genes (Tirosh et al. 2016). For each cell, a cell cycle phase (G1, S, G2/M) was assigned based on its expression of G1/S or G2/M phase genes using the scoring strategy described in CellCycleScoring function in Seurat. Cells in each cell cycle state were also quantified using the which.cells function in Seurat. ## Pseudo-Time Analysis We applied the Single-Cell Trajectories analysis utilizing Monocle2 (http://cole-trapnell-lab.githu.b.io/monocle-release) using DDR-Tree and default parameter. Before Monocle analysis, we select marker genes of the Seurat clustering result and raw expression counts of the cell passed filtering. Based on the pseudo-time analysis, branch expression analysis modeling (BEAM Analysis) was applied for branch fate determined gene analysis. ## Cell Communication Analysis To enable a systematic analysis of cell-cell communication molecules, we applied cell communication analysis based on the CellPhoneDB, a public repository of ligands, receptors and their interactions. Membrane, secreted and peripheral proteins of the cluster of different time point was annotated. Significant mean and Cell Communication significance (p-value < 0.05) was calculated based on the interaction and the normalized cell matrix achieved by Seurat Normalization. ## QuSAGE Analysis (Gene Enrichment Analysis) To characterize the relative activation of a given gene set such as pathway activation, we performed QuSAGE (2.16.1) analysis. ## F1 scRNA-seq based identification of STZ-induced diabetic mouse wounds immune cell populations We performed scRNA-seq on CD45 + cells gathered from wound tissue obtained from wild-type and STZ-induced diabetic C57BL/6J mice (Fig. 1a). Four time points were selected for sampling (1, 3, 5, and 7 days). The single-cell data of the obtained samples were normalized by excluding low-quality cells to eliminate batch effects, and data from a total of 9240 cells were obtained. Principal component analysis (PCA) was performed, and the results were plotted with t-stochastic neighbour embedding (t-SNE) downscaled to show the distribution of cells from different sample sources in the overall data (Fig. 1b), along with the gene expression level of all single cells and the number of their UMI expressed (supplementary1). Fig. 1scRNA-seq based identification of STZ-induced diabetic mouse wounds immune cell populations. ( a) Experimental design. Single cell were collected from day1,day3,day5,day7,along wound healing (b) A t-distributed stochastic neighbour embedding (t-SNE) visualization of all cells displayed with different colours for samples (c) t-SNE visualization of 9240 single cells, colour-coded by assigned cell type (d) Heat map of all clusters top 20 upregulated marker gene. Shades of colour indicate high or low gene expression, with yellow being high expression and dark red being low expression QC cell data were unbiased using the Seraut package, and gene expression data from cells extracted from both conditions were aligned and projected in a 2D space through t-SNE to allow identification of overlapping and diabetic wound-associated immune cell populations. A total of 17 cell clusters were obtained, except for low-quality cells, which have a high preponderance of mitochondrial genes (Fig. 1c). We mapped the heat map of major marker genes in all populations (Fig. 1d). The cell populations obtained were 4 clusters of neutrophils (cluster 0, cluster 1, cluster 3 and cluster 12, with marker genes Ptprc, S100a8, s100a9, Csf3r, Cxcr2, and Lrg1); 2 clusters of monocytes (cluster 6 and cluster 8, with marker genes Ly6c2, Vcan, and Fn1); 3 clusters of macrophages (cluster 2, cluster 4, and cluster 9, with marker genes C1qa and Mrc1); 2 clusters of DC cells (cluster 5 and cluster 13, with marker genes Ccr7, Mgl2, Ccl22 Cd209a, and Fscn1), 1 cluster of NK cells (cluster 14, with marker genes Cd3d-, Xcl1, and Ncr1); 1 cluster of T cells (cluster 7, with the main marker genes Cd3d, Cd3e, Cd3g, and Trac); 1 cluster of mast cells (cluster 16, with the main marker genes Ms4a2, Cpa3, Gata2, and Tpsb2); 1 cluster of fibroblasts (cluster 17, with the main marker genes Col1a1 and Dcn); and 1 cluster of cells not previously described (cluster 11), with the main marker genes Acp5, Ctsk, Mmp9, Atp6V0d2, which are noted in the literature as marker genes for osteoclasts (supplementary 2, Table 1). Table 1Summary of Major Cell Types in the Wounds Healing ProcessCell clusterNo.of cellsMarker genesTranscription FactorsMajor functionNeutrophilCluster 05507Cxcr2,Csfr3,Il1r2Lrg1Cebpd,Egr1,Egr2inflammatory response,response to lipopolysaccharide,apoptotic process, cytokine-mediated signaling pathway,positive regulation of endothelial cell proliferation,positive regulation of angiogenesisNeutrophilCluster 14773Gm5483,IL1f9,Ccl3,Acod1,Stfa211Cebpd,Egr1,Egr2inflammatory response,response to lipopolysaccharide,neutrophil chemotaxis,immune response,cytokine-mediated signaling pathway,positive regulation of cytokine productionNeutrophilCluster32899Retnlg,Ngp,Lcn2,Wfdc21,Mmp8Cebpd,Egr1,Egr2inflammatory response,immune system processmyeloid dendritic cell chemotaxis,negative regulation of lymphangiogenesisNeutrophilCluster12594Rsad2,Ifit1,Ifit3,Isg20,Slfn1Cebpd,Egr1,Egr2innate immune response,response to virus,defense response to virusMacrophageCluster 23581Cd74,C1qb,Ctss,Apoe,Csf1rAtf3,Irf7,Klf4,Spicantigen processing and presentation of exogenous peptide antigen via MHC class II,immune system process,immune response,apoptotic cell clearanceMacrophageCluster 42793Ccl8,C1qc,Selenop, Mrc1,Cbr2,Igf1,Cd163Atf3,Egr1,Egr2,Irf7,Jun,Klf4,Mafb,Nr1d1,Spicinflammatory response,endocytosis,response to drug,angiogenesis,positive regulation of smooth muscle cell proliferation,positive regulation of cell proliferationMacrophageCluster 91587Gpnmb,Pf4,Lpl,Cd36,Fabp5Atf3,Egr2,Irf7,Klf4,Mafb,Nr1d1,Spicinflammatory response,lipid metabolic process,response to oxidative stress,response to oxidative stressMonocyte Cluster 62370Arg1,Pdpn,Ccl2,Cxcl1,Fn1Atf3,Egr2,Irf7,Klf4,Prdm1,SpicAngiogenesis,Inflammatory response,immune responseMonocyte Cluster 82140Plac8,Chil3,Vcan,Ly6c2,Lyz2Atf3,Egr2,Irf7,Klf4,SpicTranslation,monocyte chemotaxis,positive regulation of T cell activationDendritic cellCluster 52497H2-Ab1,H2-Aa,H2-Eb1,cd209a,H2-DMa,H2-Dmb1Irf7,Atf3,Nr4a2,Spibribosomal small subunit biogenesis,antigen processing and presentation of peptide or polysaccharide antigen via MHC class IIc,regulation of T cell proliferationDendritic cellCluster 13563Fscn1,Ccl22,Tbc1d4,Ccr7,Tmem123,Cacnb3Atf5,Ehf,SpibTranslation,antigen processing and presentation,immune system processT cellsCluster 72148Icos,Rora,Ets1Gata3,Rora,Tcf7,Stat4,CremT cell activation,T cell differentiationOsteoclast Cluster 11829Ctsk,Acp5,mmp9,Atp6V0d2Atf3,Irf7,Klf4,Nr4a2Translation, RNA splicing,mRNA processing,oxidation-reduction processNK cellsCluster 14487Gzma,Ccl5,Il2rb,Xc1l,Nkg7Eomes,Gata3,Myb,Tcf7Cytolysis,response to virus,positive regulation of T cell mediated cytotoxicityMast CellsCluster 16147Cpa3,Serpinb1a,Tpsab1,Mrgprb1,Gata2Egr1,Gata2,positive regulation of mast cell degranulationFibroblast Cluster 1750Fstl1,Bgn,Col5a1,Col16a1,Mmp2Ebf1,EGr1,Plagl1,Tbx15,Twist2cell adhesion,collagen fibril organization,extracellular matrix organization ## F2 scRNA-seq analysis reveals a dynamic immune landscape in STZ-induced diabetic mouse wounds After obtaining the overall spectrum of immune cells, we further counted the number of each group of immune cells in the two groups of wounds according to different time points and combined it with their gene expression. Exploring the differences in immune status between two groups of wounds healing process and their possible underlying causes (Fig. 2; Table 2, Supplementary 3). Fig. 2scRNA-seq analysis eveals a dynamic immune landscape in STZ-induced diabetic mouse wounds. ( a) Stacked bar plots showing the proportion of cells from each sample source among the different cell types (D-1: diabetes group day 1, D-3: diabetes group day 3, D-5: diabetes group day 5, D-7: diabetes group day 7, W-1: Wildtype control group day1, etc.) ( b) Pie chart plots showing the proportion of various cells in different samples Table 2Summary of Cell Numbers in Differently Sampling TimeClusterCellTypeSamples and Cell NumberD-1D-3D-5D-7 W-1 W-3W-5 W-70Neutrophil196128866264320913522169411Neutrophil8601167895127103860532492Macrophage2924402524731416954907983Neutrophil108520731215119175774Macrophage34207483072916292310835DC2154004215021034051532986Monocyte698286206175699103611427T cells1061404375661032131254588Monocyte6113383239233435524639Macrophage283691121639516542023510LowQuality1572411096813924915715011Osteoclast4114282941204349612Neutrophil273654011180180713DC26971171112775466414NK1619861084080613215Cycling T/NK3295489448234816Mast cells232510102524121817Fibroblast3407141021 The population of neutrophils that responded earliest to wound healing also had the correspondingly highest proportion of total cell counts, and the proportion decreased over the healing process, but the proportion of neutrophils declined gently on early days 1, 3, and 5 in the diabetic group, whereas a steep decline occurred on day 5 in the control group. The change of subcluster Retnlg + Lcn2 + Wfdc21 + Mmp8 + neutrophils (cluster3) was noteworthy, with the initial cell count in the control group consistent with that of the diabetic group (day1, 1085 vs. 1191) and then dropping rapidly to low levels (day5, 312 vs. 7). Lcn2 promotes neutrophil recruitment and can contribute to inflammation through synergistic Th17 (Hau et al. 2016; Shashidharamurthy et al. 2013). It is also a marker of inflammation associated with obesity and insulin resistance (Wang et al. 2007). The enrichment of MMp8 in chronic inflammation and its ability to degrade the extracellular matrix suggest that this group of neutrophils may be a factor in the chronic healing of diabetic wounds (Diegelmann 2003). At day1 monocytes were the second most abundant cell type and then began to decline, with the proportion of monocytes on day 5 in the control group decreasing dramatically and being significantly lower than in the diabetic group ($1.83\%$ vs. $11.28\%$). Monocytes were divided into two groups of subclusters, Arg1 + Pdpn + Ccl2 + Cxcl1 + Fn1 + monocytes (cluster6). The functions of cluster 6 c include Angiogenesis in addition to Inflammatory response, immune response. Although Arg1 has been reported to be elevated in ischaemic chronic wounds (Roy et al. 2009), the initial cluster6 cells counts we observed in both groups of wounds were consistent, so the differential decrease in this group of monocytes may not be due to a compensatory effect but may be due to a blocked conversion of monocytes to macrophages in diabetic wounds. In contrast, the other group of Plac8 + chil3 + Vcan + Ly6c2hi, CCR2hi monocytes (Cluster8) was consistent with the inflammatory monocytes reported previously (Shi and Pamer 2011). The number of cells in this group was significantly higher in the diabetic group than in the control group at the beginning (day1, 611 vs. 334) and reversed at the end (day5, 323 vs. 24). These results suggest that excessive inflammation in diabetic wounds in terms of monocytes may be the result of a combination of pro-inflammatory monocyte retention and impaired monocyte-macrophage transformation. DC cells regulate and activate endogenous and adaptive immunity, further activating T cells through antigen presentation. We observed a gradual increase in the proportion of Dc cells in diabetic wounds from day1-day7, with a regression in the number of cells in the control wounds group from day5-day7. In addition to the classical Dc cells expressing major histocompatibility complex class II (cluster5, H2-Ab1 + H2-Aa + H2-Eb1+) there was also a group of cells expressing the Fscn1, Ccl22, Tbc1d4, Ccr7 marker gene, migration-related (CCR7, FSCN1), and encoding chemokine ligands (CCL22) suggest the function of recruit immune cells, mostly Tregs (Peng et al. 2022).The proportion of T cells (cluster7, Icos + Rora + Ets1+) followed the same trend as that of Dc cells. Day5 was the cut-off point for the change in the proportion of numerous cells in both wounds group, and the differences in the immune profile between wounds in terms of the number and proportion of cells accumulated from day1 to day3, after which the differences in the degree of inflammation between the control and diabetic wounds groups became highly significant. The precise timing of interventions selected for different immune cell populations appears to be important in promoting diabetic wound healing. ## F3 Gene expression characteristics and biological function analysis of cluster 11 and the gene expression differences compared with other macrophages The steep increase in the numbers of cells expressing osteoclast marker gene (clusters11) in the control wounds group attracted our attention. To characterize cluster 11 as a specific group of immune cells, we mapped the top 20 marker genes on a violin plot (Fig. 3a) and performed GO functional enrichment analysis of the marker genes. The genes that were highly expressed were the osteoclast-associated genes Ctsk and Acp5; the adipose tissue-associated genes Hmgn1, Ranbp1 and Lpl; and the macrophage-associated genes Tsc22d1 and Banf1. The cycling basal cell-related genes Stmn1, Top2a, Ube2c, Pclaf, and Birc5 suggest that this group of cells may be a previously undescribed type of skin-resident macrophage. The GO functional enrichment analysis results showed that the gene functions were mainly related to translation, RNA splicing, mRNA processing, rRNA processing, oxidation-reduction process, translational initiation tricarboxylic acid cycle, cell cycle, protein folding, transport, etc. ( Fig. 3b) We also applied cell cycle analysis, according to G2M.Score, only a small part of cluster 11 cells were enriched for cell cycle gene (Stmn1, Top2a). ( Supplementary 4), The self-renewal and proliferation of this small number of cells indicates that this group of cells is actively involved in the healing process and suggests that our data are indicative of the dynamic characteristics of this group of cells during the healing process. Fig. 3Gene expression characteristics and biological function analysis of cluster 11 and the gene expression differences compared with other macrophages. ( a) Violin plot view cluster11 top 20 marker gene demonstrating overall gene expression. The number of identity is the same of clusters (b) GO histogram analysis results of cluster11 marker gene: Biological Process (BP), Molecular Function (MF), Cellular Component (CC). Coordinate axis Y: Go-Term entry name,Coordinate axis X: -log10 (P-Value). Red for significant entries, blue for non-significant entries (c) Volcano plot view for the gene expression difference between cluster11 and other macrophages(cluster2,4,9).Coordinate axis Y: -log10(P-Value), axis X:avg_log2FC.X<-1 use pink color as down expression, X > 1 use blue color as up expression (d, e) GO analysis up regulated (d) and down regulated gene (e) of cluster11: Biological Process, Coordinate axis Y: Go-Term entry name, Coordinate axis X: Gene Ratio. Colors of the bubble represents P.adjust < 0.05 for significant entries. The size of the bubble indicates the number of genes enriched in this item We further compared the gene expression differences between cluster 11 and all other macrophages (cluster 2, cluster 4, and cluster 9). A total of 230 genes were upregulated and 205 genes were downregulated in cluster 11 compared to the other macrophage populations (Fig. 3c). GO enrichment of the differential genes showed that upregulated genes were enriched in tissue remodeling, skeletal system development, multicellular organismal homeostasis, cation transmembrane transport, cation transport, collagen metabolic process, bone resorption, bone remodeling, porton transmembrane transport, and tissue homeostasis (Fig. 3d). Biological functions of the downregulated genes are enriched in defense response, immune response, inflammatory response, response to bacterium, leukocyte migration, myeloid leukocyte migration, cell chemotaxis, granulocyte migration, neutrophil migration, and granulocyte chemotaxis (Fig. 3e). These results suggest that this group of cells is not primarily involved in the inflammatory process, instead may be involved in the wound healing process by balancing tissue homeostasis, tissue remodelling, and collagen metabolism in the extracellular matrix. This also explains the difference in their distribution between the two groups of wounds samples. ## F4 Macrophage gene metabolism pattern analysis and cell-cell contact We observed that the differentially expressed genes in cluster 11 were enriched in multiple metabolic pathways, and we generated a metabolism heatmap for all cell populations. The gene metabolism patterns of cluster 11 were highly enriched in one-carbon pool by folate, vitamin B6 metabolism, lipoic acid metabolism, synthesis and degradation of ketone bodies, citrate cycle, oxidative phosphorylation, 2-oxocarboxylic acid metabolism, carbon metabolism, pyruvate metabolism, fatty acid biosynthesis, and cysteine and methionine metabolism. Among the remaining macrophage populations, cluster 4 and cluster 9 showed some similarity in gene metabolism patterns and differed significantly from cluster 2. The similarities between cluster 4 and cluster 9 were mainly enriched in caffeine metabolism, glycosphingolipid biosynthesis – globo and isoglobo series, sphingolipid metabolism, other glycan degradation, glycosaminoglycan degradation, ascorbate and aldarate metabolism, and glycosphingolipid biosynthesis – ganglio series (Fig. 4a). Macrophage function is dependent on different metabolic pathways, and the metabolism-related gene set of cluster11 is actively enriched, particularly in the tricarboxylic acid cycle and glycolysis-related genes. Tissue and vascular-related damage and hypoxia during the inflammatory phase have little effect on inflammatory macrophages, which are mainly dependent on glycolysis (Murdoch et al. 2005). The enrichment of anabolic metabolism is one of the key features of the tissue proliferation, repair and remodelling phase, and this population of macrophages, which peaks during the repair phase, achieves its repair-promoting function through active metabolism, while the high glucose environment of diabetic wounds and its induced production of ROS affects the activation and function of aerobic metabolic pathways of macrophages (Rendra et al. 2019). In particular, this population of cells is not active in metabolism with arginine and ornithine compared to the traditionally defined alternative activated macrophages, as a potential target for metabolism-related interventions that may avoid excessive scarring and fibrosis (Liu et al. 2017). Fig. 4Macrophage gene metabolism pattern analysis and cell-cell contact. ( a) Heatmap of Qusage Analysis, shows the significance of enrichment between cluster in metabolism gene set. Axis Y: Gene set information,aixs X:clusters. The colour represents the significance of each cluster in each gene set, the closer the colour to red, the more significant it is; the closer the colour to blue, the less significant it is (b) Heatmap show number of potential ligand-receptor pairs between immune cell groups predicted by CellphoneDB The violin plots for the marker genes expressed in cluster 2, cluster 4, and cluster 9 showed that cluster 4 expressed genes that were similar to those previously defined as “M2 macrophages” (Mrc1 and cd163). Cluster 2 had more pro-inflammatory genes, and the genes cd74, tnsf9, tnsf12, and tnsf12a were highly expressed. Gene expression of Gpnmb, Pf4, Lpl, Cd36, Apoe were found more significant in cluster9 (supplementary 5). To further characterize cell-cell interactions, we inferred putative cell-cell interactions based on ligand-receptor signaling inferred from our scRNA-seq data using CellPhoneDB. fibroblasts and macrophages showed the most interactions (Fig. 4b). Further visualization of intercellular interactions revealed that hebp1/Fprs ligand-receptor pairs are widespread among neutrophil macrophages and mediate the recruitment of monocytes to play a reparative role (Birkl et al. 2019). The cell-cell contact between cluster11 and neutrophils is supported by Sema4d/ Cd72 (Supplementary 6), and the promotion of cluster11 production by neutrophils may be related to Sema4D inhibition of osteogenic activity and promotion of osteoclastogenesis (Shindo et al. 2022). Cluster2 pro-inflammatory macrophages communicate with monocytes (cluster8), macrophages (cluster$\frac{4}{9}$) as well as Dc cells (cluster13), fibroblasts (cluster17) via Grn/Sort1 ligand-receptor pairs (supplementary 7), and the multiple involvements of this immunomodulatory mechanism in our cutaneous trabecular immune cells include in Grn/Sort1 regulates the migration and division of fibroblasts for angiogenesis and the recruitment and activity of immune cells during wound repair (Terryn et al. 2021). According to our findings Grn can be used as one of the biological indicators of the intensity of the inflammatory response in skin wounds. The group of fibroblasts (cluster 17, Pdgfrahigh, Acta2+, CD45+, Col12a1+) we identified while sorting myeloid cells had some similarities to fibroblasts that have been previously reported to be differentiated from trabecular myeloid cells (Haensel et al. 2020). Due to our prior cell sorting based on myeloid markers, fibroblasts were less numerous but showed strong auto cellular communication (Fig. 4b), which we observed in greater numbers in control group, and the expression of secreted cytokines such as VEGF supported their role in repair angiogenesis (supplementary3, 8). Other fibroblast populations could not be located in our samples, so the role of interfibroblast communication could not be clarified, but the role of these myeloid-derived fibroblasts on other myofibroblasts in the wounds was seen in earlier reports(Suga et al., 2014). These myeloid-derived intermediate cells are very easy to miss in the in vitro observation of fibroblasts and single-cell sequencing provides an excellent tool for analysis. ## F5 Differences in the proportion of macrophages over time and the differences in gene expression between the diabetic wound group and the control group The phenotypic changes and overall proportional changes in macrophages in the two different subgroups are also an important part of our understanding of their mechanisms. Thus, we counted the proportional changes in the macrophage populations in the two experimental groups at different sampling times, and the proportion of the cluster 11 cell population increased in the early stage (day 1–day 3) in both the diabetic wound group and the control group, but unlike the diabetic wound group, the proportion of this cell population in the control group increased consistently ($1.26\%$) on day 5 and was much higher than that in the diabetic wound group ($0.08\%$) and decreased ($0.28\%$) on day 7, but the proportion was still higher than that of the diabetic group ($0.08\%$) (Fig. 5a). The proportion of Cluster 2 cells was higher in diabetic groups on day 1($0.85\%$ versus $0.41\%$), with similar trends in cell proportions within both groups. After day 3 the proportion of cluster2 cells was higher in the control wound group than in the diabetic wound group ($1.42\%$ versus $0.73\%$). Cluster 4 showed a gradual increase in the proportion of cells in the diabetic wound group, except on day 5. In the control group, however, a much higher increase was observed on day 5 ($3.14\%$) and day 7 ($8.09\%$) than that in the diabetic group. A peak in the proportion of cluster 9 was observed in the diabetic group ($1.07\%$) at an earlier time point (on day 3) than in the control group ($1.22\%$ on day 5) (Fig. 5b). Fig. 5Differences in the proportion of macrophages over time and the differences in gene expression between the diabetic wound group and the control group. ( a) The proportion of cluster 11 cells in the control group on day 5 and day 7 was much higher than diabetic group. Coordinate axis Y: Proportion of specific cluster of cells in all single cells, Coordinate axis X: cell clusters, group and sampling time. Ex: D-1(Diabetic group -day 1), W-1(Wild type control group-day 1) (b) The proportion of cluster 2, cluster 4, and cluster 9 cells in the control group and diabetic group (c) GO enrichment analysis of the differential genes and found that the differences in the biological functions between the two groups with respect to cluster 11 on day 3 (d) GO enrichment analysis of the downregulated genes in cluster 11 on day 5 in the diabetes group and the biological functions of the differences (e) KEGG terms of control and diabetes two groups of cluster 11 cells on day 3 differentially expressed genes (f) KEGG terms of the cluster 11 cells on day 7 upregulated genes in the diabetes group (g) GO enrichment analysis of the upregulated genes in cluster 4 on day 5 in the diabetic group and the biological functions of the differences (h) KEGG enriched differential gene pathways on day 5 of cluster 4 It is well known that the immune environment of the diabetic group differs from that of the control group, so the specific differences in the macrophage population at different time points are of interest to us. In the next step, we performed GO enrichment analysis of the differential genes and found that differences in the biological functions between the two groups with respect to cluster 11 on day 3. The number of cells in the two groups was very similar at day3, and the genes we found to be different included Acod1, Ccl3, Ctsk, vHsap5, Mmp9, and Fos, the functions of these genes were mainly enriched in immune system process, response to external stimulus, regulation of immune system process, regulation of neuron death, defense response to bacterium, and cellular response to oxidative stress (Fig. 5c). Differentially expressed genes CCl3, Hsap5, Mmp9, Fos, Ctsk corresponded to rheumatoid arthritis, lipid and atherosclerosis, and the Toll-like receptor signaling pathway (Fig. 5e). As the wound healing progressed we found a huge difference in the number of this group of cells on day5, the peak number of cluster11 osteoblast-like macrophages. Apoe, Ccl7, Cd36, and Fnip1 genes were downregulated in the diabetic group compared to the control group on day5, the biological functions of the downregulated genes were enriched in positive regulation of protein modification process, positive regulation of cell communication, positive regulation of protein phosphorylation, positive regulation of phosphorus metabolic process, negative regulation of cell death, The downregulation of the Hmox1, Jun, Pf4 gene suggests that this group of cells Capacity of blood vessel morphogenesis has also been reduced (Fig. 5d). Then at day 7, the end of our observation H2-Aa, H2-Ab1, H2-DMa, H2-DMb1, H2-Eb1, Il1b genes were upregulated in the diabetes group corresponded to type I diabetes and Th17 cell differentiation (Fig. 5f). As for the pro-inflammatory macrophage-cluster2, the expression level of Acod1, Il1a, Mt1, Retnlg, Osal1, Lyz1, Saa3, CD163, was different in day 3, functions of these genes are enriched in response to stress, defence response, response to external stimulus, response to external biotic stimulus, response to other organism, response to biotic stimulus, interspecies interaction between organisms, response to bacterium, immune response, inflammatory response, and defence response to other organism. This enhanced immune response capacity is in line with our expectations. In parallel to the downregulated gene of pro-inflammatory macrophages in the diabetic group, mainly Lpl, which functions in cytokine production and regulation of cytokine production and with the healing process, in day5 we observed a decrease in Lpl, Cd36, Lipa genes which could suggest the possible involvement of cholesterol metabolism pathway in the progression of inflammatory cells. Inflammatory pathways suppress cholesterol metabolism and reverse cholesterol transport (RCT) which in turn enhances inflammatory responses, previously reported mainly in atherosclerosis-related studies, a similar mechanism can now be considered in diabetic skin damage (Groenen et al. 2021; Westerterp et al. 2018; Yvan-Charvet et al. 2008) (Supplementary 9). On day 1 the cluster 4 macrophages differentially expressed genes Lgals3, Bcl2, Sod2, Hsph1 biological functions of these genes were enriched in positive regulation of developmental process, negative regulation of apoptotic signalling pathway, Ccl3, Tlr2, Tnfaip3 genes were enriched in interleukin-1 beta production, and interleukin-1 production. On day 3 differential genes Acod1, Cd209b, Cd209d, Clec4e were enriched in response to external stimulus, defence response, interspecies interaction between organisms, response to external biotic stimulus, response to other organism, andCd163, Cfh, Chil3 inflammatory response (Supplementary 10). The biological functions of the upregulated genes at day 5 in the diabetic group Acod1, H2-Aa, H2-Eb1, S100a8, A100a9, Slpi were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, interspecies interaction between organisms, innate immune response, defense response to other organism, and response to lipopolysaccharide (Fig. 5g). The KEGG enriched differential gene H2-Aa, H2-Eb1 on day 5 were autoimmune thyroid disease, allograft rejection, graft-versus-host disease, type 1 diabetes mellitus, antigen processing and presentation, systemic lupus erythematosus, Staphylococcus aureus infection, and viral myocarditis pathways (Fig. 5h). The downregulated genes in cluster 9 in the diabetes group on day 1 including Arg1, Cxcl3, Plac8, Saa3 were enriched in response to external biotic stimulus, response to other organism, response to biotic stimulus, defence response (Supplementary 11). The downregulated genes at day 3 Ccr2, Egr1, Fos, and Jun were functionally enriched in tissue development, leukocyte differentiation, blood vessel development, vasculature development, cellular response to growth factor stimulus, and response to growth factor positive regulation of endothelial cell proliferation. The upregulated gene on day 5 including Ccl3, S100a8, S100a9, and Tnbs1, their functions were enriched in positive regulation of response to external stimulus, regulation of hydrolase activity, granulocyte chemotaxis, regulation of peptidase activity, granulocyte migration, myeloid leukocyte migration, and leukocyte chemotaxis (Supplementary 11). On day 7 the downregulated genes Egr1,Fos, and Jun were enriched in biological functions including response to abiotic stimulus, cellular response to stress, positive regulation of pri-miRNA transcription by RNA polymerase II, and positive regulation of neuron death (Supplementary 11). The KEGG analysis showed that the downregulated genes at day 3 Cracr2b, Nr4a1, Fos, Jun, Vegfa were involved in the MAPK signalling pathway, Nr4a1, Fos, Jun were involved in the relaxin signalling pathway, chemical carcinogenesis-receptor activation, and rheumatoid arthritis. The downregulated genes on day 7 Fos, Hspa1a, Hspa1b, and Jun were enriched in the Estrogen signalling pathway, measles, MAPK signalling pathway, lipid and atherosclerosis, prion disease, human T-cell leukaemia virus 1 infection, endocrine resistance, and antigen processing and presentation (Supplementary 11). Taken together these suggest that the MAPK pathway may play a regulatory role in the proliferation and differentiation of this group of macrophage cells. ## F6 Pseudotime analysis of macrophage populations and analysis of the differential gene expression patterns in the developmental branches Monocytes can differentiate into macrophages during the immune process, and macrophages have rich phenotypic diversity and perform different functions at different times during wound healing. We performed a chronological analysis of the observed mononuclear macrophage population and the cells in the diabetic and control wound groups could be classified into 11 states (Fig. 6a and b). According to the pseudotime analysis (Fig. 6c and d), cluster 6 and cluster 8 were predominantly found in the early states, followed by cluster 2, and cluster 4 and cluster 9 were found in large numbers at later time points. In the diabetic wound group, a large number of cluster 4 cells were observed in only one state, whereas in the control group, cluster 4 cell aggregates were observed in several states. In contrast, in the diabetic group, cluster 2 was observed within multiple stages of differentiation (Fig. 6e and f). This finding suggests that within the diabetic group, cell differentiation was more towards cluster 2, whereas in the control group, more branches were differentiated into cluster 4, and the greatest number of cluster 4 aggregates could be seen in the diabetic group with a branch point of 3 compared to 1 in the control group, leading us to more closely analyze the differential gene expression patterns of the two trajectory branches. Fig. 6Pseudotime analysis of macrophage populations and analysis of the differential gene expression patterns in the developmental branches. ( a, b) Reconstruction of the monocyte/macrophage trajectory as 11 state. Diabetes group (a), Control group (b). ( c, d) Reconstruction of the monocyte/macrophage trajectory in a pseudotime manner. Diabetes group (c), Control group (d). ( e, f) Reconstruction of the monocyte/macrophage trajectory in cell clusters. Diabetes group (e), Control group (f). ( g, h) Heatmap of branch 3 gene in diabetes group (g), branch 1 gene in control group (h), the pre-branch in the middle represents all the cells from the branch point to the root cell. Cell fate 1 corresponds to the state with small id while cell fate 2 corresponds to state with bigger id In branch 1 of the control group and branch 3 of the diabetic group, a pattern of differential expression consisting of the grouping of genes with a reduction in the differentiation pathway towards cluster 4 and an elevation of the differentiation pathway towards cluster 2 can be observed, with such a pattern seen in branch3 of the diabetic group for Acod1, Slc7a11, il1a, spp1, Ccdc71l, Tnbs1, F10, Ptgs2, Chil3, Met, and Cxcl3 (Fig. 6g). In the control group, there were Tgfbi, cd52, plac8, Ifi2712a, plbd1, lmnb1, gpr132, lsp1, ly6a, ccr2, and cytip in branch1 (Fig. 6h). No crossover genes were found, suggesting that the polarization patterns of macrophages in the control and diabetic group may be quite different. ## Discussion Using single-cell sequencing unbiased analysis, we described immune cell populations within the wound-associated cells of STZ-induced diabetic and wildtype control mice and defined a population of macrophages expressing osteoclast cell marker genes. The proportion of cd45 + immune cells within the skin was also found to vary between populations at different time points. We proximally analysed the differences in gene expression in macrophage populations at different sampling points and found evidence of temporal variation in the effects of immune dysfunction on wound healing in diabetic mice. The differences in the developmental differentiation trajectory of mononuclear macrophages were also analysed to describe specific differential genes at the macrophage differentiation and polarization branching points. Our wound-associated macrophage profile yielded three classes of macrophages, with cluster 2 macrophages expressing the cd74, tnfsf9, tnfsf12, and tnfrsf12a genes, suggesting their potential for proinflammatory function. Cd74 is a high affinity receptor on the cell membrane that binds macrophage migration inhibitory factor (MIF) (Su et al. 2017).The interaction of MIF with CD74 can occur at an early stage as a manifestation of the cellular response to injury. CD74 is involved in numerous inflammatory-related disease processes, and recent studies in inflammatory bowel disease (IBD) have shown a strong association between CD74 polymorphisms and the failure of anti-TNF therapy in patients with ulcerative colitis (Yoon et al. 2017). In a mouse model of experimental ischaemia-reperfusion injury, renal tubular injury was more severe in MIF, MIF-2 and CD74 knockout mice than in wild-type control mice(Ochi et al., 2017), and the autoimmune disease systemic lupus erythematosus (SLE) can cause renal inflammation known as lupus nephritis (Almaani et al. 2017). In mouse models of SLE, researchers have observed elevated levels of CD74 expression in B lymphocytes, and elevated MIF has been demonstrated in lupus-prone strains of mice. Inhibition of MIF and knockdown of CD74 protect against glomerulonephritis in lupus-susceptible mice (Lapter et al. 2011; Zhou et al. 2017). CD74 has been less well reported in skin tissue injury, but there is previous evidence in early animal skin injury models that MIF-CD74 promotes the proliferation and migration of keratinocytes at the trabecular margin (Abe et al. 2000). In our experiments, KEGG pathway enrichment analysis suggested that the difference in the LPL gene expression between the wound groups at day 5 was related to the cholesterol metabolic pathway, perhaps leading to a greater response to inflammation in the diabetic wound group than in the control group. Tests of macrophage depletion in wounds at different time points have also shown that early depletion of macrophage populations significantly contributed to delayed wound healing but could attenuate late scar formation (Lucas et al. 2010). This finding suggests that CD74 + immune cells may have different roles within different tissues and in different immune settings. In our present results, CD74high macrophages were observed in the early stages in both groups of samples, with the control group having lower responsiveness than the diabetic group on the first day and a higher overall proportion in the control group at the later stages. This group of macrophages was also observed to express Tgfbi. As CD74high macrophages appear at an early stage as a high proportion of the macrophages, it remains to be investigated whether modulation of CD74 expression can influence scar formation. Cluster 4 cells expressed the mrc1-cd206, il-10, cd163 and cbr2 genes, with gene expression patterns similar to those traditionally described for M2 macrophages, while cluster 4 cells also expressed the F13a1, lyve1, and gas6 resident-like macrophage genes (Beckers et al. 2017; Ensan et al. 2016). Notably, when comparing the differences in gene expression between cluster 4 cells from the diabetic and control wounds, the diabetic group was found to express higher levels of Lyve1, while the control group expressed higher levels of ccr2, suggesting that this group of cells with the M2 cell gene signature may have origins in both monocyte recruitment and resident macrophages (Cochain et al. 2018). In our experiments, the distribution of M2-like macrophages in both groups was much higher in the control group than in the diabetic group, suggesting that the control group included resident macrophages and more differentiated monocytes recruited from the blood, whereas the M2-like macrophages in the diabetic group were more dependent on their own resident macrophages. A comparative analysis of the gene differences between the two groups at different time points showed that the expression of il-1 was promoted at an early stage in the diabetic group, and later, the diabetic group had a higher level of inflammatory response and defence function than the control group. In contrast, in the control group, the differential gene expression in the early phase was mainly enriched in the functions of proliferation and apoptosis regulation. Higher cd163 expression was observed in the diabetic group than in the control group at both day 3 and day 7. It has also been suggested that sCD163 is higher in type 2 diabetes patients than in healthy individuals (Semnani-Azad et al. 2021). These results may suggest that the elevated feedback appearance of cd163 as an anti-inflammatory gene in macrophages is one of the protective mechanisms promoting wound repair in the diabetic group. The KEGG pathway analysis of the day 5 differential genes showed enrichment in type 1 diabetes and several pathways associated with autoimmune diseases in both groups, suggesting that immune disorders in diabetic patients are more prevalent in this group of macrophages. Cluster 9 cells are highly similar to cluster 4 in their M2-like macrophage gene expression profile, with the difference being that cluster 9 is highly Gpnmb-expressing, and in tumor-related studies, the tumor-promoting role of myeloid cells is associated with Gpnmb, which can promote cancer cell survival, cancer stem cell expansion and metastatic phenotype acquisition via IL-33(Liguori et al. 2021). This ability to promote stemness was transduced in skin injury to promote stem cell proliferation and repair capacity in the skin, and transplantation of GPNMB-expressing macrophages improved skin healing in GPNMB-mutant mice. Furthermore, topical treatment with recombinant GPNMB restored mesenchymal stem cell recruitment, prompted polarization of wound macrophages towards anti-inflammatory M2 macrophages, and accelerated wound closure in diabetic skin (Yu et al. 2018). It remains to be determined whether this group of cells appearing in a higher abundance at the peak of the proportion of diabetic wounds (day 3) but at a lower level than that in the control group afterward is the result of some inflammatory influence that hinders the proliferation and expression of this group of cells in the mid-term. Our results show that among the enriched GO biological functions of the differentially expressed genes in cluster 9 on day 3, the functions of the downregulated genes are associated with tissue repair, angiogenesis and development and cellular response to growth factors. It has been demonstrated that Junb knockout mice can develop normally, but a lack of Junb under wound conditions results in excessive epidermal skin proliferation and a delayed inflammatory disorder remodelling phase (Florin et al. 2006). Nr4a1, a monocyte transition gene, has been shown to have an important role in genetic models and in the differentiation of monocytes/macrophages in the mouse intestine (Honda et al. 2020). According to the differential analysis, the elevated Gpnmb-high macrophage Tgfbi, IL10, and CD163 expression in the diabetic wound group on day 5 and day 7 may be a result of a delayed repair phase compared to the control group. Osteoclast macrophages (cluster 11) are an interesting group of cells with high expression of the osteoclast-associated genes Ctsk and Acp5; the adipose tissue-associated genes Hmgn1, Ranbp1, and Lpl; the macrophage-associated genes Tsc22d1 and Banf1; and the cycling basal cell-associated genes Stmn1, Top2a, Ube2c, Pclaf, and Birc5. Under physiological conditions, macrophages and osteoclasts are part of the outcome of monocyte differentiation, and the main determinants of osteoclast production are the relative concentrations of CSF-1, RANKL, and osteoprotegerin (OPG; TNF receptor superfamily member 11B) (Teitelbaum and Ross, 2003; Walsh et al. 2006). In vitro stimulation of RAW264.7 macrophages with RANKL can result in osteoclasts (Song et al. 2019; Zheng et al. 2020). Advanced glycation end products (AGEs) in the diabetic state can affect the expression of bone metabolism proteins (Asadipooya and Uy 2019). Increased reactive oxygen species in diabetes can also affect the balance between osteoclasts and osteoblasts, leading to osteoporosis (Loi et al. 2016). Chronic inflammatory skin diseases such as atopic dermatitis (AD) and psoriasis vulgaris (Pso) are associated with osteoporosis (Shaheen and Silverberg, 2019; Wu et al. 2017). Secondary osteoporosis occurs in a transgenic model of spontaneous dermatitis(Mizutani et al., 2020). All of this evidence suggests that an increase in osteoclasts occurs in response to a strong inflammatory response and stimulation by proinflammatory factors. However, in our study, the proportion of macrophages with osteoclast markers was lower in diabetic mouse wounds with enhanced inflammatory responses, suggesting that, unlike the mechanism of cutaneous inflammation leading to arthritis and osteoporosis, this may be a localized form of cell differentiation specific to the skin. Human bone, epidermis and hair are all lifelong renewable tissues, and perhaps due to this similarity, except for the role of NF-κB receptor activator (RANK) in osteogenesis and resorption, mice lacking RANK ligand (RANKL) are unable to initiate a new growth phase of the hair cycle and display stalled epidermal homeostasis. RANKL can be expressed in the skin by activated interfollicular epidermis (Barbaroux et al. 2008), and RANK-RANKL regulates hair renewal and epidermal homeostasis and provides a communication channel between these two activities (Duheron et al. 2011). This regulatory ligand for long-term self-renewal may be responsible for the generation of our group of osteoclast-like macrophages. The metabolic pathways and differential gene enrichment results of this group of cells also suggest that they have a strong biosynthetic and metabolic capacity and that the response of this group is stronger in control wounds, suggesting that perhaps this class of skin-specific macrophages contributes to skin homeostasis and repair after damage. There are still some limitations to this study. The time points chosen for this study are based on previous studies and animal studies and are still somewhat intermittent, as there may be specific peaks in immune cell changes and microenvironmental regulation that do not necessarily occur at the times we chose. The study was conducted at the transcriptional level only and did not include other factors that may affect macrophage function. Further validation of histological level staining for specific cells and the use of knockout animals are needed to better explore and demonstrate the immune-related mechanisms of diabetic wound healing. These macrophages vary in type and temporal characteristics, and the classical definitions describing the markers and classifications of classically activated macrophages and alternative activated macrophages, or M1 and M2 cells, do not seem to match the descriptions exactly. These results suggest that in vitro studies of immune cells with a single factor and a small number of markers may not accurately model the in vivo environment, especially when analysing the differences in their temporal patterns. More precise gene profiling of cells in vivo and improvements in the way in which they are tracked in vivo may better enable us to identify new immune regulatory mechanisms and therapeutic targets. ## Conclusion In summary, we characterized the genetic profile of macrophage populations in wounds of diabetic and wildtype control mice by single-cell sequencing and identified a population of macrophages with osteoclast-like gene expression, presumably associated with skin renewal and repair responses. We described the genetic differences between the different cell populations in the wounds of the two groups according to chronological order, providing a closer look into specific processes. 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--- title: Identification reproducible microbiota biomarkers for the diagnosis of cirrhosis and hepatocellular carcinoma authors: - Huarong Zhang - Junling Wu - Yijuan Liu - Yongbin Zeng - Zhiyu Jiang - Haidan Yan - Jie Lin - Weixin Zhou - Qishui Ou - Lu Ao journal: AMB Express year: 2023 pmcid: PMC10030758 doi: 10.1186/s13568-023-01539-6 license: CC BY 4.0 --- # Identification reproducible microbiota biomarkers for the diagnosis of cirrhosis and hepatocellular carcinoma ## Abstract Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence in China, which is mainly related to chronic hepatitis B (CHB) and liver cirrhosis (LC) caused by hepatitis B virus (HBV) infection. This study aimed to identify reproducible gut microbial biomarkers across Chinese population for LC and HCC diagnosis. In this study, a group of 21 CHB, 25 LC, 21 HCC and 15 healthy control (HC) were examined, and used as the training data. Four published faecal datasets from different regions of China were collected, totally including 121 CHB, 33 LC, 70 HCC and 96 HC. Beta diversity showed that the distribution of community structure in CHB, LC, HCC was significantly different from HC. Correspondingly, 14 and 10 reproducible differential genera across datasets were identified in LC and HCC, respectively, defined as LC-associated and HCC-associated genera. Two random forest (RF) models based on these reproducible genera distinguished LC or HCC from HC with an area under the curve (AUC) of 0.824 and 0.902 in the training dataset, respectively, and achieved cross-region validations. Moreover, AUCs were greatly improved when clinical factors were added. A reconstructed random forest model on eight genera with significant changes between HCC and non-HCC can accurately distinguished HCC from LC. Conclusively, two RF models based on 14 reproducible LC-associated and 10 reproducible HCC-associated genera were constructed for LC and HCC diagnosis, which is of great significance to assist clinical early diagnosis. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13568-023-01539-6. The online version contains supplementary material available at 10.1186/s13568-023-01539-6. ## Key points Gut microbial biomarkers across Chinese population can be used as a non-invasive tool for the diagnosis of LC and HCCWe constructed two RFmodels based on reproducible genera to distinguish LC or HCC from HCThe efficacy of two models was greatly improved when clinical factors were added ## Introduction Hepatocellular carcinoma (HCC), the predominant form of liver cancer, is the third leading cause of cancer-related deaths worldwide. According to the statistics of the World Health Organization’s International Agency for Research on Cancer in 2020, there are about 410,000 new cases of HCC and 390,000 deaths in China. Its high prevalence, high mortality, and poor prognosis have led to serious public health problems. Different from the developed nations, the dominant reasons for the high incidence of HCC in China are chronic hepatitis B (CHB) resulting from hepatitis B virus (HBV) persistent infection, and HBV-induced liver cirrhosis (LC) (Chen et al. 2016). Patients with CHB are at high risk for progression to LC and eventually to HCC (Tu et al. 2014). About 70–$90\%$ of HCC patients are associated with cirrhosis (Lavanchy 2004). Early detection of precancerous cirrhosis and HCC can effectively improve the clinical outcome. However, due to the atypical symptom of early HCC, only about $30\%$ of HCC are diagnosed at an early stage (Kudo 2012). The lack of methods for early diagnosis contributes to the urgency to develop novel biomarkers for LC and HCC. The gut microbiome is the largest microbiome living in the human body. Relevant studies have reported that the gut microbiota plays a crucial role in liver disease (Chassaing et al. 2014). Xie et al. found that bile acid dysregulation caused by intestinal flora dysregulation was associated with the progression of liver disease to HCC (Xie et al. 2016). The bacteria and their products in the intestine can be transported to the liver through the gut-liver axis, which further promotes the occurrence of HCC (Dapito et al. 2012; Li et al. 2016; Yu et al. 2010). Many studies have reported that gut microbial markers are used as non-invasive diagnostic tools in type 2 diabetes (Qin et al. 2012), colorectal cancer (Yu et al. 2017), and pancreatic cancer (Ren et al. 2017). These studies provide a strong theoretical basis for intestinal microbes as a non-invasive tool for the early diagnosis of LC and HCC. However, gut microbes are susceptible to the geographical environment, dietary habits, and technical differences. The gut microbial composition in samples collected from different regions was significant different (Rehman et al. 2016; Wilson et al. 2020; Yatsunenko et al. 2012), which lead to inconsistent results across studies. Moreover, there are few reports on the relationship between gut microbial alterations and the progression of HBV-induced liver diseases from CHB to LC and HCC. This study aimed to explore the alterations of gut microbiota during the progression from healthy control (HC) to CHB, LC, and HCC, and develop reproducible gut microbial biomarkers for diagnosis of LC and HCC across Chinese population. A total of 82 stool samples from HBV-induced CHB, LC, HCC, and HC were collected and subjected to 16S rRNA gene sequencing. They were analyzed together with 320 samples (including 121 CHB, 33 LC, 70 HCC and 96 HC) in four public datasets from different regions of China. Compared with HC, reproducible differential genera across datasets were identified in LC and HCC, respectively. Two random forest (RF) classifier models based on these reproducible microbial biomarkers were constructed to distinguish LC or HCC from HC and verified in cross-region datasets. Furthermore, clinical indicators were added into the models to improve the diagnostic efficiency. This study highlighted the potential of the gut microbiota biomarkers as cross-region diagnostic tools for LC and HCC. ## Patient recruitment and stool sample collection The study was approved by Ethics Review from Branch from Research and Clinical Technology Application, Ethics Committee of First Affiliated Hospital of Fujian Medical University (Approval No. MRCTA, ECFAH of FMU [2017]019) and performed according to the Helsinki Declaration. All participant signed informed consent before enrolment. Patients who were diagnosed as chronic HBV liver disease with positive HBV surface antigen test for at least 6 months were recruited from the liver disease center of the first affiliated hospital of Fujian medical university. All participants were excluded from liver diseases caused by other viruses and alcohol, and the interference of other cancers and chronic diseases. In addition, the control group were healthy volunteers or healthy postgraduates of Fujian medical university. Finally, 82 samples, including 21 CHB patients, 25 LC patients, 21 HCC patients and 15 HC, were included and subjected to 16S rDNA gene sequencing. The V3-V4 hypervariable region of the bacterial 16S rDNA gene was amplified from the DNA samples with the barcoded forward primers (5′‐CTTTCCCTACACGAC‐3′) and reverse primers (5′‐ TGGAGTTCAGACGTGT‐3′). More detailed information can be found in our previous work (Zeng et al. 2020).Raw Illumina read data of this study were deposited in The National Genomics Data Center (NGDC) GSA (https://ngdc.cncb.ac.cn/gsa/) with accession number: CRA007561. At the same time, 14 clinical indicators, including gender, age, body mass index (BMI), prothrombin time (PT), platelet count (PC), total bilirubin (TB), total protein (TP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and alpha-fetoprotein (AFP), were collected (Additional file 1: Table S1). ## Public data collection We searched for gut microbial studies from PubMed using the terms “chronic hepatitis B”, “liver cirrhosis” and “hepatocellular carcinoma”. The final inclusion conditions were: [1] patients in China; [2] 16S rRNA gene sequencing; [3] patients with liver diseases caused by HBV infection; [4] stool samples; [5] sequencing data and related sample information are publicly available. Finally, a total of 70 HCC, 33 LC, 121 CHB and 96 HC in four studies from Jilin (Northeast of China), Xiamen (Southeast of China), Nanjing and Shanghai (East of China) were included. Only clinical information in Jilin samples were available, including age, BMI, TP, ALT, AST, GGT, AFP, TB and albumin. Detailed information of datasets used in this study was shown in Table 1.Table 1Description of data used in this studyData sourcesHCCHBLCHCCSequence regionLayoutNo. of readsCityRegion of ChinaCRA00756115212521V3-V4Paired7.4 × 106FuzhouSoutheastSRP194355208835V4Paired6.3 × 106JilinNortheastSRP217171212825–V3-V4Paired9.8 × 106XiamenSoutheastSRP12844233––35V4Single4.8 × 106NanjingEastSRP1038962285––V3-V4Paired2.0 × 106ShanghaiEast ## Unified data processing Raw fastq files were downloaded from the sequence read archive (SRA) database. The quantitative insights into microbial ecology platform 2 (QIIME2) (Caporaso et al. 2010) was used to process all the raw sequencing data in a pipeline to obtain annotation profiles of taxis classification. All sample sequences were preprocessed using the same process as follows. The default parameters of FLASH software were used to splice the pair-ended paired samples, and other parameters were adjusted to –× 0.2; V3-V4 -M 200; V4 -M 150. The sequences with a quality score lower than 25 were filtered and the high-quality sequences were retained. Operational taxonomic unit (OTU) with $97\%$ similarity was obtained by de novo clustering in each individual study. Then, chimera and monomer sequences were removed. The representative sequences of OTU were aligned to the SILVA (Quast et al. 2013) database for bacterial taxis classification, and the abundance profiles of bacterial classification at the phyla and genus levels were extracted for analysis. ## Statistical analysis The Shannon index, Simpson index, Chao1 index and ACE index of alpha diversity were calculated by the “vegan” R package (Oksanen et al. 2020), and the differences between groups were compared by Kruskal–Wallis test. Beta diversity was measured by Bray–Curtis distance and the differences between groups were compared by permutational analysis of variance (PERMANOVA) with 999 randomized permutations. Principal coordinate analysis (PCoA) was used to display the beta diversity and the distribution between datasets. The microbial composition in each disease stage was analyzed at the phylum and genus levels, and the average relative abundance of each microbiota was calculated. Wilcoxon rank-sum test and Kruskal–Wallis rank-sum test was used to identify the microbiota with significant difference in HCC, LC and CHB compared with HC. All clinical indicators were tested by Kruskal–Wallis test except the chi-square test for gender. Spearman rank correlation was used to calculate the relationship between microbial makers and clinical indicators. Statistical significance was defined as $p \leq 0.05.$ ## Model construction A genus that was significantly different between HCC or LC and HC in two datasets and had the same dysregulation trend in the third dataset was defined as reproducible differential genus. RF models based on the reproducible differential genera were constructed to discriminate LC or HCC from HC. Five-fold cross-validation was performed to determine the optimal set of two parameters mtry and ntree, and the out-of-bag error rate was taken as a reference. The receiver operator characteristic (ROC) curve was plotted and the area under the curve (AUC) value were calculated to evaluate the effectiveness of the models. All statistical analyses were performed in R (version 3.6.1) software (https://cran.r-project.org/bin/windows/base/old/3.6.1/) (Dessau and Pipper 2008). ## Clinical characteristics of the patients and healthy individuals As shown in Table 2, except for gender and BMI, other clinical characteristics of the participants in Fuzhou cohort were significantly different among disease states. In addition, age, TP, AST, and AFP were also significantly different between the Jilin and Fuzhou cohort (Additional file 1: Table S2). These results indicated that the HCC diagnostic biomarkers derived from these data ought to be independent of clinical characteristics. Table 2Clinical characteristics of 82 samples collected in this studyDisease state (number of samples)StatisticP valueHC ($$n = 15$$)CHB ($$n = 21$$)LC ($$n = 25$$)HCC ($$n = 21$$)Gender(M/F)$\frac{10}{514}$/$\frac{719}{618}$/32.5750.462Age30.8 ± 6.2140.14 ± 8.6748.4 ± 13.1653.14 ± 10.333.692.30E-07BMI21.55 ± 1.921.33 ± 322.52 ± 2.9522.58 ± 3.143.290.349PC232.13 ± 31.28158.38 ± 52.3117.6 ± 94.38121.76 ± 103.1730.301.19E-06PT11.49 ± 0.7913.41 ± 214.16 ± 3.8215.07 ± 3.5130.521.07E-06TB12.47 ± 3.3957.55 ± 60.0142.24 ± 66.3922.67 ± 11.6424.691.79E-05TP75.26 ± 4.9169.31 ± 6.6565.44 ± 7.8263.5 ± 10.3121.787.24E-05ALT19.13 ± 7.0359.86 ± 29.3953.6 ± 44.1745.86 ± 45.2623.872.65E-05AST21.67 ± 5.2662 ± 28.9470.76 ± 77.3160.33 ± 57.831.177.83E-07AKP60.2 ± 16.5196.62 ± 32.87121 ± 61.13139.57 ± 148.2715.091.74E-03AFP2.19 ± 0.7971.25 ± 135.66957.86 ± 4344.36511.62 ± 888.2816.399.42E-04TG4.25 ± 0.581.25 ± 0.51.11 ± 0.750.96 ± 0.6938.981.75E-08HDL1.49 ± 0.281 ± 0.520.87 ± 0.41.07 ± 0.4219.612.04E-04LDL2.99 ± 0.512.18 ± 0.692.03 ± 0.822.66 ± 1.3116.241.01E-03Continuous variables were expressed as mean ± standard deviation. Chi-square test was used for gender, and Kruskal–Wallis test was used for other clinical variablesNA indicated not applicable ## Microbial diversity differences Firstly, we compared the microbial diversity of samples at various stages of liver disease with HC. The Shannon index, Simpson index, Chao1 index and ACE index of alpha diversity were calculated, respectively. The Kruskal–Wallis test showed that only the Shannon diversity in Fuzhou HCC samples were significantly higher than that in HC, and the Shannon, Chao1 and ACE diversity in Xiamen LC samples were significantly lower than that in HC (Kruskal–Wallis test, $p \leq 0.05$, Fig. 1a and Additional file 1: Tables S3, S4, S5, S6, S7). Notably, in three datasets with multiple disease stages, only the microbial diversity in the Xiamen samples was significantly decreased with disease progression. Fig. 1Microbial diversity differences between different groups. a Alpha diversity measured by the Shannon index, Simpson index, Chao1 index and ACE index. : $p \leq 0.05.$ b PCoA of beta diversity based on Bray–Curtis distance for five datasets Beta diversity was calculated using Bray–Curtis distance, and PCoA analysis showed that the compositions of individual microbial community structure among CHB, LC, HCC and HC were significantly different in Fuzhou, Jilin and Xiamen samples (Fig. 1b). The PERMANOVA results showed that disease stage (LC and HCC) exerted significant influences on the communities (Table 3), while CHB did not. Significant differences of beta diversity between CHB and HC were only observed in Fuzhou and Jilin samples but not in the Xiamen and Shanghai samples. The results indicated that the composition of the microbial community changed greatly in LC and HCC.Table 3PERMANOVA test results of beta diversity based on Bray–Curtis distanceF-statisticR2P-valueDatasetCHB vs HC1.9240.0540.006FuzhouCHB vs HC2.8090.0980.007JilinCHB vs HC1.0640.0220.315XiamenCHB vs HC1.4720.0140.117ShanghaiLC vs HC3.2840.0800.001FuzhouLC vs HC4.3150.1420.001JilinLC vs HC1.9310.0420.011XiamenHCC vs HC2.9810.0810.002FuzhouHCC vs HC2.7640.0500.001JilinHCC vs HC1.2300.0180.220NanjingR2 indicated the percentage of explained variance occupying in the total by group parameters, P-value indicated the significance and bold fonts indicated statistically significant differences between groups Moreover, all samples from five datasets were pooled together for PCoA analysis to evaluate the biological variations and technical differences in different datasets. As shown in Additional file 1: Fig. S1, samples tended to cluster together by different studies rather than by different disease states. These results indicated that the heterogeneity between datasets was greater than the difference between different disease states. Therefore, different datasets were analyzed separately in the subsequent analysis. ## Alterations in microbial composition In order to understand the specific changes of gut microbiota in different disease stages, we firstly analyzed the composition of gut microbiota at the phylum and genus levels. At the phylum level, Firmicutes and Bacteroidetes were the main dominant bacteria in HC, CHB, LC and HCC, followed by Proteobacteria and Actinobacteria (Fig. 2a). The relative abundances of Firmicutes in LC and HCC were significantly decreased compared to that in HC, and significantly decreased as disease progressed, while the relative abundance of Bacteroides was significantly increased (Wilcoxon rank-sum test, $p \leq 0.05$, Fig. 2b, Additional file 1: Fig S2a). Previous studies have shown that the ratio of Bacteroidetes/Firmicutes (B/F) is related to the development of inflammatory diseases, and the increase of the ratio can promote the development of inflammation (Kabeerdoss et al. 2015, Stojanov et al. 2020, Walker et al. 2011). The result indicated that patients with LC and HCC may be accompanied with more inflammatory responses. In addition, the relative abundance of Proteobacteria was also significantly increased in LC and HCC patients, suggesting that a high proportion of Bacteroides/Firmicutes and a high abundance of Proteobacteria may jointly contribute to the progression of HBV-induced liver disease (Fig. 2b).Fig. 2Distribution of the predominant bacteria at the phylum and genus levels in five datasets. a Stacked bars of the microbial composition at the phylum level among HC, CHB, LC and HCC. b Bar chart of the relative abundance of predominant taxa at the phylum levels in LC and HCC compare to HC. Wilcoxon rank sum test was used to compare the difference. : $p \leq 0.05$, : $p \leq 0.01$, : $p \leq 0.001.$ c Stacked bars of the microbial composition at the genus level among HC, CHB, LC and HCC At the genus level, the main bacteria composition were Bacteroides, Faecalibacterium, Prevotella 9, Escherochia/Shigella, Erysipelotrichaceae UCG-003 and Lachnoclostridium (Fig. 2c). Compared with HC, 83, 142 and 60 differential genera were identified in Fuzhou, Jilin and Xiamen in LC samples, respectively (Wilcoxon rank-sum test, all $p \leq 0.05$, Fig. 3a), of which 14 genera were consistently dysregulated in at least two datasets, denoted as reproducible LC-associated microbial markers. Among the 14 genera, three genera (Akkermansia, Barnesiella and Bacteroides) were significantly increased in LC, while 11 genera (Blautia, Fusicatenibacter, Howardella, Lachnospiraceae ND3007 Group, Lachnospiraceae UCG-008, Marvinbryantia, Butyricicoccus, CAG-352, Dialister, Eggerthella, Ruminococcaceae UCG-013) were significantly decreased ($p \leq 0.05$, Fig. 3b). Similarly, 299, 188 and 43 genera with significant differences were identified between HCC and HC samples in Fuzhou, Jilin and Nanjing datasets (Wilcoxon rank-sum test, all $p \leq 0.05$, Fig. 3c), of which 10 genera were consistently dysregulated in at least two datasets, denoted as reproducible HCC-associated microbial markers. Among the 10 differential genera, six genera (Fluviicola, Veillonella, Cryomorphaceae__uncultured, Flavobacteriaceae__uncultured, NS9 Marine group__uncultured bacterium, Spongiibacteraceae BD1-7 clade) were significantly increased in HCC, while four genera (Lachnospiraceae UCG-008, CAG-352, Ruminiclostridium 5, uncultured Erysipelotrichaceae bacterium) were significantly decreased ($p \leq 0.05$, Fig. 3d).Fig. 3The significantly differential genera between LC or HCC and HC across datasets. a–b UpSet plot and bubble plot of the significantly differential genera between LC and HC across datasets. c–d UpSet plot and bubble plot of the significantly differential genera between HCC and HC across datasets. Red and green represented the direction of differential genera, the shape size represented the significant level. NA, not detected genera In addition, the stepwise comparative analysis of CHB vs HC, LC vs CHB and HCC vs LC were also conducted, respectively. Compared with HC, 46, 130, 22 and 11 differential genera were identified in Fuzhou, Jilin, Xiamen and Nanjing CHB samples, respectively (Wilcoxon rank-sum test, all $p \leq 0.05$, Additional file 1: Fig S2b). Among them, Bacteroides was significantly increased in Fuzhou and Jilin datasets, while Phascolarctobacterium, Gordonibacter and DTU089 were significantly decreased in Jilin and Nanjing datasets. Compared with CHB, there were 43, 92 and 48 differential genera in Fuzhou, Jilin and Xiamen LC samples, respectively (Wilcoxon Rank-sum test, all $p \leq 0.05$, Additional file 1: Fig S2c), of which 8 genera were consistently dysregulated in at least two datasets. Among them, Bacteroides was also significantly increased in two datasets, while 7 genera were significantly decreased. Compared with LC, 174 and 216 differential genera were identified in Fuzhou and Jilin HCC samples, respectively (Wilcoxon rank-sum test, all $p \leq 0.05$, Additional file 1: Fig S2d). Only 5 genera (Ruminococcaceae UCG − 014, Akkermansia, Flavobacteriaceae__uncultured, Blautia and Eggerthella) showed a consistent dysregulated direction, of which Ruminococcaceae UCG − 014 and Akkermansia were significantly decreased. ## Construction the diagnostic model for LC on reproducible differential genera The following analysis was performed at the genus level. A RF classification model based on the 14 LC-associated genera was constructed to discriminate LC patients from HC. The Fuzhou samples were used as the training data and five-fold cross-validation was performed on a RF model with optimal parameter combination for mtry = 4 and ntree = 650. The AUC of the RF classifier model was 0.824 ($95\%$ CI 0.697–0.951, Fig. 4a) in Fuzhou samples. Then, the RF model achieved AUCs of 0.919 ($95\%$ CI 0.796–1.00, Fig. 4b) and 0.833 ($95\%$ CI 0.706–0.951, Fig. 4c) in Jilin and Xiamen samples, respectively. Moreover, AST to platelet ratio index (APRI), and FIB-4 are established as biomarkers for LC diagnosis in recent years, which were also applied in Fuzhou dataset with the same thresholds as previous studies (APRI: 1.5, FIB-4: 3.25) (Lurie et al. 2015; Xiao et al. 2015). The AUC values of APRI and FIB-4 for LC diagnosis were 0.72 and 0.51, respectively (Table 4), which were lower than the RF model based on 14 LC-associated genera. Collectively, these 14 LC-associated genera could be used as a potential microbial marker for LC diagnosis. Fig. 4The performances of two RF models based on 14 LC-associated genera or 10 HCC-associated genera. a–c ROC curve of the RF model based on 14 LC-associated genera in Fuzhou, Jilin and Xiamen samples. d The heatmap of the relationships between 14 LC-associate genera and 13 clinical indicators. e–f ROC curve of the RF model based on 10 HCC-associated genera in Fuzhou and Jilin samples. g The heatmap of the relationships between 10 HCC-associated microbial genera and 13 clinical indicatorsTable 4Performance of conventional diagnostic biomarkersBiomarkersFuzhouJilinSensitivitySpecificityAUCSensitivitySpecificityAUCAPRI0.441.000.72NANANAFIB-40.560.470.51NANANAAFP0.521.000.760.771.000.89NA indicated not applicable Correlation analysis* between the above 14 common differential genera and 13 clinical factors in Fuzhou samples were performed. The results showed that 40 genera-factor pairs were significantly correlated, including 18 pairs with significantly positive correlation and 22 pairs with significantly negative correlation (Spearman, all $p \leq 0.05$, Fig. 4d). Among them, age, PT, AST, AKP, HDL and AFP were strongly correlated with the 14 LC-associated genera. In addition, Ruminococcaceae UCG-013 was significant positively correlated with TG, LDL, HDL, TP and PC, and negatively correlated with age, PT, AKP, AFP and TB. Bacteroides was negatively correlated with TG, LDL, HDL and TP, and positively correlated with age, PT, AKP, AST, AFP, TB and ALT. Interestingly, the correlation relationship of Ruminococcaceae UCG-013 and Bacteroides with clinical factors was opposite. Further correlation analysis showed that there was a marginally significant negative correlation between Ruminococcaceae UCG-013 and Bacteroides (Spearman, R = − 0.2, $$p \leq 0.071$$). To enhance the diagnostic efficacy for LC, clinical factors that were significantly correlated with the 14 microbial markers in Fuzhou samples and commonly collected in Jilin samples were selected as candidate features, including age, AST and AFP. Single or multiple clinical factors were added into the 14 LC-associated genera to reconstruct a classification model. The results showed that the classification accuracy of the reconstructed model was greatly improved (Additional file 1: Fig. S3a–f). The similar results were observed in Jilin cohort, which achieved the highest AUC combined age and AST. The results suggest that clinical factors (age, AST and AFP) can greatly improve the discrimination efficiency of the 14 LC-associated genera. ## Construction the diagnostic model for HCC on reproducible differential genera Meanwhile, another RF classification model with optimal parameter combination for mtry = 9 and ntree = 200 by five-fold cross-validation was constructed based on the 10 HCC-associated genera to discriminate HCC from HC. The value of AUC in training Fuzhou samples was 0.902 ($95\%$ CI 0.794–1.00, Fig. 4e). Further, the model was validated in Jilin samples and achieved an AUC of 0.897 ($95\%$ CI 0.805–0.989, Fig. 4f). Validation was not performed in the Nanjing samples because only 4 of the 10 microbial markers were detected. Moreover, AFP is currently the most widely used biomarker for HCC diagnosis (Trevisani et al. 2001). As shown in Table 4, with the cut-off value of 10 ng/mL, the AUC values of AFP in differentiating HCC and HC were 0.76 in Fuzhou dataset and 0.89 in Jilin dataset, respectively, which were lower than the RF model based on 10 HCC-associated genera. These results indicated that the 10 HCC-associated genera could be used as potential microbial markers for HCC diagnosis. These results indicated that the classification efficiency of these 10 genera for HCC was better than the conventional diagnostic biomarker, and could be used as potential microbial markers for HCC diagnosis. Correlation analysis between the above 10 genera and 13 clinical factors showed that 8 genera-clinical factor pairs were significant positively correlated and 12 genera-clinical factor pairs were significant negatively correlated (Spearman, all $p \leq 0.05$, Fig. 4g). Among them, Veillonella was significant positively correlated with age, PT, AST and AKP, and negatively correlated with TP, PC and TG. Ruminiclostridium 5 was negatively correlated with age, PT and AKP, and positively correlated with PC and TG. The correlation between the two genera and clinical factors was opposite. Correlation analysis also demonstrated that the relative abundance of Veillonella was significant negatively correlated with that of Ruminiclostridium 5 (Spearman, R = − 0.33, $$p \leq 0.0022$$). Then single or multiple clinical factors, including age, AST and AFP, were combined with the 10 HCC-associated genera to reconstructed a model. The results showed that the classification accuracy was also greatly improved by the reconstructed model, which ranged from 0.921 to 0.990 (Additional file 1: Fig. S4a–f). The 10 microbial markers combined with AST and AFP achieved the highest AUCs in the two datasets (Additional file 1: Fig. S4f). These results indicated that clinical variables (age, AST and AFP) can greatly improve the ability of microbial markers to distinguish HCC patients. ## Identification the microbial markers for early diagnosis of HCC A multi-stage comparative analysis was performed in the 14 LC-associated genera and the 10 HCC-associated genera. In Fuzhou samples and Jilin samples, eight genera (Ruminococcaceae__CAG-352, Howardella, Lachnospiraceae UCG-008, Akkermansia, Eggerthella, Flavobacteriaceae__uncultured, NS9 Marine group__uncultured bacterium, uncultured Erysipelotrichaceae bacterium) were significantly different among multiple disease stages (Kruskal–Wallis test, $p \leq 0.05$). Among them, Ruminococcaceae__CAG-352 and Lachnospiraceae UCG-008 were shared by the LC-associated genera and the HCC-associated genera. In Fuzhou samples, the relative abundance of Ruminococcaceae__CAG-352 sharply decreased from HC to CHB, LC and HCC, and the relative abundance of Lachnospiraceae UCG-008 gradually decreased with the progression of disease (Fig. 5a). Howardella, Akkermansia and Eggerthella were unique in the LC-associated genera. The relative abundance of Akkermansia increased gradually in the precancerous stage of LC but decreased sharply in HCC, while the relative abundance of Eggerthella decreased gradually with the progression from HC to CHB and LC but increased significantly in HCC (Fig. 5b). Moreover, Flavobacteriaceae__uncultured, NS9 Marine group_uncultured bacterium and uncultured Erysipelotrichaceae bacterium were unique in the HCC-associated genera. The relative abundances of Flavobacteriaceae__uncultured and NS9 Marine group uncultured bacterium were very low in the precancerous samples, but increased sharply in Fuzhou HCC samples. The relative abundance of uncultured Erysipelotrichaceae bacterium was higher in HC, but significantly decreased or even disappeared in CHB, LC and HCC (Fig. 5c). The similar results were also observed in Jilin samples (Fig. 5d–f). These results suggested that the eight genera might play important roles in the progression from LC to HCC, which could be the potential microbial markers for the early diagnosis of HCC. Based on the above eight genera, a random forest classification model with optimal parameter combination for mtry = 6 and ntree = 2000 by five-fold cross-validation was constructed to distinguish HCC from LC by pooling Fuzhou and Jilin samples together. The model achieved an average AUC of 0.899 ($95\%$ CI 0.826–0.972, Fig. 5g), showing a good classification efficiency of HCC and LC.Fig. 5The alterations of microbial markers during disease progression. a Alterations of genera overlapped in the LC-associated genera and the HCC-associated genera in Fuzhou samples. b Alterations of LC-associated genera in Fuzhou samples. c Alterations of HCC-associated genera in Fuzhou samples. d Alterations of genera overlapped in the LC-associated genera and the HCC-associated genera in Jilin samples. e Alterations of LC-associated genera in Jilin samples. f Alterations of HCC-associated genera in Jilin samples. g ROC curve of the RF model based on eight genera for discriminating HCC and LC in the combined dataset of Fuzhou and Jilin samples ## Discussion This study comprehensively evaluated the alterations of gut microbiome in HBV-related liver diseases (including CHB, LC and HCC) across Chinese population, and identified 14 reproducible LC-associated genera and 10 reproducible HCC-associated genera. Two random forest classification models were developed based on these reproducible genera which accurately distinguished LC or HCC from HC and showed good diagnostic efficiencies in cross-region validation datasets. The diagnostic efficacy of the two set of microbial markers was further improved by combining relevant clinical variables (age, AST, AFP). In addition, a reconstructed random forest classification model based on eight genera which were significantly different among multiple disease stages had a good classification efficacy for HCC and LC. The results of this study suggested that gut microbial markers could be used as a promising non-invasive diagnostic tool for LC and HCC. Previous studies have revealed that short chain fatty acid (SCFAs) was lower in stool samples from liver cirrhosis patients, and the abnormality became more obvious with the severity of liver disease, which may be an important factor promoting the development of liver cirrhosis (Jin et al. 2019). Notably, 10 of 14 LC-associated genera, including Blautia, Fusicatenibacter, Howardella, Lachnospiraceae ND3007 Group, Lachnospiraceae UCG-008 and Marvinbryantia in the Lachnospiraceae family, and Butyricicoccus, ruminococcaceae__CAG-352, Dialister, and Ruminococcaceae UCG-013 in the Ruminococcaceae family, which were closely related to the production of SCFAs, were decreased in LC samples. Another two increased genera, Bacteroides and Barnesiella, which belong to the Bacteroidetes family, are important clinical pathogens (Stojanov et al. 2020, Wexler 2007). These results showed that beneficial bacteria were significantly decreased and harmful bacteria was significantly increased in LC patients, which might corporately contribute to the progression of liver disease. Among the 10 reproducible microbial biomarkers identified at HCC, Erysipelotrichaceae has been found to be enriched in HCC (Pinero et al. 2019), Veillonella has been reported to be increased in LC and HCC groups (Tang et al. 2021). Since both geographical location and lifestyle have significant impacts on human gut microbiota, cross-cohort studies provide the possibility to identify reproducible gut microbial biomarkers of cross-population. Several multi-cohort studies have combined metagenomic datasets to assess the diagnostic accuracy of gut microbiota in colorectal cancer across populations (Thomas et al. 2019; Wirbel et al. 2019). Recently, Ren et al. have established an early diagnostic model of HCC on 30 optimal operation taxa and validated in HCC samples from cross-region (Ren et al. 2019). However, they did not perform cross-region validation in LC samples. By integrating multiple cohort studies and using unified data preprocessing pipeline, this study identified reproducible LC-associated genera and HCC-associated genera, and constructed two random forest classification models to accurately distinguish LC or HCC from HC. Further, the altered gut microbiota from non-HCC samples to HCC samples highlighted the possibility of microbial markers to monitor and prevent HCC development. However, the data collected from public database was very limited and the sample size for each disease stage was relatively small, especially the Jilin cohort contained only 8 cirrhosis samples with definite HBV infection. And the information of hepatitis B virus carrier was absence in all the datasets. In addition, due to the relatively low sequencing depth in Shanghai and Nanjing samples, the annotated microbiota and the common differential genera with other cohorts were relatively less. Moreover, this study included samples from Southeast, Northeast and East of China, but lacked samples from western China and central China. Therefore, more clinical data are still needed to validate and optimize the diagnostic models in this study. The stage information was unavailable and the efficacy of early diagnosis for HCC also need to be further evaluated. There are also obvious technical limitations. The data analyzed in this study were 16S rRNA gene sequencing data, which can only be annotated to the genus level. Further investigation of the bacterial species or functional gene families by metagenomic sequencing or integrated with multi-omics data may improve the diagnostic efficacy and help to understand the biological function. In conclusion, this study revealed the alterations of gut microbiota in the progression of liver disease, and identified two list of reproducible microbial biomarkers that have the potential for non-invasive diagnosis for LC and HCC. ## Supplementary Information Additional file 1: Figure S1. PCoA of samples from five datasets based on Bray-Curtis distance. PCoA analysis of samples from five datasets based on Bray–Curtis distance showed the fecal microbiota composition was different among studies ($p \leq 0.01$) and stages ($p \leq 0.01$). Datasets were color-coded and stages (HC, CHB, LC and CRC) were indicated by different shapes. Figure S2. The significantly differential microbe in the development of HCC. ( a) Bubble plots of the significantly differential phyla of CHB vs HC, LC vs CHB and HCC vs LC across datasets. ( b-d) UpSet plot and bubble plot of the significantly differential genera of CHB vs HC, LC vs CHB and HCC vs LC across datasets. Red and blue represented the direction of differential microbe, the shape size represented the significant level. Figure S3. ROC curve of the RF model based on 14 LC-associated genera combined with age, AST and AFP. ( a-c) 14 LC-associated genera combined with age, AST and AFP, respectively. ( d) 14 LC-associated genera combined with age and AST. ( e) 14 LC-associated genera combined with age and AFP. ( f) 14 LC-associated genera combined with AST and AFP. Figure S4. ROC curve of the RF model based on 10 HCC-associated genera combined with age, AST and AFP. ( a-c) 10 HCC-associated genera combined with age, AST and AFP, respectively. ( d) 10 HCC-associated genera combined with age and AST. ( e) 10 HCC-associated combined with age and AFP. ( f) 14 genera combined with AST and AFP. Table S1. The clinical indicators of Fuzhou samples. Table S2. Statistical analysis of clinical characteristics of patients in Jilin and Fuzhou datasets. Table S3. Alpha diversity in Fuzhou samples. Table S4. 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--- title: CCN2/CTGF promotes liver fibrosis through crosstalk with the Slit2/Robo signaling authors: - Liya Pi - Chunbao Sun - Natacha Jn-Simon - Sreenivasulu Basha - Haven Thomas - Victoria Figueroa - Ali Zarrinpar - Qi Cao - Bryon Petersen journal: Journal of Cell Communication and Signaling year: 2022 pmcid: PMC10030765 doi: 10.1007/s12079-022-00713-y license: CC BY 4.0 --- # CCN2/CTGF promotes liver fibrosis through crosstalk with the Slit2/Robo signaling ## Abstract Liver fibrosis is the common outcome of many chronic liver diseases, resulting from altered cell-cell and cell-matrix interactions that promote hepatic stellate cell (HSC) activation and excessive matrix production. This study aimed to investigate functions of cellular communication network factor 2 (CCN2)/Connective tissue growth factor (CTGF), an extracellular signaling modulator of the CYR61/CTGF/Nov (CCN) family, in liver fibrosis. Tamoxifen-inducible conditional knockouts in mice and hepatocyte-specific deletion of this gene in rats were generated using the Cre-lox system. These animals were subjected to peri-central hepatocyte damage caused by carbon tetrachloride. Potential crosstalk of this molecule with a new profibrotic pathway mediated by the Slit2 ligand and Roundabout (Robo) receptors was also examined. We found that Ccn2/Ctgf was highly upregulated in periportal hepatocytes during carbon tetrachloride-induced hepatocyte damage, liver fibrosis and cirrhosis in mice and rats. Overexpression of this molecule was observed in human hepatocellular carcinoma (HCC) that were surrounded with fibrotic cords. Deletion of the Ccn2/Ctgf gene significantly reduced expression of fibrosis-related genes including Slit2, a smooth muscle actin (SMA) and Collagen type I during carbon tetrachloride-induced liver fibrosis in mice and rats. In addition, Ccn2/Ctgf and its truncated mutant carrying the first three domains were able to interact with the 7th -9th epidermal growth factor (EGF) repeats and the C-terminal cysteine knot (CT) motif of Slit2 protein in cultured HSC and fibrotic murine livers. Ectopic expression of Ccn2/Ctgf protein upregulated Slit2, promoted HSC activation, and potentiated fibrotic responses following chronic intoxication by carbon tetrachloride. Moreover, Ccn2/Ctgf and Slit2 synergistically enhanced activation of phosphatidylinositol 3-kinase (PI3K) and AKT in primary HSC, whereas soluble Robo1-Fc chimera protein could inhibit these activities. These observations demonstrate conserved cross-species functions of Ccn2/Ctgf protein in rodent livers. This protein can be induced in hepatocytes and contribute to liver fibrosis. Its novel connection with the Slit2/Robo signaling may have therapeutic implications against fibrosis in chronic liver disease. ### Supplementary information The online version contains supplementary material available at 10.1007/s12079-022-00713-y. ## Introduction Hepatic wound healing as a normal biological process involves inflammation, proliferation, and extracellular matrix (ECM) remodeling after liver injury. This process must be coordinated through cell-cell and cell-matrix interaction to ensure efficient communication of internal and external signaling. However, imbalanced signaling occurs during chronic liver injury. As a result, persistent damage causes profibrotic signaling leading to hepatic stellate cell (HSC) activation and excessive ECM deposition in liver fibrosis. Scar tissues disrupt normal blood flow and sequester hepatocytes in regenerative nodules that eventually result in portal hypertension, cirrhosis, and even liver cancer development. Identification of the disturbed signaling in cell-cell and cell-matrix interactions would facilitate the development of preventive and therapeutic approaches for liver fibrosis and possibly for hepatocellular carcinoma (HCC) (Kisseleva and Brenner 2021). The Slit family of secreted proteins (Slit1, 2, and 3) were originally discovered as neuronal guidance cues that bind to Roundabout receptors (Robo1, 2, 3, and 4) in the immunoglobulin (Ig) superfamily. Activation of the Slit2-Robo1 signaling is critically involved in liver fibrosis by activating HSCs (Chang et al. 2015). Moreover, Slit2 can act through Robo2 leading to modulation of the fibrogenic activity and migration of HSC (Zeng et al. 2018). Activation of PI3K and ERK signaling is involved in initiating HSC activation, whereas blocking the PI3K/AKT signaling can decrease expression of fibrogenesis-related genes in activated HSCs during liver fibrogenesis (Son et al. 2009), (Munoz-Felix et al. 2016). Slit2 has been shown to significantly increase the expression of pro-fibrotic mediators including Cellular communication network protein 2/Connective tissue growth factor (Ccn2/Ctgf) and Collagen through activation of PI3K/AKT pathway (Zeng et al. 2018). Therefore, Slit2 and its receptors promote liver fibrogenesis with a direct effect on HSCs. Ccn2/Ctgf is a matricellular protein (Perbal, Tweedie, and Bruford 2018) and is commonly co-expressed with transforming growth factor (TGF)β in a diverse variety of fibrotic disorders. This protein modulates cell adhesion, migration, differentiation, and apoptosis through binding to growth factors, ECM proteins, and cell surface receptors (Gressner and Gressner 2008). It contains a four modular protein structure and elicits adhesive signaling by acting as a linker between ECM and HSC. Moreover, it can potentiate the profibrogenic action of TGFβ, and control activation of HSCs and ductular reaction during liver damage (Huang and Brigstock 2012; Pi et al. 2008; Pi et al. 2015). Overexpression of CCN2/CTGF has been found in fibrotic diseases involving many organ systems (Gressner and Gressner 2008). Inhibition of CCN2/CTGF using pharmacological to targeted siRNA approaches have been shown to reduce fibrosis in a variety of experimental models (George and Tsutsumi 2007; Brigstock 2009; Li et al. 2008; Li et al. 2006; Lipson et al. 2012). Clinical trials targeting CCN2/CTGF for anti-fibrosis treatment are ongoing (Richeldi et al. 2020; Raghu et al. 2016). In this paper, we conditionally deleted the Ccn2/Ctgf gene in both mice and rats, and asked whether loss of this gene influenced liver fibrosis. We have identified Ccn2/Ctgf as an interactor for multiple angiogenic regulators including Slit ligands (Pi et al. 2012). Herein, we characterized the interaction between Ccn2/Ctgf and Slit2. This interaction was associated with enhanced Slit2/Robo signaling, HSC activation, and liver fibrosis. ## Animal experiments Wild type C57BL6 mice (8–10-week-old) were subjected to a single dose of CCl4 (1 µl/g body weight) prediluted 1:3 in olive oil through intraperitoneal injection (IP) for induction of acute liver injury. In addition, Ccn2/Ctgf conditional mouse knockouts were generated through tamoxifen administration of floxed homozygotes (obtained from Dr. Andrew Leask in College of Dentistry, University of Saskatchewan) carrying the human ubiquitin C promoter driven Cre transgene that was fused to a triple mutant form of the human estrogen receptor (ubc-Cre/ERT2) according to our previous report (Pi et al. 2015). Liver fibrosis was induced through IP injection of CCl4 (0.5 µl/g body weight) twice a week for six weeks the Ccn2/Ctgf conditional mouse knockouts. The hepatocyte-specific Ccn2/Ctgf knockouts (KO) in rats were generated based on Cre-lox system as follows. At first, rat Ccn2/Ctgf floxed homozygotes (designed as Ccn2/Ctgfflox/flox) were obtained from SMOC INC (Shanghai, China) based on fee-for service. To generate the animals, the clusters of regularly interspaced short palindromic repeats (CRISPR) technology were used to insert two loxP cassettes on 5’ and 3’ untranslated region (UTR) of the Ccn2/Ctgf gene (ENSRNOG00000015036). As shown in Supplemental Fig. 1, one loxP site was designed in a target donor construct that contained DNA sequences for a 5’ homologous arm using Cas9 mRNA and two guide RNA (gRNA)s (gRNA1: 5’ GCTGAAGAGGCAGATACCAC GGG 3’; gRNA2: 5’ AGCTGAAGAGGCAGATACCA CGG 3’). The other loxP site was designed in the target donor construct that contained DNA sequences for a 3’ homologous arm using gRNA3 (5’ CAGTGACAGAACGCACACTA AGG 3’) and gRNA4 (5’ GCACACTAAGGTGAGCCTCC TGG 3’). The donor vector was constructed by in-fusion technology, and contained the 5’ homologous arm, a flox region, and the 3’ homologous arm. The mixtures of Cas9 mRNA, gRNAs and donor vector were microinjected into fertilized eggs of Sprague Dawley (SD) rats. The F0 rats were identified by PCR based genotyping and crossed with wildtype SD to generate F1 rats. By long-PCR identification and sequencing, we confirmed there were 7 F1 rats carrying the targeted allele (Ccn2/Ctgfflox). Homozygotes were generated by crossing female and male F1 rats. For genotyping the offspring, short-PCR by P1 and P2 primer pair were used to identify homozygous, heterozygous and wildtype rats (Supplemental Fig. 1). For generation of the hepatocyte-specific knockouts, Ccn2/Ctgfflox/flox rats (10-week-old age) received tail vein injection of recombinant adeno-associated virus serotype 8 (AAV8) virus expressing codon-improved Cre recombinase (iCre) under the control of the human thyroxine-binding globulin (TBG) promoter (AAV8-Cre). The virus was purchased from Vector Biolabs (Malvern, PA) and was given at 2 × 1012 plaque forming unit (pfu) per rat. The same amount of AAV8-green fluorescence protein (GFP) was injected into animals as a control. Three weeks after the viral administration, the AAV8-Cre infected knockouts (KO) and AAV8-GFP-infected controls (CT) were exposed to 6-week CCl4 (1 µl/g body weight) intoxication for liver fibrosis induction as described above. Oil was injected into both AAV8-GFP and AAV-Cre-treated rats in parallel studies. In addition, wild type SD rats (8-week-old age, 200–220 g) received IP injection of chronic CCl4 (1 µl/g body weight) up to 1.5 to 3 months for detection of expression pattern of Ccn2/Ctgf protein in fibrotic or cirrhotic livers. Fig. 1Ccn2/Ctgf is induced during hepatocyte damage, liver fibrosis and cirrhosis of rodent models as well as in human HCC. C57BL6 mice ($$n = 5$$) in (A-D) were subjected to IP injection of one dose of CCl4 (1ml/g body weight). ( A) qRT-PCR analysis detected upregulation of Ccn2/Ctgf transcript at 24 h post CCl4 intoxication from 5 mice. Values are means ± SD from triplicate studies. * $P \leq 0.05.$ ( B) H&E and immunofluorescent staining detected Ccn2/Ctgf localization in periportal zones (PT) using consecutive slides. ( C and D) The immunofluorescent staining labeled Ccn2/Ctgf expression in Hnf4a+ hepatocytes but not CK19+ cholangiocytes. ( E) Dual staining for Ccn2/Ctgf and aSMA in fibrotic mouse livers ($$n = 3$$) that received 6-week CCl4 intoxication (0.5 ml/g body weight, twice/week). ( F) Trichrome and Ccn2/Ctgf staining were performed in liver fibrosis or cirrhosis sections from Sprague Dawley (SD) rats ($$n = 3$$ per group) that were exposed to chronic CCl4 (twice a week, 0.5ml/g body weight) for 6 weeks or 3 months respectively. ( G) Dual staining for Ccn2/Ctgf and aSMA was carried out on SD rats ($$n = 3$$ per group) that received chronic CCl4 for 6 weeks (upper panel) or 90 days (lower panel) respectively. ( H) CCN2/CTGF and Trichrome staining were performed on consecutive sections of human HCC section ($$n = 2$$). Scale bar: 100 mm Mice (8-week-old) that ectopically expressed a rat Ccn2/Ctgf gene with FLAG epitope at C termini were generated through AAV8 delivery systems (AAV8-Ccn2/Ctgf:FLAG). The expression vector pTR-UF12 is a gift from Dr. Alfred S Lewin at University of Florida Gainesville. It contains the 381–base pair (bp) cytomegalovirus enhancer immediate early gene enhancer and the 1352-bp chicken b actin promoter-exon1-intron1 (CBA) promoter (Qi et al. 2007). This plasmid is linked to GFP via a 637-bp poliovirus internal ribosomal entry site. The Ccn2/Ctgf:FLAG DNA fragment was amplified with a primer set 5′ GCTAGCCCTCCTGCCGCGCCCCGACCATGCTCGCC3′ and 5′ GGATCCCGCCATGTCTCCATACATCTTCCTGT3′ and cloned into flag-pGEM-vector according to our previous report (Pi et al. 2005). The resulting cDNA and FLAG sequences were further cloned into SpeI site of the pTR-UF12 vector and expressed under the control of CBA promoter. Recombinant AAV8 virus was packaged via fee for service in the Powell Gene Therapy center at the University of Florida Gainesville. Animals received 5 × 1011 AAV8-Ccn2/Ctgf:FLAG through tail vein injection. AAV8-GFP control was injected in parallel studies. Six-week intoxication by CCl4 (0.5 µl/g body weight) was introduced to induce liver fibrosis in infected mice at three weeks later after the viral administration. All animals in this paper were euthanized to assess the extent of liver fibrosis at two days after the last dose of chronic CCl4 intoxication. Isoflurane overdose followed by cervical dislocation was used for animal euthanasia. Liver tissues were harvested for histological, molecular, and biochemical analyses. All animal protocols were approved by the Animal Care and Usage Committee at Tulane University and were conducted in compliance with their guidelines. ## Histological analysis Human HCC sections were obtained from BioChain (Newark, CA). Mouse and rat livers were fixed in $4\%$ paraformaldehyde. Histological analyses were performed with standard protocols using OCT or paraffin-embedded Sect. ( 6 μm). Primary antibodies used were rabbit anti-hepatocyte nuclear factor (Hnf)4a (Santa Cruz biotechnologies, Dallas, TX, USA), rabbit anti-Ccn2/Ctgf (Abcam, Cambridge, United Kingdom), and rabbit anti-cytokeratin (CK)19 (Abcam). Detection was carried out according to the manufacturer’s instructions using the ABC-Elite kit with ImmPACT DAB substrate (Vector Laboratories, Burlingame, CA, USA). Collagen deposition was measured by Sirius Red staining or Trichrome blue according to previous publication(Pi et al. 2015). Alexa Fluor® 596 conjugated a smooth muscle actin (SMA) antibody (Abcam) was also used for double staining with the Ccn2/Ctgf rabbit antibody (Abcam). For estimation of stained positive areas, images were captured with CellSens software using an Olympus BX 51 upright fluorescence microscope outfitted with an Olympus DP80 camera, Plan Fluorite objectives and a LED transmitted light source (Olympus, Waltham, MA, USA). DAB stained areas were quantified from 10 random fields of images using Image J software (http://rsb.info.nih.gov/ij/) and IHC profiler according to published methods (Varghese et al. 2014). ## Cell culture, immunofluorescent staining, and fluorescent microscopy Primary hepatocytes were isolated from Ccn2/Ctgff/f rats and cultured in William’s E media according to our previous publication (Pi et al. 2008). In brief, rats were IP injected with chronic CCl4 (0.5 ml/g body weight, twice a week for 6 weeks). Two days after the last injection, livers were perfused by the two-step Collagenase digestion method (Pi et al. 2015). Ca2+ free Hanks buffer containing EGTA (34 mg/ml, Sigma-Aldrich, St. Louis, MO, USA) and $0.5\%$ BSA were used in the first step of perfusion. Then enzymatic digestion was carried out in Hanks buffer containing $0.05\%$ (w/v) Collagenase II (Worthington Biochemical Corporation, Lakewood, NJ, USA) and CaCl2 (58.8 mg/ml) via recirculation. The digested livers were further disassociated in the Hanks buffer. Hepatocytes were isolated after centrifuge at 50 x and $45\%$ Percoll followed by 200 x g centrifuge. The cell pellet was re-suspended in complete William’s E medium (Thermo Fisher Scientific, Waltham, MA, USA) containing L-glutamine (2mM), $5\%$ fetal bovine serum (FBS) (Invitrogen, Waltham, MA, USA), insulin (100 nM), dexamethasone (100 nM), penicillin (100U/ml) and streptomycin (100 mg/ml). Purity of the isolated hepatocytes was about $85\%$ according to staining of the hepatocyte marker Albumin using a rabbit antibody (Invitrogen) as shown in Supplemental Fig. 2. The isolated cells were seeded in Collagen coated plates with complete Williams’ E media at 37oC with $5\%$ CO2. Four hours later, new complete Williams’ E media were added. The cultured Ccn2/Ctgff/f hepatocytes were transduced with AAV8-Cre at a multiplicity of infection (MOI) of 100,000 viral genomes/cell to delete the Ccn2/Ctgf gene in vitro. The same amounts of AAV8-GFP were transduced in parallel wells as control groups. GFP visualization indicated nearly $100\%$ transduction rate (Supplemental Fig. 3). Three days later, the infected cells were used for immunostaining or Western analysis. Double staining was performed using the rabbit anti-Albumin and a mouse anti-Ccn2/Ctgf antibody (Santa Cruz Biotechnologies) followed by detection with Alexa Fluor® 488 or 596 conjugated Donkey secondary antibodies. Primary HSCs were isolated from wildtype mice according to our previous publication(Pi et al. 2015). They were maintained in Dulbecco’s Modified Eagle’s Medium (Invitrogen) supplemented with $10\%$ FBS and antibiotics at 37 °C in a $5\%$ CO2 humidified atmosphere. Double staining was performed using a rabbit anti-Slit2 (Proteintech, Rosemont, IL, USA) and the mouse anti-Ccn2/Ctgf antibody (Santa Cruz Biotechnologies). Recombinant mouse Slit2 protein (R&D Biosystems, Minneapolis, MN, USA), recombinant mouse Ccn2/Ctgf protein (Abcam), or human ROBO1-Fc chimera protein (R&D Biosystems), phosphate buffered saline (PBS) vehicle, or in combination were added to primary mouse HSC cells that were pre-starved for 16 h. 30 min after the stimulation, the cells were lysed for total protein extraction followed by Western blotting for the serine/threonine kinase AKT, p-AKT, phosphatidylinositol 3-kinase (PI3K), and p-PI3K. Levels of aSMA and actin were examined after 24-hour stimulation by Western blotting. ## RNA isolation and reverse transcriptase-polymerase chain reaction (RT-PCR) Total RNA isolation, RT-PCR, and quantitative RT-PCR (qRT-PCR) analyses were performed in the same conditions according to our previous publication (Zhou et al. 2020). Primers included: 5’ TGGCGAGATCATGAAAAAGAA 3’ (forward) and 5’ GCCTTTGTCATCGTCATCCT 3’ (reverse) for for rat Ccn2/Ctgf:FLAG; 5’ ATGCTCGCCTCCGTCGCGGGT 3’ (forward) and 5’ GCCTTGTCTCCATACATCTTCCTG 3’ (reverse) for the full-length rat Ccn2/Ctgf; 5’ GGCAGACACTGTCCCTATCG 3’ (forward) and 5’ ATCTATCTTCGTGATCCTCGTGA 3’ (reverse) for mouse Slit2; 5’ GTGCGTCTGGTGTGAATGAG 3’ (forward) and 5’ ATGCTCCTGGTGCAACTGTG 3’ (reverse) for rat Slit2; 5’ GTGCAAGCTGAACAACAGGA 3’ (forward) and 5’ CCAGCATCCACATTCTCCTT 3’ (reverse) for iCre; 5’ TTACTTGTACAGCTCGTCCATG 3’ (forward) and 5’ GCCACCATGGTGAGCAAGGG 3’ (reverse) for GFP. Other primers were previously reported (Pi et al. 2015), (Zhou et al. 2020). All qRT-PCR experiments were performed in triplicate using cDNA samples from independent RNA sets and the relative amount of target mRNA was calculated using the delta‐delta CT method according to published reports (Livak and Schmittgen 2001) and was normalized against reference gene (18 S) in each sample. ## Yeast two-hybrid analyses The cDNAs of rat Ccn2/Ctgf (Genbank accession# NM_022266), and its mutants were cloned into NotI and SalI sites in pPC97 vector containing DNA binding domain (BD) of the GAL4 gene as described in our previous report (Pi et al. 2012). The cDNA fragments for human SLIT2 (GenBank#: AF133270.1) were cloned into NotI and SalI sites in pPC86 vector containing GAL4 Activation domain (AD). Primers for the BD fused C terminal fragment of SLIT2 (BD:SLIT2C) were 5’ GAGGTCGACCGACTTCCAGAAGGTG 3’ (P1) and 5’ TAGCGGCCGC AGGACACACACCTCG 3’ (P2). The BD fused C terminal cysteine knot of SLIT2 (BD:SLIT2-CT) were amplified using 5’ GAGGTCGACCAGGCTTTCAGGTCTG 3’ and P2 primer. The BD fused SLIT2 carrying the epidermal growth factor (EGF) repeats 7–9 (BD:SLIT2-EGF) were amplified using P1 primer and 5’ TAGCGGCCGCTGCTTTTGGTAATAAT 3’. Yeast two-hybrid analyses were performed according to our previous publications (Pi et al. 2008; Pi et al. 2012). BD constructs contained tryptophan gene and AD constructs expressed leucine gene. Therefore, the co-transformants that carried out BD and AD constructs could grow in the Synthetic Complete (SC) agar media lacking tryptophan and leucine (Clontech Laboratories Inc, Mountain View, CA, USA). HIS3 reporter gene was designed in the yeast two-hybrid system to detect interaction between proteins that were fused to GAL4 BD or AD in vectors. 3-aminotriazole (3-AT) (Sigma) was added in SC media to eliminate the transcriptional background mediated by HIS3 reporter gene. Only interactors could grow in SC agar media containing 3-AT but lacking tryptophan, leucine, and histidine. ## Protein expression, purification, and immunoprecipitation The full-length human SLIT2 cDNA (Genbank accession# BC117190) was obtained from Open Biosystems (Huntsville, AL, USA) and cloned to fuse with the MYC epitope at its 3’ end in pSEC-B vector at restriction enzyme sites KpnI and XhoI. The resulting SLIT2:MYC construct, together with the 3xFLAG tagged Ccn2/Ctgf or mutants, was transfected into Chinese hamster ovary (CHO) cells using lipofectamine 3000 (Invitrogen). Immunoprecipitation assays were performed at two days after cell transfection. Conditioned media from the transfected cells were adjusted to contain 1× Tris-buffered saline (TBS; 20 mM Tris-HCl, pH 7.4, and 150 mM NaCl) and proteinase inhibitor cocktail (Sigma-Aldrich). For co-immunoprecipitation assays in AAV8-Ccn2/Ctgf:FLAG- or AAV8-GFP-infected murine livers, total protein homogenates were also extracted using 1x TBS buffer supplemented with $0.5\%$ Triton-100 and proteinase inhibitor cocktail. The conditioned media, or cell lysates were incubated with M2 antibody-conjugated agarose (Sigma-Aldrich) for 16 h. After extensive wash with TBS buffer, the bound proteins were eluted with 100 mM glycine (pH 2.5), separated in $6\%$ SDS-PAGE gel, and immunoblotted with the rabbit Slit2 antibody (Proteintech). For preparation of maltose-binding protein (MBP) fusion proteins, the C terminal regions of SLIT2 (SLIT2C) tested were removed from the pPC86 yeast two-hybrid construct and inserted at SalI and NotI sites before the 3′ of MBP in a modified pMAL vector described previously (Pi et al. 2012). Fusion proteins were induced in Escherichia coli (E. coli) DE3 strain with 0.4 mM isopropyl-β-d-thiogalactopyranoside, purified using amylose beads (New England Biolabs, Ipswich, MA, USA) with column buffer containing 20 mM Tris-HCl (pH 7.4), 200 mM NaCl, and 1 mM EDTA, and eluted with 10 mM maltose. ## Solid-phase protein-binding assays Solid-phase protein-binding assays were carried out as follows. Microplates were coated with purified MBP or MBP-SLIT2C proteins with concentrations about 10, 50, 100, or 150 nM at 4 oC overnight. Then the wells were washed with TBS-T buffer (TBS with $0.05\%$ Tween 20) followed by blocking in TBS-T containing $1\%$ BSA for two hours at room temperature. The wells were incubated with purified recombinant rat Ccn2/Ctgf:3xFLAG protein (1 mg/ml) for 2 h at room temperature. Unbound protein was removed by extensive washing with TBS-T. M2 antibody-conjugated HRP (Sigma) was prepared at 1:20,000 dilution and incubated with the wells for 1 h at room temperature. Tetramethylbenzidine was added as the substrate (R&D Systems), and the reaction was stop by 1 M H2SO4. The absorbance at 450 nm of each well was measured with an EnVision 2104 multimode microplate reader (PerkinElmer, Hopkinton, MA, USA). ## Western blotting Total proteins were extracted from mouse livers or cultured cells in RIPA buffer containing proteinase inhibitors (Sigma). Total protein lysates (50 µg) were boiled in 1x Laemmli buffer containing $5\%$ β-mercaptoethanol, separated in SDS-PAGE gel, and electro-transferred onto polyvinylidene difluoride membrane for immunoblotting. Primary antibodies included rabbit anti-Ccn2/Ctgf (Abcam), rabbit anti-Slit2 (Proteintech), rabbit anti-aSMA (Proteintech), rabbit anti-Collagen (Proteintech), rabbit anti-p-AKT (Cell Signaling Technology, Danvers, MA, USA), rabbit anti-AKT (Cell signaling), rabbit anti-p-PI3K (Cell signaling), rabbit anti-PI3K (Cell signaling), and rabbit anti-GAPDH (Abcam). Detection was carried out using horseradish peroxidase-conjugated secondary antibodies (Santa Cruz biotechnologies) and the ECL Plus kit (Amersham Biosciences, Piscataway, NJ, USA). ## Statistical analysis GraphPad Prism 6.0 (GraphPad Software, San Diego, CA, USA) was used for statistical analysis. Statistical significance ($P \leq 0.05$) was evaluated using the unpaired t-test and one-way analysis of variance (ANOVA). ## Upregulation of Ccn2/Ctgf gene during CCl4-induced hepatocyte damage, liver fibrosis, cirrhosis, and human HCC CCl4 is a potent hepatotoxin known to cause oxidative stress and pericentral damage leading to steatohepatitis, fibrosis, cirrhosis, and liver cancer in experimental models. We utilized this chemical and observed upregulation of Ccn2/Ctgf transcript within 24 h post one acute dose of CCl4 in mice (Fig. 1A). This observation was consistent with our previous publication about upregulation of Ccn2/Ctgf during CCl4-induced acute damage in absence or presence of moderate ethanol pre-exposure (Zhou et al. 2020). Hematoxylin and eosin (H&E) and immunofluorescent staining for Ccn2/Ctgf on consecutive sections indicated that this protein was localized in periportal zones of the CCl4-treated murine livers (Fig. 1B), although there was little staining of this molecule in untreated livers at day 0 (Supplemental Fig. 4). Additional immunofluorescent staining for the biliary specific marker CK19 confirmed that Ccn2/Ctgf immunoreactive cells were located within periportal regions but not in cholangiocytes (Fig. 1C). Moreover, these Ccn2/Ctgf highly expressed cells were stained positive for the hepatocyte marker Hnf4a (Fig. 1D). We also stained fibrotic murine livers that were damaged through 6-week chronic CCl4 intoxication. Dual staining showed that these Hnf4a+ hepatocytes were closely associated with activated aSMA+ HSC (Fig. 1E). This observation agrees with our previous findings about lack of Ccn2/Ctgf promoter activity in aSMA+ or desmin+ myofibroblast cells during liver injury after CCl4 intoxication in mice (Pi et al. 2015). In rats, abundant Ccn2/Ctgf protein was also found in cells proximal to macrovesicular regions, fibrotic zones, and cirrhotic nodules after chronic CCl4 administration as shown in Fig. 1 F. Dual staining detected close association of these ballooning hepatocytes with activated aSMA+ HSC (Fig. 1G and Supplemental Fig. 5), implicating paracrine effects of hepatocyte-derived Ccn2/Ctgf on HSC during liver fibrosis. CCN2/CTGF overexpression was also found in HCC tumor cells that were surrounded by heavily fibrotic stroma (Fig. 1H). Taken together, these results indicated cross-species upregulation of Ccn2/Ctgf gene during hepatocyte damage, liver fibrosis, cirrhosis, and HCC development. ## Systemic deletion of Ccn2/Ctgf gene reduces myofibroblast activation and liver fibrosis in adult mice after chronic CCl4 intoxication To determine the importance of Ccn2/Ctgf gene in CCl4-induced liver fibrosis, we deleted exon 4 utilizing mice that were homozygous for the floxed-Ccn2/Ctgf allele and hemizygous for the ubc-Cre/Ert2 transgene according to our previous report (Pi et al. 2015). For simplicity, the conditional knockouts were termed as Ccn2/Ctgfk/k in this study. Exon 4 deletion was generated after tamoxifen administration at one month before CCl4 administration. We compared the fibrogenic response in Ccn2/Ctgfk/k knockouts with controls that were floxed homozygotes (Ccn2/Ctgff/f) after a 6-week CCl4 administration. As shown in Fig. 2A, the CCl4-treated conditional knockout mice had deletion of the Ccn2/Ctgf gene and showed downregulation of Slit2, aSMA and Collagen type I transcripts as detected by qRT-PCR and Western blotting analyses (Fig. 2A and B). IHC revealed much smaller areas of fibrotic lesions detected in aSMA staining as well as Sirius red staining compared with Ccn2/Ctgff/f controls (Fig. 2C). In contrast, Ccn2/Ctgfk/k and Ccn2/Ctgff/f did not exhibit any fibrotic responses in oil-treated conditions (Fig. 2A-C). These results indicated that Ccn2/Ctgf deficiency reduced CCl4-induced liver fibrosis in mice. Fig. 2Ccn2/Ctgf conditional knockouts exhibited reduced myofibroblast activation and liver fibrosis as evidenced by low production of Slit2, aSMA and Collagen type I at mRNA and protein levels in adult mice after chronic CCl4 intoxication. The conditional knockout mice (Ccn2/Ctgfk/k, $$n = 3$$) and floxed littermates (Ccn2/Ctgff/f, $$n = 3$$) were subjected to chronic intoxication by IP injection of CCl4 (0.5ul/ug body weight, twice a week) for 6 weeks. Olive oil was injected in additional experiments ($$n = 3$$ per group). ( A) Downregulated transcripts of Slit2, aSMA and Collagen type I genes were found in the CCl4-treated Ccn2/Ctgfk/k livers by qRT-PCR analysis. In contrast, no difference was found compared to additional oil-treated groups. Values represent means ± SD in relation to oil treated Ccn2/Ctgff/f controls from three independent experiments. * $P \leq 0.05.$ ( B) Lower levels of these gene products were also detected compared to controls in Western analysis. Graphs are from densitometric analyses based on band intensity. Data are expressed as means ± SD in relation to Ccn2/Ctgff/f controls in corresponding CCl4 and oil treated Ccn2/Ctgfk/k groups from three different studies. * $P \leq 0.05.$ ( C) Reduced levels of aSMA and Collagen were found in IHC and Sirius Red staining of the CCl4-treated Ccn2/Ctgfk/k livers in comparison to the CCl4-treated Ccn2/Ctgff/f controls. Values were means ± SEM based on quantification of images from more than 10 fields per mouse ($$n = 3$$ mice per group). * $P \leq 0.05.$ Scale bar: 500 mm in (C) ## The hepatocyte-specific deletion of Ccn2/Ctgfgene deceases myofibroblast cell activation and Collagen deposition in rats after chronic CCl4 intoxication To understand whether Ccn2/Ctgf has conserved functions in other species, we utilized the CRISPR technology and generated a novel line in which loxP sites were inserted before 5’ UTR or after 3’ UTR in the rat Ccn2/Ctgf gene (Supplemental Fig. 1A and B). Homozygotes for the floxed-Ccn2/Ctgf transgene were identified based on PCR-based genotyping (Supplemental Fig. 1C). Homozygous littermates received tail vein injection of AAV8-Cre or control AAV8-GFP virus followed by 6-week chronic exposure to CCl4. As shown in Fig. 3A, presence of iCre induced Ccn2/Ctgf deletion as evidenced by very low levels of the full-length Ccn2/Ctgf transcript in the AAV8-Cre infected KO livers in comparison to AAV8-GFP infected CT. Isolated primary hepatocytes exposed to AAV8-Cre were positive for albumin staining but had little immunoreactivity to Ccn2/Ctgf antibody (Fig. 3B). The loss of Ccn2/Ctgf in the KO hepatocytes was also verified in immunoblotting (Fig. 3C). This hepatocyte-specific loss of Ccn2/Ctgf affected liver fibrosis since there were significant decreases of Slit2, aSMA and Collagen type I genes at mRNA levels (Fig. 3A). Immunoblotting confirmed decreased levels of these fibrosis-related gene products in total liver homogenates of the CCl4-damaged KO (Fig. 3D). Decreased areas in aSMA staining and Trichrome staining were also observed in the KO (Fig. 3E). Determination of hepatic hydroxyproline content further demonstrated decreased deposition of Collagen in the damaged KO livers than controls (Fig. 3F). Additional oil-treatment in KO and CT mice did not cause any fibrotic responses (Fig. 3D-F). Taken together, these results indicated that loss of hepatocyte derived Ccn2/Ctgf in rat livers decreased fibrotic responses during injury following chronic CCl4 intoxication. Fig. 3The hepatocyte-specific deletion of Ccn2/Ctgf gene is associated with low production of Slit2, aSMA, and Collagen type I genes during CCl4-induced liver fibrosis in rats. The hepatocyte-specific rat knockouts (KO, $$n = 4$$) were generated after tail vein injection of AAV8-iCre or AAV8-GFP as control (CT). These AAV8 infected livers received CCl4 intoxication for six weeks. Oil was also injected in parallel experiments. ( A)The presence of iCre, decrease of the full-length Ccn2/Ctgf, and downregulation of Slit2, aSMA and Collagen type I genes were found in the KO livers by semi qRT-PCR analyses after chronic CCl4 intoxication. Graphs show relative changes that were expressed as means ± SD in relation to CT groups in corresponding CCl4 and oil-treated conditions ($$n = 3$$–4 per group). * $P \leq 0.05.$ ( B and C) Loss of Ccn2/Ctgf protein in albumin+ primary KO hepatocytes were confirmed by double staining (B) and Western blotting from total protein extracts of two different wells of the cultured primary KO hepatocytes (C). ( D) Lower levels of aSMA and Collagen type I proteins were found in the KO rat livers than CT controls in Western blotting. Graphs indicate relative changes that were quantified in densiometric analyses. Values represent means ± SD in relation to CT groups in corresponding CCl4 and oil-treated conditions ($$n = 3$$–4 per group). * $P \leq 0.05.$ ( E) IHC and Sirius red staining detected lower levels of aSMA and Collagen in KO compared to CT group ($$n = 4$$ per group) that received chronic CCl4. Values were means ± SEM based on quantification of images from more than 10 fields per animal. * $P \leq 0.05.$ ( F) Decreased Collagen deposition was observed in the rat KO ($$n = 4$$) after measurement of hepatic hydroxyproline contents. Values were calculated as mg/g wet tested livers and represented means ± SD ($$n = 3$$–4 per group). * $P \leq 0.05.$ Scale bar: 30 mm in (B) and 500 mm in (E) ## Ccn2/Ctgf binds to Slit2 and potentiates Slit2/ROBO1 signaling in HSC in vitro Ccn/Ctgf protein contains a four-modular protein structure (Fig. 4A). Domain I is an insulin like growth factor-binding protein (IGFBP)1. Domain II is a von Willebrand type C (vWC) module. Domain III is a thrombospondin type-1 repeat (TSP1). Domain IV is a C terminal cysteine knot (CT) motif. Our previous studies have identified Ccn2/Ctgf as a binding protein to multiple angiogenic regulators including Slit3 after screening a yeast two-hybrid library that was generated from rat livers with oval cell activation induced by partial hepatectomy and 2-acetylaminofluorene (2-AAF) (Pi et al. 2012; Pi et al. 2008). The first three domains of Ccn2/Ctgf protein exhibit a broad binding ability and are required for the interactions with Slit3 (Pi et al. 2012). To verify Ccn2/Ctgf interaction with Slit2, we performed additional yeast two-hybrid analyses. Ccn2/Ctgf and its mutants were fused to GAL4-BD constructs that contained tryptophan gene. The human cDNAs of SLIT2-EGF7 − 9, SLIT2-CT, or both were fused to GAL4-AD vectors that expressed leucine gene. All co-transformants that carried BD and AD constructs could grow in the SD agar media lacking tryptophan and leucine (Fig. 4A, right panel). HIS3 gene was designed in the yeast two-hybrid system to detect any interaction that could bright the GAL4 BD and AD fusion proteins in proximity leading to transcriptional activation of the reporter. We identified interactors that grew in the selection SD agar media containing 3-AT but lacking tryptophan, leucine, and histidine (Fig. 4A, left panel). Using this approach, we confirmed interaction of Ccn2/Ctgf with SLIT2-EGF7 − 9 and SLIT2-CT via the first three domain of this protein. To further understand this interaction, we purified MBP fused SLIT2C from E. coli strain BL2 (DE3) and found a dose-dependent binding of Ccn2/Ctgf to MBP:SLIT2C in solid-phase assays (Fig. 4B and C). We also co-expressed SLIT2:MYC with Ccn2/Ctgf or its truncated mutant in CHO cells (Fig. 4D). SLIT2:MYC protein was specifically detected in complexes that were immunoprecipitated with M2 antibody and 3xFLAG tagged Ccn2/Ctgf and Ccn2/CtgfI,II,III whereas it showed little binding to the first two domains of Ccn2/Ctgf (Fig. 4E). The TSP1 alone was not sufficient for the interaction either (data not shown), implicating that the first three domains of Ccn2/Ctgf were required for their binding to SLIT2. Nevertheless, co-localization of murine Slit2 and Ccn2/Ctgf protein was observed in isolated primary HSC that were cultured for a week (Fig. 4F). Exposure of the primary HSC to recombinant murine Slit2 at 200 ng/mL concentration induced PI3K and AKT phosphorylation as well as aSMA upregulation (Fig. 4G). Recombinant murine Ccn2/Ctgf (200 ng/ml) induced PI3K and AKT phosphorylation despite of its little effect on aSMA upregulation (Fig. 4G), indicating that Ccn2/Ctgf alone might induce transient activation of PI3K/AKT signaling that was not strong enough to activate fibrogenic action. Interestingly, this induction of PI3K and AKT phosphorylation as well as aSMA upregulation could be enhanced in presence of the recombinant murine Ccn2/Ctgf and Slit2. In contrast, addition of soluble ROBO1-Fc chimera protein (500 ng/ml), which carries two IgG domains responsible for Slit2 binding and sequestration (Liu et al. 2004), blocked these effects mediated by Slit2 and Ccn2/Ctgf. Fig. 4Ccn2/Ctgf binds to Slit2 and potentiates HSC activation in vitro. ( A) Ccn2/Ctgf interacted with SLIT2-EGF7 − 9, SLIT2-CT and SLIT2C in yeast two-hybrid analyses. LRR: leucine rich repeat domain; G: laminin G-like module; CT: C-terminal cystein knot; AD: activation domain; BD: binding domain; Trp: tryptophan; Leu: leucine; His: histidine. ( B) Purified MBP and MBP fused SLIT2C were resolved using SDS-PAGE gel electrophoresis and stained with Coomassie Brilliant Blue. ( C) Dose-dependent bindings of Ccn2/Ctgf to MBP:SLIT2C in solid-phase assays. Data are presented as means ± SD in triplicate wells. * $P \leq 0.05$; *$P \leq 0.01.$ ( D) SLIT2:MYC, Ccn2/Ctgf, and truncated mutants were expressed in CHO cells. ( E) SLIT2:MYC protein was specifically detected in complexes that were immunoprecipitated with M2 antibody (Ab) and 3xFLAG tagged Ccn2/Ctgf and Ccn2/CtgfI,II,III. MYC Ab was used in immunoblotting. Equal input of SLIT2:MYC protein was added in the immunoprecipitation assays. ( F) Co-localization of Slit2 and Ccn2/Ctgf proteins in primary mouse HSCs. Arrows in images and inserts indicate the same locations. Scale bar: 20 mm. ( G) Western blot analyses showed that recombinant Ccn2/Ctgf protein potentiated Slit2-stimulated phosphorylation of PI3K and AKT as well as aSMA upregulation in cultured mouse HSCs, whereas presence of ROBO1-Fc chimera* protein inhibited these actions mediated by Ccn2/Ctgf and Slit2. Values were means ± SD based on densitometric quantification of band intensities related to Mock controls in three independent experiments. * $P \leq 0.05$ ## Ectopic Ccn2/Ctgf binds to Slit2 and potentiates murine liver fibrosis To further understand the in vivo function of Ccn2/Ctgf, we used the AAV8 delivery system and ectopically expressed this gene in mouse livers as shown in RT-PCR analysis (Fig. 5A). The recombinant AAV8 virus contained Ccn2/Ctgf:FLAG and IRES-GFP sequences. GFP alone in AAV8 viruses was also expressed in parallel studies. The infected mice were exposed to CCl4 for 6 weeks. The qRT-PCR analysis showed higher levels of Ccn2/Ctgf, Slit2, aSMA, and Collagen in the AAV8-Ccn2/Ctgf:FLAG infected livers than AAV8-GFP controls after the 6-week exposure to CCl4 (Fig. 5B). Accordingly, elevated levels of Ccn2/Ctgf, Slit2, aSMA, and Collagen type I proteins were detected in immunoblotting and quantified in densitometry analyses (Fig. 5C). IHC confirmed larger areas of aSMA+ myofibroblast cells and Collagen deposition (Fig. 5D-F). Enhanced deposition of Collagen was also detected as indicated by increased hydroxyproline content in the fibrotic livers with ectopically expressed Ccn2/Ctgf (Fig. 5G). Furthermore, the murine Slit2 and Ccn2/Ctgf:FLAG proteins were co-immunoprecipitated using the M2 mouse antibody against FLAG epitope in the AAV8- Ccn2/Ctgf:FLAG infected livers (Fig. 5H). Collectively, these observations indicated that Ccn2/Ctgf formed complexes with Slit2 and its ectopic expression promoted liver fibrosis. Fig. 5Ccn2/Ctgf binds to Slit2 and potentiates HSC activation and liver fibrosis in vivo. AAV8-Ccn2/Ctgf:FLAG or GFP control was delivered into mice ($$n = 4$$ per group). Three weeks later, the mice received 6-week intoxication with CCl4. ( A) Semi qRT-PCR analysis detected Ccn2/Ctgf:FLAG and co-expressed GFP in infected livers. ( B and C) Ectopic expression of Ccn2/Ctgf caused upregulation of Slit2, aSMA, and Collagen type I genes as detected by qRT-PCR (B) and Western analysis (C). The band intensity in (C) was quantified by densitometry in graphs. Data in (B and C) represent means ± SD in relation to AAV8-GFP infected controls ($$n = 4$$). * $P \leq 0.05$; **$P \leq 0.001.$ ( D) IHC for aSMA and Sirius red staining. ( E and F) Quantification of aSMA+ areas and Sirius red stained areas was performed. Values were means ± SEM based on quantification of images from more than 10 fields per mouse ($$n = 4$$ mice per group). $P \leq 0.05.$ ( G) Measurement of hepatic hydroxyproline showed increased Collagen deposition in fibrotic livers that ectopically expressed Ccn2/Ctgf:FLAG. Data are means ± SD ($$n = 4$$ mice per group). * $P \leq 0.05.$ ( H) Slit2 and Ccn2/Ctgf:FLAG were pulled down together in total protein lysates isolated from the AAV8-Ccn2/Ctgf:FLAG- or AAV8-GFP-infected livers (upper panel). The M2 antibody conjugated agarose was used for immunoprecipitation and a rabbit Slit2 antibody for immunoblotting. Equal input for cell lysates containing Slit2 protein in the assays were used (lower panel) ## Discussion Ccn2/Ctgf is a matricellular protein regulating biological processes ranging from cell proliferation, angiogenesis, wound healing, tumor development, to organ fibrosis. Its germline deletion causes prenatal lethality and multiple skeletal defects in mice (Ivkovic et al. 2003). Conditional deletion of this gene can reduce wound healing responses and tissue fibrosis in multiple organs including lung, cornea, and liver (Gibson et al. 2014; Liu et al. 2011; Pi et al. 2015). Its genetic modulation in muscle is not sufficient to drive fibrosis, but alters Collagen content and organization after injury (Petrosino, Leask, and Accornero 2019). Overexpression of Ccn2/Ctgf protein has been reported in concert with signaling pathways associated with fibrosing injuries for initiation and exacerbation of fibrosis in liver and kidney (Tong et al. 2009; Yokoi et al. 2008). A high level of Ccn2/Ctgf protein renders the livers more susceptible to the injurious actions of other fibrotic stimuli (Tong et al. 2009). In line with central roles of Ccn2/Ctgf in liver fibrosis, we have observed that deletion of exon 4 in this gene in a tamoxifen-inducible manners in floxed mice that carry ubc-Cre/ERT2 transgene decreases ductular reaction and biliary fibrosis after the feeding of a biliary toxin 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) (Pi et al. 2015). Consistent with these previous findings, this study showed that the conditional Ccn2/Ctgf mouse knockouts exhibited reduction in CCl4-induced liver fibrosis. Loss of hepatocyte-derived Ccn2/Ctgf in rats through AAV8-Cre also attenuated liver fibrosis after chronic intoxication caused by CCl4. These observations indicated conserved functions of Ccn2/Ctgf in liver fibrosis between mice and rats. On the other hand, rats and mice have differential metabolisms for certain toxins. For examples, 2AAF can inhibit hepatocyte proliferation to induce hepatic progenitor/oval cells in combination with partial hepatectomy in rats (Solt, Medline, and Farber 1977), but not in mice. Given the fact that oval cell studies have been limited due to technical barriers in genetic manipulation and specific cell isolation for rats during past years, our unique model in this species shall help investigate the role of Ccn2/Ctgf in rat oval cells that are activated during certain chronic liver injury in future. Another important finding in this study is about modulation of Slit2 activity through Ccn2/Ctgf interaction. It seems that Ccn2/Ctgf binding to Slit2 protein requires multiple domains including IGFBP1, vWC, and TSP1 repeat. These domains can contribute to the regulation of fibrotic responses through interaction with growth factors such as TGFβ, vascular endothelial cell growth factor, and bone morphogenic proteins leading to enhanced presentations or sequestration of these factors to cognate receptors thereby modulating corresponding downstream signaling (Abreu et al. 2002), (Inoki et al. 2002). Thus, Ccn2/Ctgf binding may directly influence bioactivity, bioavailability, and presentation of Slit2 protein to Robo1 receptor. Moreover, Ccn2/Ctgf protein may crosstalk with Slit2/Robo signaling due to its adhesive abilities with integrins. For instances, the TSP1 repeat interacts with integrin a6b1 and stimulate Collagen deposition (Heng et al. 2006). The IGFBP1 domain binds to integrin a5b1 and stimulates rat oval cell adhesion (Pi et al. 2008). Although the C-terminal CT domain in Ccn2/Ctgf protein is not necessary for Slit2 interaction, it can directly interact with integrins such as a5b1 and avb3 (Tong and Brigstock 2006; Gao and Brigstock 2004). Integrins a5b1 and avb3 can interact with adhesive ligands through recognition of an Arg-Gly-Asp (RGD) binding motif. To our surprise, Robo1 receptor contains a RGD site in many species including human (Genbank accession# NM_133631.4), mice (Genbank accession# NM_019413), and rats (Genbank accession# NM_022188). It is easy to speculate a direct association among Ccn2/Ctgf, integrins, Slit2, and Robo1 as a whole complex on HSC cell surface during liver fibrosis. Integrins is cellular receptors for Ccn2/Ctgf and regulates a myriad of cellular activities including cell adhesion, migration, proliferation, and survival. Potential integrin binding to Robo1 via its RGD site on HSC cell surface may synergically promote downstream intracellular signaling. Emerging evidence have demonstrated Slit2 involvement in multiple types of liver pathologies. Slit2-Robo1 signaling promotes intrahepatic angiogenesis during ductular reaction (Coll et al. 2022). Slit2 signaling contributes to cholestatic fibrosis in mice by activation of HSC (Li et al. 2019). SLIT2 has also been identified as a driver of tumor dissemination and tumor-associated neutrophil infiltration in relapsed human intrahepatic cholangiocarcinoma (Zhou et al. 2021). Moreover, ROBO1 and SLIT2 are induced in HCC and neighboring cells and shed into serum in humans (Ito et al. 2006). This pair of receptor and ligand can differentiate histopathological subgroups of liver tissues depending on both tumor staging and differentiation status (Avci, Konu, and Yagci 2008). Coincidently, Ccn2/Ctgf has been found to mediate tumor-stroma interaction between hepatoma cells and HSC to accelerate HCC progression (Makino et al. 2018). It is conceivable that the interactions of Slit2 and Ccn2/Ctgf activate Robo and integrin resulting in promoted HSC activation and HCC development in vivo. In supporting of this concept, crosstalk between the activated Slit2-Robo1 pathway and TGFβ1 signaling has been found to promote cardiac fibrosis (Liu et al. 2021). Inhibiting crosstalk of the two profibrotic pathways by targeting Ccn2/Ctgf and Slit2 interaction may help develop therapeutic strategies against HSC activation and liver scaring in chronic liver disease. In summary, this paper presented a novel interaction between Ccn2/Ctgf and Slit2 during liver fibrosis. The two ligands may work in concert to stimulate their corresponding receptors-integrins and Robo1 as part of regulatory mechanisms in their common functions, including inflammatory cell recruitment, angiogenesis, and fibrosis during tissue damage. 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--- title: Ethnic differences in right ventricular structure and function in urbanized hypertensive patients in the Gornaya Shoriya region authors: - Alexey N. Sumin - Nina S. Gomozova - Anna V. Shcheglova - Oleg G. Arkhipov journal: Scientific Reports year: 2023 pmcid: PMC10030773 doi: 10.1038/s41598-023-31834-y license: CC BY 4.0 --- # Ethnic differences in right ventricular structure and function in urbanized hypertensive patients in the Gornaya Shoriya region ## Abstract Aim of this study was to compare right ventricular echocardiography parameters in urbanized hypertensive patients of the Shor and non-indigenous ethnic groups in the Mountain Shoria region. The study included patients with arterial hypertension: 58 Shors and 50 non-indigenous urbanized residents, comparable in age, and divided by ethnicity and gender into 4 groups: Shors men ($$n = 20$$), Shors women ($$n = 38$$), non-indigenous men ($$n = 15$$) and non-indigenous women ($$n = 35$$). All underwent echocardiographic examination, and the right heart parameters were studied. Shor men with arterial hypertension had the lowest values of the pulmonary artery index ($$p \leq 0.05$$), the right atrium dimensions ($$p \leq 0.04$$), and the highest values of the blood flow velocity in the right ventricle, et' ($$p \leq 0.05$$) and st' ($$p \leq 0.05$$) in comparison with non-indigenous men. Shor women have the lowest values Et/At ratios ($$p \leq 0.05$$). RV diastolic dysfunction was detected mainly in women compared with men ($23.1\%$ and $1.9\%$, $$p \leq 0.0014$$), somewhat more often in Shors. Ethnicity was one of the factors associated with the right ventricular diastolic dysfunction presence ($$p \leq 0.002$$). Among the factors associated with the RV diastolic dysfunction were risk factors (smoking, obesity), blood pressure, gender, ethnicity, and left ventricular parameters (diastolic dysfunction and the myocardial mass increase). Thus, our study established the influence of ethnic differences on the right heart echocardiographic parameters in Shors and Caucasians with arterial hypertension. The effect of sex on RV diastolic dysfunction was a lot bigger compared to the effect of ethnicity. The revealed differences should improve the assessment of the right heart structure and function in patients with arterial hypertension from small ethnic groups, which will help to improve the diagnosis and treatment of such patients. ## Introduction Recent studies have pointed out an unfavorable course of arterial hypertension in European ethnic minority populations compared to host populations1,2. A similar situation is observed when examining ethnic minority populations in other regions, for example, in Russian Siberia3,4. To improve treatment and control rates and reduce differences between populations, it is necessary to identify the determinants of these rates across ethnic groups, and to develop and implement ethnicity-specific intervention strategies, which ultimately help reduce ethnic disparities in hypertension-related complications2. There are more and more data on importance of the right heart’s clinical and prognostic significance in cardiac diseases5–7. However, this information mainly relates to chronic heart failure, coronary artery disease or valvular diseases5,6,8–10. Assessment of right ventricular (RV) function is still insufficiently studied in patients with arterial hypertension, despite the first information about its dysfunction in hypertension being obtained over 30 years ago11. Researchers are currently studying the factors associated with RV remodeling in hypertension12 and have already shown an independent effect of RV parameters on the prognosis in these patients13. Distinguishing between normal and abnormal right heart dimensions and functions is therefore clinically relevant. However, a routine assessment of RV function in hypertensive patients (as opposed to, for example, assessing the state of the left ventricle (LV)) has not yet been widely used in clinical practice. Limited normative data is one of the possible barriers: developing normative indicators for right heart echocardiographic assessment used the results of studies carried out mainly in Caucasians in Europe and North America14,15, which cannot be extended to other racial and ethnic groups. Several recent multicenter studies have been aimed primarily at obtaining information about the ethnic characteristics of the left heart structural and functional indicators16–18. For the right heart, such studies are extremely scant19,20, therefore, the study of right heart ethnic characteristics in normal and pathological conditions remains an important scientific direction. However, there is another aspect of the problem. Changes in the structure and function of the heart may not only be caused by genetic characteristics of a particular ethnic group, but also by peculiarities of the lifestyle (for example, traditional lifestyle of ethnic groups), living conditions (countryside / city, highlands / plain), as well as influence of pathological conditions. Previous studies have studied the features of echocardiography in representatives of two ethnic groups of the Gornaya Shoria region—the indigenous population (Shors) and the non-indigenous population (Caucasians) living both in rural areas3,4 and in the city21, (Sumin et al., in press). While the ethnic differences in left ventricular echocardiographic parameters were studied both for healthy urban residents of these groups21 and for patients with arterial hypertension (Sumin AN, in press), the indicators of the right ventricle were only compared in healthy individuals21. Accordingly, the aim of this study was to compare right ventricular echocardiography parameters in urbanized hypertensive patients of the Shor and non-indigenous ethnic groups. ## Patients A cross sectional study of the indigenous (Shor) and non-indigenous (Caucasians) population living in Gornaya Shoria in the south of Western Siberia was carried out during 2017 and 2018. The Shors belong to the South Siberian segment of the Asian race3; in small rural communities in the middle mountains, their lifestyle is focused on hunting, fishing, subsidiary animal husbandry, primitive manual farming and gathering. Following intensive urbanization, the Shors are resettling from the countryside to the cities, which changes the usual way of life. The present study included Shors living in urban conditions. Recruitment of the studied indigenous nationalities and comparison groups was carried out by a continuous method according to the lists provided by the Myski city administration (Fig. 1), with persons aged 18–55 years. Initially, 270 adults (154 Shors and 116 non-indigenous residents) were examined by a therapist during a visit to the city polyclinic. Office blood pressure was measured using an Omron X3 Comfort (HEM-7155-EO) (OMRON, Kyoto, Japan); arterial hypertension was defined as a systolic blood pressure of 140 mm Hg or more, diastolic blood pressure 90 mm Hg or more and / or taking antihypertensive drugs. Patients with symptoms of angina pectoris were excluded from the study; a total of 108 patients with arterial hypertension (58 Shors and 50 Caucasians subjects) were identified. Ethnic groups were matched for gender and age. The study was carried out in accordance with the Helsinki Declaration, approved by the Local Ethics Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russian Federation) and all study participants signed an informed consent. Figure 1Study flowchart. ## Echocardiography All subjects underwent echocardiography on a Vivid S5 ultrasound system (GE Healthcare, Chicago, Illinois, USA) using a phased array sector probe. All echocardiographic examinations were performed by the same examiner (OA), measurements were carried out according to the current guidelines14,15. Reported values of all echocardiographic parameters were obtained as the average value of 3 consecutive cardiac cycles. Structural parameters of the left and right heart were assessed using M-modal and B-modal scanning in standard positions. All structural indicators were indexed to the BSA. The following left heart indicators were used: end-diastolic and end-sistolic diameters (EDD and ESD), posterior wall thickness (TPWLV) and its index (TPWLVi), interventricular septum thickness (TIVS) and its index (TIVSi), end-diastolic and end-systolic volumes (EDV and ESV) of the LV, index of the end-diastolic volume of the LV (LVEDVi). LV mass was calculated in B-mode at the end of diastole, and LV myocardial mass index (LVMi) was also calculated. The LV ejection fraction (LVEF) was determined by the Simpson method. In the section of 4 chambers at the end of diastole, the maximum transverse diameter of the left atrium (LA), its volume (LAV) and index (LAVi) were measured. In the pulse-wave Doppler mode, the velocity of early diastolic atrioventricular flows (E), the flow rate of atrial systole (A). Using spectral tissue Doppler, the parameters of the regional function of the mitral valve annulus, related to the diastolic (e ', a') and systolic parts of the spectrum (s'), were measured. From the data of the right heart, the diameter of the pulmonary artery (PA), its index (PAi), the end-diastolic anteroposterior size of the right ventricle (RV), its index (RVi) were assessed; the size of the right atrium (RA), its area (RAS) and the area index (RASi). In the M-mode, the longitudinal systolic function of the RV was assessed by measuring the systolic excursion of the tricuspid annulus (TAPSE). In the pulse-wave Doppler mode, the velocities of early diastolic atrioventricular flows (Et), the time of their deceleration (DTt), the flow rates of atrial systole (At), the ratio Et / At were assessed. We assessed the parameters of the regional function of the tricuspid valve annulus, related to the diastolic (e't, a't, e't / a't), and the systolic part of the spectrum (s't). The index of the overall performance of the RV were calculated as the ratio of the sums of the isovolumic relaxation time and the isometric filling time to the expulsion time (Tei index). Using color M-modal scanning, we measured the propagation velocities of the early tricuspidal flow (Vft) according to the slope of the brightest part of the spectrum. RV diastolic dysfunction was considered with the ratio Et/At < 0.8 or > 2.1 and/or the ratio Et/e’t > 614. Since the assessment of the RV has not been well established, we used a sufficient number of right heart measurements to detect possible ethnic differences. In assessing the systolic function of the right ventricle, we focused primarily on the indicators of s't and TAPSE. Right ventricular diastolic function was assessed using the ratio Et/At, the ratio Et/et’, Vft, and the ratio e’t/a’t based on our previous studies7,10. ## Statistical analysis Statistical processing was performed using the standard Statistica 10.0 and SPSS 17.0 software packages. Qualitative values were presented in absolute numbers (n) and percentage (%), comparisons between the groups were performed using χ2 tests. The normality of the distribution was verified using the Kolmogorov–Smirnov test. For a distribution other than normal, all quantitative variables were presented as the median, low and upper quartiles (ME [LQ, UQ]). Comparison of quantitative data was carried out using the Kruskal—Wallis test. Qualitative and binary characteristics were compared using the χ2 (chi-square) test with Yates' correction for small samples. Intergroup differences were assessed using the Mann—Whitney test with Bonferroni's correction. Using binary logistic regression analysis (enter method), we studied the relationship of possible factors with RV diastolic dysfunction. The level of statistical significance was taken as $p \leq 0.05.$ Performance of RV values for diagnosing the RV diastolic dysfunction presence was assessed through receiver operating characteristic curve analysis. For intra-observer variability, the analysis was repeated 1 week after the first measurement in 20 random patients. Reproducibility was expressed using the coefficient of variation and interclass correlation coefficients. ## Results General characteristics of the Shor and Caucasian groups are presented in Table 1. The groups were comparable in terms of age and sex. Anthropometric indicators (height, weight, BSA) were lower in Shors compared to Caucasians, but the differences did not reach statistical significance including for BMI ($$p \leq 0.069$$). Smoking was more common among the Shors ($$p \leq 0.012$$). Obesity, and office diastolic blood pressure levels were higher among the non-indigenous population ($$p \leq 0.028$$, and $$p \leq 0.028$$, respectively). The Shors had significantly lower triglyceride, LDL and urea levels and higher HDL levels than the non-indigenous population. A more detailed description of these groups, depending not only on ethnicity, but also on gender, was presented by us earlier (Sumin et al., in press).Table 1General characteristics of hypertensive patients of various ethnic groups. Shors ($$n = 58$$)Non-indigenous ethnicity ($$n = 50$$)ZpAge (years)49 [44–54]52 [44–55]− 0.9740.330Men, n (%)20 ($34.5\%$)15 ($30\%$)− 0.4010.689Weight (kg)68 [62–80]78 [67–88]0.1550.877Height (cm)158.5 [155–164]163 [158–167]1.2060.228BSA (m2)1.74 [1.67–1.85]1.86 [1.72–1.99]− 0.0390.968BMI (kg/m2)27.67 [24.03–30.80]30.41 [25.67–32.56]− 1.8180.069Obesity, n (%)18 ($31\%$)26 ($52.0\%$)− 2.2010.028Elementary education, n (%)4 ($6.9\%$)1 ($2\%$)0.4370.662Secondary education, n (%)23 ($39.7\%$)12 ($24\%$)1.3990.162Secondary special education, n (%)28 ($48.3\%$)34 ($68\%$)− 1.7620.078University education, n (%)3 ($5.2\%$)3 ($6\%$)− 0.0740.941Hard physical labor, n (%)36 ($62\%$)23 ($46\%$)1.4360.151Smoking, n (%)23 ($39.7\%$)9 ($18\%$)− 2.4460.014SBP (mm Hg)138.4 ± 17.4145.4 ± 16.9− 1.8790.060DBP (mm Hg)81.9 ± 7.686.6 ± 9.6− 2.2030.028Glucose (mmol / L)4,95 [4,22–5,80]5.92 [5.15–6.20]− 3.4410.0006Cholesterol (mmol / L)6.47 [5.60–6.97]6.40 [5.90–7.16]− 0.782500.433924LDL (mmol / l)2.90 [2.16–3.70]3.71 [2.90–4.15]− 2.2980.022HDL (mmol / L)1.17 [1.0–1.59]1.06 [0.94–1.20]− 3.3640.0008Triglycerides (mmol / L)2.10 [1.49–2.70]2.44 [2.10–2.94]2.0830.037Urea (mmol / L)4.82 [3.70–6.41]6.80 [5.60–7.49]− 4.4020.00001Creatinine (μmol / l)87.0 [75.1–113.0]94.0 [82.1–109.0]− 0.7360.462ACE inhibitors, n (%)17(29.3)33(66.0)− 2,7860.0011β-blockers, n (%)7(12.1)14(28.0)− 1,2180.092Diuretics, n (%)2(3.44)(4.0)0,00390.98Regular medication, n (%)14 (24.14)30 (60.0)3.240.00015SBP—systolic blood pressure; DBP—diastolic blood pressure, BSA—body surface area, BMI—body mass index; LDL-low-density lipoprotein; HDL—high density lipoprotein; ACE—angiotensin converting enzyme. The main structural indicators of the left ventricle, stratified by sex, had no ethnic differences (Table 2). Only the E / A ratio and the Tei index were the highest in the Shor men ($$p \leq 0.016$$ and $$p \leq 0.034$$, respectively).Table 2Left ventricular and atrial parameters in hypertensive patients of various ethnic groups. Patients of Shor nationality ($$n = 58$$)Patients with non-indigenous ethnicity ($$n = 50$$)pMen ($$n = 20$$)Women ($$n = 38$$)Men ($$n = 15$$)Women ($$n = 35$$)TPW (mm)11 [11.0–12.0]12 [11.0–13.0]11 [11.0–12.0]11 [11.0–13.0]0.38TPWi (mm/m2)9.06 [8.5–10.3]8.6 [7.2–10.3]8.8 [6.6–9.7]8.5 [7.5–9.6]0.15TIVS (mm)12 [11.0–13.0]12.5 [11.0–13.0]11 [11.0–12.0]12.0 [10.0–13.0]0.18TIVSi (mm/m2)9.8 [7.9–10.7]9.08 [8.09–10.4]8.8 [6.6–9.7]8.3 [7.2–9.5]0.39LVMi (g/m2)141.9[119.7–164.2]132 [114–162]127.4 [105–147]134 [108.4–154]0.37LVEDD (mm)50 [48–53]53 [48–54]55.5 [53–56]51 [49–55]0.30LVEDVi (ml/m2)89.4 [84–98]86.7 [73.5–110]102 [77.9–120]89.8 [79.5–104.6]0.46LVEF %66 [62–69]65 [63–68]67 [64–70]67 [64–70]0.52LA (mm)38 [35–39]38 [37–40]39 [37–40]39 [37–40]0.26LAV (ml)81 [50–89]79 [57–88]89 [68–91]81 [66–89]0.68LAVi (ml/m2)63.2 [41.9–72.5]54.5 [44.5–62.5]64.3 [47.8–69.7]53.1 [40.4–66.4]0.14E/A1.3 [1.1–1.54]0.87 [0.75–1.15]0.99 [0.63–1.28]0.81 [0.71–1.27]0.016s' (cm/s)9.5 [9.0–12.0]9.0 [7.0–10.7]9.0 [7.8–13.0]10.0 [8.0–12.0]0.559E/e'0.37 [0.30–0.44]0.30 [0.26–0.41]0.37 [0.29–0.47]0.32 [0.28–0.47]0.44LV Tei index0.37 [0.29–0.44]0.49 [0.40–0.58]0.49 [0.42–0.73]0.50 [0.39–0.64]0.034TPW—posterior wall thickness, TIVS—interventricular septum thickness LVM—LV myocardial mass, LVEDD—left ventricular end-diastolic diameter, LVEDV—left ventricular end-diastolic volume, LVEF—left ventricular ejection fraction LA—left atrium, LAV—left atrium; All structural indicators were indexed to the body surface area; E—early diastolic mitral flow (pulse Doppler); A—late diastolic mitral flow (pulse Doppler); e’ early diastolic relaxation velocity, s’—systolic velocity of lateral mitral annulus (tissue Doppler), LV—left ventricular; $p \leq 0.05$ compared with Shor men. Right ventricle parameters had more noticeable ethnic differences (Table 3). In Shor men, the lowest values of the pulmonary artery index ($$p \leq 0.05$$), right atrium sizes ($$p \leq 0.04$$), and the highest values of the propagation velocity of the RV filling flow ($$p \leq 0.01$$), velocity of early diastolic ($$p \leq 0.05$$) and systolic ($$p \leq 0.05$$) tricuspid annulus movement were identified. Shor women showed the smallest values of the Et / At ratio ($$p \leq 0.05$$).Table 3Right ventricular and atrial parameters in hypertensive patients of various ethnic groups. Patients of Shor nationality ($$n = 58$$)Patients with non-indigenous ethnicity ($$n = 50$$)HpMen ($$n = 20$$)Women ($$n = 38$$)Men ($$n = 15$$)Women ($$n = 35$$)PA (mm)21 [20–21]21 [20–22]22 [20–22]21 [20–22]3.880.16PAi (mm/m2)17.2 [15–18]15.7 [13.9–17.9]15.3 [12.6–17.6]15.4 [13–17.4]5.470.05RA (mm)32 [31.5–35]35 [32–36]35 [33–38]35 [32–38]7.590.04RAS (cm2)11.5 [11.5–14.4]14.4 [11.5–16.3]15.4 [12–17.5]14.2 [11.5–17]5.350.067RASi (cm2/m2)10 [8.4–11.7]10.3 [8.7–11.7]10.7 [9.3–12]9.3 [7.6–11.5]2.560.78RV (mm)29 [25–32]30 [28–32]30 [29–30]30 [29–31]1.330.51RVi (mm/m2)16.74 [14.09–18.19]16.22 [15.14–18.18]15.64 [14.41–17.11]16.52 [15.34–17.53]1.460.48TAPSE (mm)25 [21–26.5]23 [19–27]21 [19–25]20 [18–25]4.30.3Et (cm/s)55 [49–65.5]47.5 [43–60]53 [46–62]53.5 [50–59]− 1.840.06At (cm/s)43 [32.3–51.2]46.5 [35.3–54.8]38.7 [34.8–45.9]42 [34.0–50.9]0.640.52Et/At1.26 [1.1–1.6]1.11 [1.0–1.34]1.25 [1.09–1.45]1.27 [1.1–1.47]− 1.950.05DTt (ms)187.5 [152.5–229.5]196 [170–251]178.5 [130–207]180 [163–222]0.880.37Vft (cm/s)43.5 [39–47]37 [34–44]39 [37–52]38 [33–49]− 2.430.01RV IVRT (ms)72 [67–78]70 [67–80]80 [72–85]75.5 [67.5–81.5]0.090.92e't (cm/s)16.0 [11.0–18.0]13.0 [9.0–16.7]13.7 [11.1–18.0]12.8 [10.2–16.0]− 1.930.05a't (cm/s)15.9 [13.4–21.6]14.6 [11.3–18.03]17.4 [12.9–19.2]15.0 [12.2–17.5]− 1.030.30e't / a't1.04 [0.68–1.3]0.8 [0.7–0.95]0.8 [0.68–1.04]0.8 [0.73–1.04]− 1.810.07s't (cm/s)17.0 [13.8 − 19.5]13.0 [11.0–17.9]16.0 [11.0–20.0]14.0 [11.0 − 15.7]− 0.950.05RV Tei index0.48 [0.3–0.5]0.39 [0.3–0.5]0.5 [0.4–0.5]0.47 [0.3–0.6]0.370.94PA—pulmonary artery diameter; RAS—right atrium square; RV—right ventricle; TAPSE—tricuspid annular plane systolic excursion; RA—right atrium; mPAP—mean pulmonary artery pressure; SPAP—systolic pulmonary arterial pressure; Et—early transtricuspid diastolic filling; At—late transtricuspid mitral diastolic filling; e't—early diastolic tricuspid annular tissue velocity; a't—late diastolic tricuspid annular tissue velocity; s't—systolic tricuspid annular tissue velocity; $p \leq 0.05$ compared with Shor men. The right ventricle diastolic dysfunction was detected in the Shors as often as in the non-indigenous population ($29.3\%$ and $20.0\%$, $$p \leq 0.406$$). However, stratification by sex (Fig. 2) revealed significant differences ($H = 11.81$, $$p \leq 0.0081$$) primarily due to the more frequent detection RVDD in women compared with men ($23.1\%$ and $1.9\%$, $$p \leq 0.0014$$). At the same time, ethnic differences in RVDD between women and men wasn’t revealed. Figure 2The right ventricle diastolic dysfunction frequency in the study groups. RVDD – right ventricular diastolic dysfunction; $p \leq 0.05$ compared with men. In a univariate binary logistic regression including of all variables (Table 4), the following factors had a significant association with the right ventricle diastolic dysfunction (χ2[24] = 62.5, $p \leq 0.001$): ethnicity, sex, smoking, body mass index, obesity, systolic blood pressure, urea, LVMM index, diastolic LV function (e'/a', E/e'). The model explained $67.8\%$ (Nagelkerke R2) of the variance in RVDD and correctly classified $90.1\%$ of cases. In multiple binary logistic regression model (forward LR method), sex was the only significant factor included in the model (χ2 = 11.8, $p \leq 0.001$), but this model explained only $16.2\%$ (Nagelkerke R2) of the variance in RVDD and correctly classified $74.3\%$ of cases. Nevertheless, despite the reliable statistical significance of the latter model, its low quality (judging by the Nagelkerke R2 indicator) does not allow us to reject univariate analysis with other indicators associated with RVDD in, in particular, the ethnicity of the patients. Table 4Factors associated with the presence of right ventricular diastolic dysfunction (results from binary logistic regression analysis).BS.EWalddfSigExp(B)Caucasians ethnicity− 3.4791.1489.17910.0020.031Female sex4.5071.6687.30410.00790.656Age− 0.0070.0680.01210.9130.993Smoking3.1771.4954.51510.03423.971BMI− 0.5150.2404.61510.0320.597Obesity4.1101.8834.76310.02960.951SBP0.1630.0675.86310.0151.177DBP− 0.0120.0910.01910.8910.988Glucose− 0.0140.3220.00210.9650.986Cholesterol− 0.3710.5910.39510.5300.690Triglycerides− 1.4470.8562.85310.0910.235LDL1.0100.5293.64310.0562.746HDL0.6020.6140.96010.3271.826Urea1.3640.4489.27010.0023.912Creatinine− 0.0290.0241.49810.2210.971LVEF− 0.1930.1113.02110.0820.824TPWi− 2.1501.1273.63910.0560.117TIVSi0.3930.8450.21610.6421.481LVMMi5.0401.9666.57010.010154.448E/A0.8021.0190.62010.4312.231e’/a’− 4.3661.6377.11410.0080.013E/e’0.8090.2897.83010.0052.245Constant− 4.28210.2580.17410.6760.014SBP—systolic blood pressure; DBP—diastolic blood pressure, BMI—body mass index; LDL-low-density lipoprotein; HDL-high density lipoprotein; LVEF—left ventricular ejection fraction; TPWi—posterior wall thickness index, TIVSi—interventricular septum thickness index; LVMMi—LV myocardial mass index, E—early diastolic mitral flow (pulse Doppler); A—late diastolic mitral flow (pulse Doppler); e’ -, a’—early and late diastolic relaxation velocity of lateral mitral annulus (tissue Doppler). ROC-curves of the studied left ventricular variables (LVMMi, LVEF, ratio E/A and e’/a') association with RVDD are presented in Fig. 3. The areas under the curves were below 0.7 (for e’/a'—0.682, for LVEF—0.620, for E/A—0.557, and for LVMMi—0.556), which indicated an inability of these to identify RVDD. Among the right ventricle indicators (Fig. 4) the ability to identify RVDD was established for the velocity of early diastolic flow propagation (AUC = 0.872) and the ratio e't / a't (AUC = 0.726).Figure 3Receiver operating characteristic curve analysis. Performance efficacy of the right ventricle echocardiographic parameters in the right ventricular diastolic dysfunction detecting. Vft—propagation velocities of the early tricuspidal flow, Et—early transtricuspid diastolic filling; At—late transtricuspid mitral diastolic filling; e't—early diastolic tricuspid annular tissue velocity; a't—late diastolic tricuspid annular tissue velocity; AUC—area under curve. Figure 4Receiver operating characteristic curve analysis. Performance efficacy of the left ventricle echocardiographic parameters in the right ventricular diastolic dysfunction detecting. e’—early diastolic relaxation velocity; a’—late diastolic relaxation velocity; LVEF—left ventricular ejection fraction; LVMMi—LV myocardial mass index; E—early diastolic mitral flow (pulse Doppler); A—late diastolic mitral flow (pulse Doppler); AUC—area under curve. ## Discussion When assessing the right ventricle parameters in hypertensive patients we revealed lower values of the pulmonary artery index, the size and area of the right atrium, as well as the highest values of the RV velocity flow propagation, the rate of the tricuspid annulus early diastolic and systolic movement in Shor men compared with Caucasians. Shor women had the lowest values of early transtricuspid blood flow and the Et / At ratio. RV diastolic dysfunction was detected mainly in women, somewhat more often in Shor women. Accordingly, ethnicity was one of the factors associated with the RV diastolic dysfunction presence. Risk factors (smoking, obesity), blood pressure, gender, ethnicity, as well as LV parameters (impaired diastolic filling and an LV myocardial mass increase) were also associated with detection of RVDD. Our previous study assessed the state of the right heart in healthy individuals of the Shor nationality21. In that study, an increase in the dimensions of the pulmonary artery and right ventricle was found in Shor men and women compared to Caucasians. The indicators of the RV diastolic filling in the Shors were moderately better, which was manifested, in particular, by higher RV filling velocity in them. Participants of the present study were older and, therefore, both arterial hypertension and age influenced the state of the right heart, leveling out the initial ethnic differences in the LV structural parameters observed when comparing healthy individuals, and the right heart indicators in Shor women. It is possible that genetic factors influenced this dynamic of indicators during hypertension development. It was previously shown that different ethnic groups of rural Gornaya Shoriya residents had different genetic associations with LV hypertrophy. At the same time, LV hypertrophy among hypertensive patients was more prevalent in the Shor group than in the non-indigenous (Caucasian) group4. In the previous study, no genetic associations with the state of the right ventricle were studied, however, in our study, there were no ethnic differences in the severity of LV hypertrophy. A possible reason for this is the influence of environmental factors: the shift from the traditional way of life of the Shors in rural areas to life in the city, greater availability of medical care, and an increase in the educational level. Examining healthy individuals in the MESA-Right Ventricle Study has shown that age, sex, and race are associated with significant differences in RV mass and volumes19. The authors suggested that these differences could potentially explain distinct RV responses to cardiopulmonary disease19; however, in our study, on the contrary, we noted the leveling of the initial ethnic differences in RV parameters during the development of arterial hypertension. Previous studies have shown that the level of physical activity22, smoking23 and the left ventricular hypertrophy12,24 affect the right ventricle. It was previously shown that healthy residents of highlands have small left heart and large right ventricle due to exposure to hypoxemia at high altitudes, and these changes did not depend on ethnicity25. In the present study the RV dimensions are higher in Shor men than in non-indigenous men, which can be explained by a combination of several factors (high smoking frequency, genetic predisposition, and a decrease in daily activity due to changes in the traditional lifestyle is compensated, apparently, by the high prevalence of heavy physical labor among them). We focused on the RV diastolic function since its development precedes systolic dysfunction both in experiment26,27 and during the disease’s development10,28, adversely affecting the prognosis at the same time7,29. Our study confirmed the influence of the above factors on the presence of RVDD. The detection rate of RVDD was significantly lower than in a number of previous studies, where it was up to 45–$60\%$. However, these studies examined patients with stable coronary artery disease before surgery10 or with uncompensated hypertension24. The more frequent RVDD detection in women turned out to be unexpected for us. In previous studies, on the contrary, there was a greater resistance of women to the RV dysfunction development compared to men30. Perhaps this is characteristic of RV systolic dysfunction, but not of diastolic dysfunction. It also cannot be ruled out that the existing criteria for RV diastolic dysfunction may inaccurately reflect its presence in women, which apparently requires further research in this area. Among the RVDD echocardiographic indicators, the velocity of RV filling to the greatest extent reflected its presence. This is probably a natural result. First, this indicator changes linearly with the increasing severity of RVDD, in contrast to the ratio of transtricuspid flow velocities. Secondly, assessing the filling flows of the right ventricle using 4D-MRI turned out to be the most informative in identifying initial changes in the right heart31. It is proposed to continue the study of the 4D-MRI technique in assessing the RV diastole32, but, apparently, it is impossible to leave out the echocardiographic assessment of the RV filling flows due to the greater availability of this examination technique. Furthermore, this is consistent with the notion that, due to its high availability, echocardiography is the first choice of imaging modalities for assessing the right ventricle. In turn, MRI should be performed as a second-line imaging modality in cases where surgery is planned for congenital heart disease or when differential diagnosis is needed33. We see the clinical significance of the study in the fact that, firstly, the leveling of most ethnic echocardiographic differences between Shors and Caucasians in the development of arterial hypertension shows that the clinical assessment may not take the influence of the patient's ethnicity into account. Secondly, the obtained data emphasize the complex interactions of genetic factors, environmental conditions, development of diseases, as well as a change in the traditional lifestyle of the Shors (moving from the middle mountains to the plains, reducing daily physical activity, changing diet), increasing the availability and quality of medical care for them for changes in the right heart. Revealing the diastolic function of the right ventricle predominantly in women among hypertensive patients requires additional study. Besides, the Shors are a small people, the total number is about 14 thousand people. However, they are close to other Turkic-speaking peoples living in Siberia (Altaians, Khakasses, Chulyms, etc.). Therefore, the results obtained by us can be useful for examining representatives of other small peoples of Siberia, as well as other representatives of the Mongoloid race in Asia. ## Study limitation Several limitations should be mentioned. Subclinical coronary artery disease cannot be excluded in this study because coronary angiography was not performed. However, invasive diagnostics had not been indicated since this study included asymptomatic participants with no evidence of atherosclerotic lesions in other arterial regions. Another limitation is the relatively small number of included patients. This was due to the relatively small number of Shors living in urban settings. Nevertheless, we managed to obtain statistically significant results, which are desirable to confirm in larger studies. Finally, the assessment of right ventricular function was based on standard indicators of right ventricular systolic and diastolic function without the use of second-level methods (for example, right and left atrial atrioventricular strain), which have been used in recent years, including in patients with arterial hypertension24,30. However, an international study has shown that new technologies such as global longitudinal strain and 3D echocardiography are rarely used to quantify right ventricular function in clinical setting ($3\%$ and $1\%$, respectively)34. Therefore, the use of traditional RV indicators, in our opinion, at present can be justified, especially in an essentially screening study similar to ours. However, in the future, it is rational to conduct research using new technologies for assessing ethnic differences in RV function. ## Conclusion Our study established the influence of ethnic differences on the right heart echocardiographic parameters in Shors and Caucasians with arterial hypertension. We revealed in Shor men the lowest values of the pulmonary artery index, the size and area of the right atrium, as well as the highest values of the RV velocity flow propagation, the rate of the tricuspid annulus early diastolic and systolic movement in comparison with Caucasians. Shor women had the lowest values of early transtricuspid blood flow and the Et / At ratio. RV diastolic dysfunction was detected mainly in women, somewhat more often in Shor women. Accordingly, ethnicity (Shors) was one of the factors associated with the RV diastolic dysfunction presence. 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--- title: 'Eating order and childhood obesity among preschoolers in China: A cross-sectional study' authors: - Jin Dai - Jingyun Yang - Hailing Fan - Yixin Wu - Huilan Wu - Yun Wang - Tao-Hsin Tung - Lizhen Wang - Meixian Zhang journal: Frontiers in Pediatrics year: 2023 pmcid: PMC10030843 doi: 10.3389/fped.2023.1139743 license: CC BY 4.0 --- # Eating order and childhood obesity among preschoolers in China: A cross-sectional study ## Abstract ### Background Early childhood is a critical period for dietary education and development of good eating habits. However, few studies have investigated the effect of eating order in children and childhood obesity in real-world settings. ### Objective To examine whether the order in which meats/fish or vegetables are consumed affects the risk of obesity in preschoolers. ### Methods We conducted a population-based cross-sectional study using a self-administered online survey on the lifestyle and health behaviors of preschoolers in Taizhou, China. A total of 3,200 parents were invited to take part in the survey, and 2,049 of them completed the questionnaire. Children were classified as having a normal weight, overweight, or obesity using the definitions provided by the International Obesity Task Force, and z-scores for body mass index were calculated. We divided the children's eating order at the beginning of the meal into two groups: “vegetables before meats/fish” or “meats/fish before vegetables”. We analyzed the relationship between what was consumed first at a meal and the overweight status of each child. ### Results No difference in body mass index was observed between the children eating meats/fish-first and the children eating vegetables-first during a meal. Children with parents who were affected by obesity were more likely to eat vegetables first. Among children of mothers with obesity, body mass index was significantly higher in the meats/fish-first group than that in the vegetable-first group (2.891 vs. 0.845, $$P \leq 0.007$$). In children whose mothers were affected by obesity, those that ate meats/fish first had a 12.21 times higher risk of being overweight compared with those that ate vegetables first ($95\%$ CI:1.22–121.74, $$P \leq 0.033$$). ### Conclusion Our findings suggest eating vegetables or meats/fish at the start of a meal does not affect weight status in preschoolers. ## Introduction Childhood obesity remains a global public health challenge. An estimated 39 million children under the age of 5 years were affected by overweight or obesity in 2020 [1]. In China, childhood obesity has increased rapidly over the past four decades, with the latest prevalence being $6.8\%$ for overweight and $3.6\%$ for obesity in children younger than 6 years old, and $11.1\%$ for overweight and $7.9\%$ for obesity in children and adolescents aged 6–17 years [2]. Overweight and obesity affect children's physical and mental health, leading to a series of adverse health outcomes such as cardiovascular disease, type 2 diabetes, and cancer [3]. Additionally, school children with overweight or obesity are more vulnerable to bullying and unfair treatment while in school, which can impede their healthy development. It is well known that the early prevention of obesity is much better than the treatment of obesity at later stages [4]. Several preventable risk factors for childhood overweight have been identified, however there is still a lack of effective long-term interventions [5, 6]. Poor dietary habits and low consumption of healthy foods are risk factors for mortality, whereas good eating habits are vital to a lifetime of health and can help prevent obesity. A study of the general population in Japan showed that modifying unhealthy eating habits and avoiding the accumulation of multiple unhealthy eating habits is important for reducing the risk of obesity [7]. The order in which food is eaten at the start of a meal varies among individuals. The traditional Chinese diet consists of mainly grains, vegetables, poultry, pork and fish [8]. We usually serve cold dishes, drinks and wine first, followed by hot dishes (vegetables, meat, fish or soup), followed by the staple food (rice or wheat), and finally the desert and fruits. In a family, we usually eat together after serving the staple food. Interestingly, eating order was also indicated to be associated with the risk of childhood obesity [9], as consuming vegetables first at a meal may not only reduce meal energy intake, but also increase vegetable intake [10]. A survey of preschool children in Changsha, Hunan Province found that daily consumption of vegetables was reported by $64.4\%$ of children [11]. However, few studies have investigated the relationship between the eating order of meats/fish and vegetables, and childhood obesity. Given the low levels of vegetable intake and the increasing obesity epidemic, we hypothesized that the eating order of meats and/or fish and vegetables during a meal may be associated with childhood overweight and obesity in China. Dietary patterns established in early childhood can persist into adulthood [9, 12]. The pattern and quality of diet in childhood are longitudinally associated with weight and cardiometabolic markers [13, 14]. Therefore, early childhood is a critical period for dietary education and development of eating habits [15]. Our study aims to provide evidence for kindergarten teachers and parents to carry out dietary education for young children. To achieve this, we conducted a survey to investigate the eating habits of preschoolers and to explore the relationship between the eating order of meat and vegetables and childhood obesity. ## Study design and participants Based on a cross-sectional design, we conducted a convenience sampling to recruit eight kindergartens in Taizhou, which is a city with unique cultural and economic background, located in the middle coast of Zhejiang Province, China. A digital survey was delivered to the parents of preschoolers by their teachers using the WeChat-combined Wen-Juan-Xing platform, which was a platform that distributes self-administered questionnaires electronically to others. The participants voluntarily answered the self-administered survey by scanning the Quick Response code on their mobile phones from December 1, 2021, to January 31, 2022. A total of 3,200 parents were invited to take part in the survey,and 2,049 of them completed the questionnaire with a response rate of $64.03\%$. The exclusion criteria included duplicate submissions ($$n = 10$$), children younger than 3 years of age or older than 7 years ($$n = 29$$), and extreme height or weight values defined by outside mean ± 3 SD ($$n = 75$$). This study focused on the effect of eating order on overweight and obesity rather than thinness, so children with thinness ($$n = 419$$) were also excluded according to the International Obesity Task Force (IOTF) BMI cutoffs. After quality control, 1,562 interviewees with valid data were included in the study. The purpose of the survey are explained in the introduction section. Voluntary participation in the survey was considered as the receipt of informed consent from both the children and their parents. This study was approved by the ethics committee of the Taizhou Hospital of Zhejiang Province (approval no.: K20220123) in China. All procedures were performed in compliance with the guidelines of our institutional ethics committee and the tenets of the 1975 Declaration of Helsinki. All information of the respondents was kept anonymous. ## Children's and parental weight status The interviewees reported data on the height and weight of their child and of the child's parent(s). Body mass index (BMI) was calculated as the weight (in kilograms) divided by the square of height (in meters). Based on the International Obesity Task Force (IOTF), each child's BMI was adjusted for age and sex, and converted to a standardized z-score. Children were categorized as having a normal weight, overweight, or obesity [16]. Parental weight status was classifi−ed as having a underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), and overweight/obesity (BMI ≥ 30 kg/m2), based on international thresholds [17]. ## Order of eating The eatin−g order exhibited by children was assessed by the following question: “Does your child usually eat vegetables or meats/fish first at start of meal?” The following two response options were provided: “vegetables before meats/fish” or “meats/fish before vegetables”. ## Covariates The questionnaire collected sociodemographic information on the sex, date of birth, residence (urban and rural), and parental education level of each child. The one-child family was inferred by the following question, “How many siblings does the child have?” ( 0, 1, or ≥2). Annual household income was measured by the following question, “What was your total household income last year?” ( <120,000, 120,000–500,000, or >500,000 Chinese Yuan [CNY]). ## Statistical analysis Categorical variables, including demographic, parental, and familial characteristics, were expressed as counts and percentages. Chi-squared tests were used to compare differences in these characteristics between the vegetables-first group and meats/fish-first group. The difference in BMI z-scores in children with different eating orders was tested using the Student's t-test. Odds ratios (OR) and $95\%$ confidence intervals (CI) were calculated to assess the association between what was consumed first at a meal and the overweight status of each child using a logistic regression model. Place of residence, one-child family, income, and parental educational levels were adjusted as covariates. Statistical analyses were performed using the IBM SPSS software (version 26.0, SPSS Inc.). The tests were two-sided with the significance set at P ≤ 0.05. ## Results A total of 1,562 children aged 3–6 years with a mean BMI z-score of 0.725 ± 1.600 were analyzed in this study, of which 830 were boys and 732 were girls. More than half of the children had a normal weight, while the proportion of overweight and obesity were $10.3\%$ and $12.6\%$, respectively. Overall, the proportion of vegetables and meats/fish consumed first at the start of the meal were $52.1\%$ and $47.8\%$, respectively. The sociodemographic characteristics of the study participants are summarized in Table 1. Compared with children who ate vegetables first, the children who ate meats/fish first had higher levels of parental education and annual household income, a higher proportion of urban residents and one-child families, and a higher proportion of fathers who were affected by overweight and obesity ($P \leq 0.05$). Table 1 \| Variables \| Total (n = 1562) \| Food consumed first \| Food consumed first.1 \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Variables \| Total (n = 1562) \| Vegetables (n = 814, 52.1%) \| Meat/Fish (n = 748, 47.9%) \| \| \| Child-related characteristics \| Child-related characteristics \| Child-related characteristics \| Child-related characteristics \| Child-related characteristics \| \| BMI Z score \| 0.725 ± 1.600 \| 0.768 ± 1.638 \| 0.679 ± 1.554 \| 0.274 \| \| Body weight status \| \| \| \| 0.438 \| \| Normal weight \| 1,204 (77.1) \| 619 (76.0) \| 585 (78.2) \| \| \| Overweight \| 161 (10.3) \| 84 (10.3) \| 77 (10.3) \| \| \| Obesity \| 197 (12.6) \| 111 (13.6) \| 86 (11.5) \| \| \| Sex \| \| \| \| 0.386 \| \| Boy \| 830 (53.1) \| 424 (52.1) \| 406 (54.3) \| \| \| Girl \| 732 (46.9) \| 390 (47.9) \| 342 (45.7) \| \| \| Age (years) \| \| \| \| 0.159 \| \| 3 \| 375 (24.0) \| 177 (21.7) \| 198 (26.5) \| \| \| 4 \| 500 (32.0) \| 266 (32.7) \| 234 (31.3) \| \| \| 5 \| 533 (34.1) \| 291 (35.7) \| 242 (32.4) \| \| \| 6 \| 154 (9.9) \| 80 (9.8) \| 74 (9.9) \| \| \| Parental-related characteristics \| Parental-related characteristics \| Parental-related characteristics \| Parental-related characteristics \| Parental-related characteristics \| \| Father's age \| 37.1 ± 11.1 \| 37.5 ± 14.2 \| 36.6 ± 6.0 \| 0.109 \| \| Mother's age \| 35.2 ± 6.8 \| 35.4 ± 7.8 \| 34.9 ± 5.4 \| 0.095 \| \| Father's education level \| \| \| \| <0.001 \| \| Junior or below \| 233 (14.9) \| 140 (17.2) \| 93 (12.4) \| \| \| Senior \| 433 (27.7) \| 254 (31.2) \| 179 (23.9) \| \| \| University or above \| 896 (57.4) \| 420 (51.6) \| 476 (63.6) \| \| \| Mother's education level \| \| \| \| <0.001 \| \| Junior or below \| 220 (14.1) \| 144 (17.7) \| 76 (10.2) \| \| \| Senior \| 324 (20.7) \| 194 (23.8) \| 130 (17.4) \| \| \| University or above \| 1,018 (65.2) \| 476 (58.5) \| 542 (72.5) \| \| \| Father's BMI category \| \| \| \| 0.005 \| \| Normal weight \| 854 (57.7) \| 466 (61.6) \| 388 (53.7) \| \| \| Overweight \| 541 (36.6) \| 247 (32.6) \| 294 (40.7) \| \| \| Obesity \| 84 (5.7) \| 44 (5.8) \| 40 (5.5) \| \| \| Mother's BMI category \| \| \| \| 0.527 \| \| Normal weight \| 1,304 (87.5) \| 663 (86.6) \| 641 (88.5) \| \| \| Overweight \| 152 (10.2) \| 84 (11.0) \| 68 (9.4) \| \| \| Obesity \| 34 (2.3) \| 19 (2.5) \| 15 (2.1) \| \| \| Family-related characteristics \| Family-related characteristics \| Family-related characteristics \| Family-related characteristics \| Family-related characteristics \| \| Residence \| \| \| \| <0.001 \| \| Urban \| 1,011 (64.7) \| 483 (59.3) \| 528 (70.6) \| \| \| Rural \| 551 (35.3) \| 331 (40.7) \| 220 (29.4) \| \| \| One-child family \| \| \| \| 0.021 \| \| Yes \| 669 (42.8) \| 326 (40.0) \| 343 (45.9) \| \| \| No \| 893 (57.2) \| 488 (60.0) \| 405 (54.1) \| \| \| Annual household income (CNY) \| \| \| \| 0.006 \| \| <120,000 \| 330 (21.1) \| 197 (24.2) \| 133 (17.8) \| \| \| 120,000–500,000 \| 1,000 (64.0) \| 506 (62.2) \| 494 (66.0) \| \| \| >500,000 \| 232 (14.9) \| 111 (13.6) \| 121 (16.2) \| \| Table 2 presents the prevalence of overweight and obesity among children with different parental weight statuses. Children with parents who were affected by obesity had higher percentages of overweight and obesity, regardless of whether the father or mother had obesity ($P \leq 0.05$). Table 2 \| Parental weight status \| Child's weight status \| Child's weight status.1 \| Child's weight status.2 \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Parental weight status \| Normal \| Overweight \| Obesity \| P \| \| Mother's BMI groups \| \| \| \| <0.001 \| \| Non-obesity (n = 1456) \| 1,141 (78.4) \| 147 (10.1) \| 168 (11.5) \| \| \| Obesity (n = 34) \| 16 (47.1) \| 6 (17.6) \| 12 (35.3) \| \| \| Total (n = 1490) \| 1,157 (77.7) \| 153 (10.3) \| 180 (12.1) \| \| \| Father's BMI groups \| \| \| \| 0.007 \| \| Non-obesity (n = 1395) \| 1,103 (79.1) \| 134 (9.6) \| 158 (11.3) \| \| \| Obesity (n = 84) \| 54 (64.3) \| 15 (17.9) \| 15 (17.9) \| \| \| Total (n = 1479) \| 1,157 (78.2) \| 149 (10.1) \| 173 (11.7) \| \| We compared the BMI z-scores of children who ate vegetables first and those who ate meats/fish first, stratified by whether their fathers or mothers were affected by obesity. The results are presented in Table 3. Only in children whose mother was affected by obesity, the BMI z-score of those who ate vegetables first ($$n = 19$$) was significantly lower than that of those who ate meats/fish first ($$n = 15$$) (0.845 ± 1.984 vs. 2.891 ± 2.098, $$P \leq 0.007$$). Table 3 \| Stratification \| n1/n2 \| Food consumed first \| Food consumed first.1 \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Stratification \| n1/n2 \| Vegetables \| Meat/fish \| P \| \| Mother's BMI groups (n = 1490) \| 766/724 \| \| \| \| \| Non-overweight \| 663/641 \| 0.684 ± 1.579 \| 0.585 ± 1.464 \| 0.242 \| \| Overweight \| 84/68 \| 0.900 ± 1.485 \| 1.030 ± 1.807 \| 0.628 \| \| Obesity \| 19/15 \| 0.845 ± 1.984 \| 2.891 ± 2.098 \| 0.007 \| \| Father's BMI groups (n = 1479) \| 757/722 \| \| \| \| \| Non-overweight \| 466/388 \| 0.599 ± 1.56 \| 0.579 ± 1.533 \| 0.846 \| \| Overweight \| 247/294 \| 0.768 ± 1.457 \| 0.755 ± 1.533 \| 0.917 \| \| Obesity \| 44/40 \| 1.357 ± 1.823 \| 0.744 ± 1.374 \| 0.088 \| Table 4 summarizes the effect of eating meats/fish first on the risk of overweight or obesity in preschoolers, stratified by parental weight status. Compared with eating vegetables first, eating meats/fish first was significantly associated with a 12.21 times higher risk of being overweight ($95\%$ CI: 1.22–121.74, $$P \leq 0.033$$) in children whose mothers were affected by obesity, after adjusting for place of residence, number of children, income, and parental education levels. Table 4 \| Stratification \| P \| OR \| 95% CI \| \| --- \| --- \| --- \| --- \| \| Mother's weight status \| Mother's weight status \| Mother's weight status \| Mother's weight status \| \| Non-obesity \| 0.764 \| 0.96 \| 0.75–1.24 \| \| Obesity \| 0.033 \| 12.21 \| 1.22–121.74 \| \| Father's weight status \| Father's weight status \| Father's weight status \| Father's weight status \| \| Non-obesity \| 0.676 \| 1.06 \| 0.81–1.38 \| \| Obesity \| 0.317 \| 0.54 \| 0.16–1.82 \| ## Familial clustering of obesity Genetic, environmental, and behavioral factors have been shown to contribute to childhood obesity. Familial clustering of obesity has been reported in several countries [18, 19]. Family environmental factors, such as dietary behavior, are important risk factors for increasing obesity in children [20]. In a childhood obesity study from Brazil, parents with obesity were emphasized as an independent predictor of childhood overweight [21]. Consistent with previous studies, our study also found that parents with obesity had children who were more likely to have obesity. In addition, we found that parents with obesity may have an impact on the order of foods in which their children ate. These findings emphasize the role of family in preventing obesity. ## Association of eating order with obesity Eating order is a specific detail of one's eating habit and often varies from person to person. Previous studies on eating orders have focused on glycemic control in adults with diabetes [22, 23]. Few studies have examined the link between eating order and risk of obesity. In our study, we investigated the relationship between the consumption order of meats and vegetables and weight in children through a self-administered survey. We found no significant difference in either BMI z-score or proportion of obesity status between children who ate vegetables-first or meats/fish-first during a meal. However, in a small subsample of children whose mothers were affected by obesity, we found that children who ate meats/fish first were more likely to have obesity than children who ate vegetables first. In a study with 4,040 first-grade students from Tokyo, Japan, it was reported that children who ate meats/fish at the beginning of a meal had a higher risk of being overweight than those who ate vegetables-first [24]. There are several possible explanations for the conflicting results. First, there are differences in both genetic and sociodemographic characteristics, including age, family, and socioeconomic status of each study population [25]. Second, significant differences in dietary structure and cultures between Chinese and Japanese populations may be responsible for these inconsistent findings (26–28). Finally, in contrast to the World Health Organization's (WHO) Child Growth Standards used in the Japanese study, our study utilized the IOTF BMI cut-offs to assess overweight and obesity in children. Therefore, the prevalence rates of childhood obesity calculated under these two criteria are quite different [16]. ## Advantages of eating vegetables first The consumption of vegetables first may have greater benefits than other alternatives. A Japanese study showed that children who ate vegetables first had a higher total vegetable intake than those who did not eat vegetables first [29]. A study in the United States reported similar results [10]. Eating vegetables first led to increased vegetable intake and reduced food energy intake [10]. The vegetable intake of children who ate vegetables first was $93\%$ higher than of that in children who do not often or never eat vegetables first [29]. In addition to children, eating vegetables also has many advantages for adults. A study found that eating vegetables first, then meat and/or fish, and then carbohydrates can prevent the fluctuation of postprandial blood glucose and insulin levels [23]. For patients with type 2 diabetes, eating vegetables can effectively control short- and long-term blood glucose levels [30]. Moreover, increasing intake of vegetables and reducing intake of saturated fats in children with a familial history of obesity early on may help prevent the occurrence of type 2 diabetes [31]. ## Establishing reasonable eating habits This study included kindergartens in different districts and levels of Taizhou. This study showed that parents with obesity had an impact on the order in which their children ate food and the risk of obesity in their children. It is known that dietary habits develop during childhood [32]. Poor diet during childhood may persist into adulthood and increase one's risk of obesity and obesity-related complications such as type 2 diabetes [2, 33]. In our study, children of mothers with obesity who eat meats/fish first had a significantly increased risk of being affected by overweight and/or obesity. Therefore, eating vegetables first during a meal may be a better option, especially for children whose mothers were affected by obesity. Our study could direct further research into the impact of eating order in this subpopulation of children with parents with obesity. ## Strengths and limitations Few studies have examined the relationship between children's eating order and health. Japanese scholars first found that eating vegetables before meats/fish could reduce the risk of obesity [21]. This study provides new evidence of the association between eating order and childhood obesity in China. Furthermore, the results of this study can provide a reference for kindergarten teachers in China to conduct dietary education for children. This study had several limitations. First, the participants were recruited from a single city in southern China, which is not representative of the entire population. Second, when stratified by maternal or paternal obesity, the sample size of the maternal obesity group was only 34. The small subsample size may have affected the results of the analysis. Third, after data quality control, the percentage of valid questionnaire was $76.2\%$. Fourth, data on weight, height, and eating order were recalled by memory and self-reported by the children's parent, therefore limiting the accuracy of the data due to recall bias. Most studies showed that self-reported height was overestimated and weight and BMI were underestimated [34, 35]. Fifth, other possible confounding factors, such as eating behavior, nutrition, exercise, and sleep, were not considered. Sixth, we did not provide the consistency and stability of the order in which children ate at each meal. In the future, With the consent of children's parents, we will carry out a one-week investigation and study, in which the children's eating order of meats/fish or vegetables at the start of a meal will be recorded every day. Seventh, additional aspects of eating order that was not addressed in this study. We did not consider the effect of eating other foods first, such as bread/rice, soup or others. Eighth, we used only one question to measure eating vegetables or meats/fish first at the start of a meal, which may introduce social desirability bias. Finally, considering the cross-sectional nature of the study, we could not clarify the causal relationship between eating order and weight status. Future longitudinal studies can be conducted to further validate this relationship. ## Conclusions Our study demonstrated that parents with obesity increased the risk of obesity in their children. There is no evidence to support the association between the order of meats/fish and vegetable consumption, and overweight and obesity in children. Further studies may be required before making recommendations on eating order. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Author contributions MZ and LW designed and implemented this study. HF and HW conducted the collection of questionnaires. YWu and YWa collated the data. JD and JY wrote the first draft of the paper. MZ and JY conducted data analysis and mainly revised the manuscript. MZ and THT interpreted the data. LW and MZ responded to the questions and made critical revisions to the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. 1.World Health Organization. Facts and figures on childhood obesity; 2020. <https://www.who.int/end-childhood-obesity/facts/en/>. 2. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol (2021.0) 9 373-92. DOI: 10.1016/S2213-8587(21)00045-0 3. Zhi X, Kuang XH, Liu K, Li J. 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--- title: Illegitimate bodies? Turner syndrome and the silent interplay of age, gender, and generational positions authors: - Nicoletta Diasio journal: Frontiers in Sociology year: 2023 pmcid: PMC10030844 doi: 10.3389/fsoc.2023.1084707 license: CC BY 4.0 --- # Illegitimate bodies? Turner syndrome and the silent interplay of age, gender, and generational positions ## Abstract This paper focuses on the strength of social norms that define the right development of the body in time. It also analyzes how the social positions of age, gender and generation intertwine in the definition of such a legitimate body. The starting point is anthropological research carried out in France between 2018 and 2020 among girls and women affected by Turner syndrome, a rare genetic condition causing small stature, ovarian insufficiency, a delay or absence of puberty, and infertility. We first explore how measuring the body has become central in the social construction of the concept of age-appropriateness. We then present four women' narratives, which express various forms of desynchronization: the gap between physical appearance, chronological age and age status; the cleft between the physical development induced by hormone therapy and being in a particular stage in life; the difference between chronological and reproductive age; and lastly, the trouble in a generational position related to infertility. For women suffering from this genetic condition, the gap between bodies, time and social statuses associated with age, gender and generation, may engender a feeling of “being out of place.” The alignment of body and time is then one of the bastions of essentialization and naturalization. Finally, we stress the complex interplay of bodily and social markers of age and gender, and their role in social relations as both a resource and a constraint. Thus, while the syndrome may cause distress and sometimes a lack of legitimacy, it also leads to a critical re-examination of hegemonic models of womanhood and their intersection with age positions. ## Introduction Anthropology has for long explored how “the body is used to think time, and how it also, in turn, is used to set the clock of the temporal thresholds which society invents” (Julien and Voléry, 2019: p. 7). Since the beginning of the 20th century, ethnologists have demonstrated how societies carefully organize the coupling of bodies and times in defining age and social transitions. More specifically, these passages—births, deaths, the end of childhood, the beginning of adulthood, the start and the end of procreative or sexual periods—all constitute opportunities to re-establish individuals and groups in time: in an age, in a life's stage, and in a generational position. Thus, despite the academic and social discourse about the dynamic character of the life course (Elder et al., 2003), and despite the reconfiguration of the categories of age (Blatterer, 2007a,b), and gender performativity (Butler, 2004), the social norms that establish a conformity between bodily transformations and stages in life have not disappeared: they mutate and reemerge in different forms. Expert knowledge is still home to norms that codify the link between body and time. The “surgery of age” (Moulinié, 1998) aligns the human body with standards of development or shields it from the ravages of time (Vincent, 2006; Dalgalarrondo and Hauray, 2015). Through hormonal corrections, a biological materiality is adjusted to social ambitions of self-optimization, to gender social identities, and to age status (Oudshoorn, 1994; Conrad and Potter, 2004; Toër-Fabre and Levy, 2007; Fishman et al., 2010). This paper aims to show how age categories, gender norms and generational position intertwine in the definition of a “legitimate body” (Boni-Legoff, 2016). According to Isabelle Boni-Legoff, this legitimacy is based on the opposition which hierarchizes bodies between masculine (legitimate) and feminine (illegitimate). She also underlines the importance of the triad “gender, class, race” in defining the contours of conformity to hegemonic norms. This approach, however, underestimates two aspects. The first is the place of bodily materiality itself, which is inseparable from its discursive setting and foundational to the experience that the actors have of this legitimacy.1 The second is the importance of the age position in the social structure and in the process of constructing personhood. The under-estimation of age categories, compared to the value given to gender, ethnicity, race and class, has been stressed by other authors, particularly those working in fields where age-related asymmetries are more apparent, such as childhood (James and Prout, 1997) or aging (Rennes, 2020).2 Finally, the position in the generational order as a central dimension of the “legitimate body” is relatively absent from the scientific debate.3 Gender distinction—whether it is assigned, negotiated or reformulated over time—age and generation constitute the very nexus between the body, the short time spans of the individual life, and the longer times which are perpetuated through the lines of descent. “ Whether it is seen as the biographical time that brings an individual from conception to birth and then to death, or the historical time that distinguishes the past, the present and the future of a society, or the socio-cosmic time spanning cycles of metamorphosis and the regeneration of beings, the masculine/feminine distinction is continually deployed and changed, constructed and altered” (Théry, 2008: p. 31). The starting point of this article is a research study on the experience of women affected by Turner syndrome, a genetic condition causing slowed growth and variations in physical and sexual development. The syndrome upends the apparent concordance between the time of growth, the social age, the expressions of conventional femininity that emerge over a number of successive stages (the forming of breasts, the first menses, the beginning of sexual activity, …), and the longer time of generational renewal, marked by the possibility of motherhood (Maciejewska-Mroczek et al., 2019). For women who suffer from this genetic condition, the gap between the physical development in time and social status associated with age, gender and generation, may engender a persistent feeling of “being out of place.” The exceptions due to this chromosome disorder allow us to demonstrate the importance of the “right” time for the fabrication of a “right” body, as well as the power of the norms that connect bodily transformations to the age and thresholds fixed by society. As the term “age” is polysemous, we will distinguish throughout the article between chronological age, age status, the stage in life, and age linked to a generational position. These different meanings intertwine and overlap, but as they interact, they refer to various ways of finding one's place in a time disrupted by illness. After presenting the research and the fieldwork, the article explores how measuring the body has become central in the social construction of age, and in the formation of the concept of “age-appropriateness” (Kelle, 2010). Then, we analyze how, among women suffering from Turner syndrome, the impossibility of conforming to such standards can produce a lack of legitimacy associated to various forms of desynchronization: between physical appearance, chronological age and age status; between the physical developments induced by hormone therapy and a particular stage in life; between chronological and reproductive age and generational position. Finally, the paper stresses how the relationship between body and time results from a complex interplay of social markers of age and gender that represent different ways of coping with the social and biographical situation. ## Fieldwork and methods The present article is the result of an ongoing research project on the bodily experience and life trajectories of people suffering from Turner syndrome. This is a rare genetic condition, due to complete or partial absence of the second chromosome X, that affects about ½, 500 newborn females in France every year. The consequences are small stature, ovarian insufficiency resulting, for the most part, in a delay or absence of pubertal development, and infertility. Morphological specificities and associated disabilities (e.g., hearing deficiencies) as well as an increased risk of diseases caught later on can also appear. Hormone treatments—both growth and sexual hormones—can be part of the treatment. Such pharmaceutical corrections are part of a molecular fabrication of age and gender (Gaudillière, 2003; Murano, 2019) that calls into question how the medical profession, as well as women and their entourage, deal with the risks of normalizing the body by its “enhancement” (William et al., 2011; Rajtar, 2019). The complex history of Turner syndrome also gives an insight into the scientific debate on sex/gender variations, which is “good to think with” as Löwy recalls quoting Lévi-Strauss (Löwy, 2019: p. 31). The fieldwork that gave rise to this article was conducted in France and carried out through ethnographic and participative methods. We met 20 girls and women between the ages of 10–60, all heterosexual, from different social classes and with varying educational backgrounds. Skin color, migrational background or the experience of being racialized or ethnicized are not significant in the population interviewed. One person had recently migrated from Central Africa to join her sister in France and to receive medical follow-up.4 The first interviewees were approached through a message that circulated thanks to a not-for-profit organization, explaining the objectives and methods of the research. After the initial contacts, people were recruited through a snowball effect. We met the respondents in their own homes on several occasions, some of them twice, and sometimes in the context of the activities of a patients' association. This approach allowed us to gather narratives in a variety of speaking situations: formal interviews, informal exchanges, for example at lunch or over a coffee, and conversations between women or girls affected by the syndrome. The formal interview guide was organized around six topics: the discovery of TS and the care trajectory; the experience of growing up and aging; the age transitions and the expected physical transformations; the evolution of the relationship with one's body, with oneself and with others; the competences engendered by Turner's syndrome; the perspective on both medical and associative care. As is common during ethnographic fieldwork, other issues arose during the discussions. In the case of the younger participants, we also met their parents and sometimes their siblings. During 2 years, we participated in the activities of a patients' organization.5 We also held two workshops on what it means to grow up with Turner syndrome. We then carried out a content analysis, paying particular attention to contextualizing the narratives and pointing out the discrepancies and convergences between the different issues with respect to the modes and contexts of the data collection (e.g., formal interviews or observation). The narratives and experiences are extremely diverse. Working with a group suffering from a rare syndrome poses the problem of constituting a population that has homogeneous social characteristics, while the women we worked with have different ages, family and professional situations and largely different histories with the disease. This diversity makes it difficult to interpret the influence of class and “race” on women's experiences. On the other hand, it has enabled us to highlight the importance of age, gender and generational positions, which emerge as transversal elements in the construction of a legitimate body. The variability is also linked to the specificity of syndromes, which are not diseases and manifest themselves rather like the expression of an anomaly (Canguilhem, 1943). A syndrome can vary greatly in shape from one person to another and its manifestations will unfold along widely diverging timelines. Finally, there is the history of Turner syndrome itself, starting with its first description dating back to 1938. Its chromosomic origins were identified in 1965 and growth and sexual hormone therapies, were widely adopted only from the 1990s onwards. Today the diffusion of prenatal screening has contributed to parents being informed at an increasingly early stage, which allows them the possibility to interrupt a pregnancy or anticipate the hormone treatments for their little girl. Treatments have evolved too, having become more individualized with a more refined adjustment of dosages. Because of this great diversity, we have decided to present our findings under four narratives. They echo the testimonies of several of the women we met, while allowing for a better contextualization of their statements and experiences. These four cases were selected for several reasons. First, each of them is particularly representative of the form of desynchronization that we wish to highlight. Second, the chosen narratives are particularly dense with information that makes it possible to situate the experience of Turner syndrome as disorder in time and in status in the overall life course. Third, the four individuals are very different with respect to social class, family history, level of education, and place of residence. The narrative of the first interviewee is characterized by the durable experience of poverty and social marginalization in a small provincial town. The second interlocutor belongs to the urban wealthy bourgeoisie, and has been educated in some of the best schools in the country. The third person has experienced a history of upward social mobility, as she is the daughter of farmers who ended up in a recognized and well-paid profession. The fourth subject belongs to the middle class, characterized however by a significant intellectual “capital” in Bourdieusian terms. The choice of these narratives is not meant to associate social profiles with experiences of the syndrome. However, we considered it important not to eliminate the diversity of social conditions and to situate the narratives in specific contexts. ## The normative power of physical development The need to organize bodily changes and to establish fixed thresholds for different ages took on a particular dimension between the 19th and 20th centuries. Scientific measuring of the body accompanies child policies, as well as policies concerning adolescence and old age. The techniques of surveillance medicine such as screening, population studies, statistical enquiries, and public health campaigns have turned the variations of these changing bodies into measurable, understandable, objective, and predictable phenomena (Armstrong, 1995). Developmental thinking has introduced the idea of a life cycle divided in a regular and universal succession of stages (Turmel, 2008; Diasio, 2019b), which relating bodily transformations to a specific vision of time, seen as linear, irreversible, progressive and teleological. This epistemology and the apparatus it deploys aim to stabilize the mutability of the body. The purpose is to distinguish between changes that happen over the course of a life and are associated with a social age, and the modifications that stem from a changed state of health. Science has taken up the challenge of making these variations measurable, understandable, objectifiable and predictable, particularly in fields like pediatrics, psychiatrics and geriatrics. This process has contributed to a conceptualization of the “healthy” body as a “stable” body (Armstrong, 1983; De Swaan, 1990), and of adulthood as the age of stability. The variations also define which dispositions and behaviors are possible, or even recommended, for which times in life: psycho-cognitive development tests for children or graphs measuring the autonomy of older people are examples of this. This means that age-appropriateness, now more than ever, constitutes a central component of the management of people's existence: the beginning of school-going, leisure and sports activities, relationships and sex lives, and ages to become a parent (or not) and to leave one's “active” life. The physical, cognitive or psychological measures intertwine with political decisions in order to find the best regulation of people's life courses. The association of age categories with the social distribution of competences is not a prerogative of so-called Western modernity, and several societies institute “a normative relation between a certain age and an activity” (Widmer, 1983: p. 346). However, in the technology-based contemporary societies, this combination is founded on the scientific measures and its legitimacy is reinforced by a linear, chronological and mathematical definition of age. The contemporary fascination for quantification (Voléry, 2020) also combines with the standardization, the bureaucratization and the individualization of age as “a neutral and universal criterion for public action” (Rennes, 2019: p. 266). These quantified measures are appropriated by the social actors as a way of precisely establishing the stages of their development: children, who are the first target of this process, do not hesitate to declare, for instance, that they are “8 years and 3 months” old. The bodily changes and the measure of age are thus at the very core of their social identities (James, 1993). The importance of measuring has two consequences. Firstly, it reinforces the effects of the naturalization of age and the difficulty of thinking this dimension as a social category with its effects on the people being categorized (Hacking, 1999). The government of time and of the body's instability is a central dimension of a biopolitical order, but it is a “soft” biopolitics (Diasio, 2019a), all the more efficient because it is so obvious. Secondly, the concern with defining more and more precisely what is “age-appropriate” opens up areas of uncertainty (Kelle, 2010). The more we try to grasp universal criteria of development, the more we encounter variations, nuances and idiosyncrasies. That is how diagnoses of non-conformity with age-appropriate-development have also become so widespread in the field of formal school learning, and as in the alarm about the precocious puberty in spite of the controversial medical data (Cozzi and Vinel, 2015; Piccand, 2015). The medicalization of ages that are judged to be “critical” (e.g., puberty, menopause) entails both an eagerness to define what is proper to a given age, and the difficulty to distinguish between “normal” and “pathological” changes. “ The body's aging process, whether in childhood or in later life, has become, in itself, problematic during the course of the 20th century in Western societies (…) the instability of the aging body, coupled with a decline in childhood mortality and increasing life expectancy, has worked to blur biomedicine's normal division between natural and pathological bodily change. And, in so doing, it has produced a range of new uncertainties about the life course as lived” (James and Hockey, 2007: p. 143). Finally, the measuring of ages and bodies is now faced with a medical world that has become more and more individualized and where the molecularization and singularization of treatments call into question instituted categories (Rabinow and Rose, 2006; Raman and Tutton, 2010). An illness or a genetic condition will deeply affect this normalization of the link between body and time. A serious child disease will place the child in a position that may put it “outside of childhood” (Bluebond-Langner, 1996), while older people faced with degenerative processes are described as returning to childhood (Hareven, 1995). In other circumstances, such as with the case of myotonic dystrophy, adults who are considered at the height of their strength will have to take the same precautions as older people (Perrot, 2021). Such imbalances do not necessarily lead in the direction of increased fragility: children suffering from type 1 diabetes can outperform adults when it comes to auto-administering their care (setting up the catheter, injecting themselves), by subverting attitudes that are usually associated with their age and, sometimes, their gender (Williams, 2000; Renedo et al., 2020). With Turner syndrome, the experience of stunted growth, small stature and late puberty highlights the normative power of the “right” kind of development when it comes to defining who one is and what one's place is: “A friend of the family described me as an old child, and I proudly compared myself to Peter Pan. […] I might not object to womanhood, but I could not imagine myself as a woman. Being a kid defined who I was” (Beit-Aharon, 2013). This temporal disorientation can also translate into a confusion about one's status: am I a child? An adult? A woman? We will focus now on four “cases” that exemplify, each one in its peculiar way, the diversity of misalignments between body, time and gender norms. These stories also present women of different ages, social conditions and family histories. As such, they require more in-depth exploration to set their experiences in perspective and in a specific context. ## Nadine: “As a child treated worse than dirt” Nadine is 55 and was born and raised in the East of France. She lives alone in social housing, in a suburb of a median size town. Her apartment carries the traces of a medicalized life: her bed is equipped to facilitate her weekly injections, there are piles of drugs in the bathroom and living room, and she has a regulator for the sound on her TV to help her with her hearing problems. As she is currently working as a home care assistant, she could be described as the help who “does the dead,” as Verdier [1979] put it. That is also how she talks about her work: “I close their eyes,” she says, and proudly recounts how she was able to accompany several people all the way to the end of their lives. As the eighth of eleven siblings, born to a family of farmers, she was raised by her grandmother until she was 11. When she was a little girl, she was already “always” getting sick. Her frail health and her difficulties at school made her father decide to make her abandon school. That was her first struggle, because she enjoyed studying, even though she was slow and had to suffer the mockeries of her classmates who called her “the dwarf.” Then, when she turned 14, she left school and entered an institution. Fourteen was also the age at which her parents, worried about the fact that she still had not had her period, took her to a doctor: “They took me to Paris, I underwent some tests and all that and that's where they discovered I had Turner syndrome. But nobody told me anything. When I was at school […] it was the nurse who would give me the drugs that made me have my period like everyone else.” The nurse came to the refectory every day at 12 to distribute the boarders' medication. Nadine would remain in the dark about her illness until she turned 24: “By dint of … my parents ended up telling me. And then after a while I also asked the doctors.” She received very vague explanations about some genetic problem, until the day she obtained more precise information through a patients' organization. This struggle for knowledge was doubled by a permanent struggle for recognition of herself and her abilities, in spite of her small stature. “ I was treated like dirt,” she often repeats. It was a struggle against her parents who considered her “inferior” to other children, a struggle for the right to get her driver's license and her first responder's certificate, or her diploma of home care assistant. As she says: “OK, we're not tall, but I do the work just as well as a person of natural height.” In her account, her small stature appears to be the source of a double process of inferiorization and invisibilization. It puts her in a status of “a child,” and “a sick child” at that. “ To make people see that I'm here, that I'm fighting, that is very hard […] when I go [to the doctor] with someone, he will be talking to the tall person who accompanies me, instead of talking to me.” The struggle to be seen and heard influences the affirmation of her femininity too. Being a woman with Turner syndrome means “fighting, thinking, and making oneself heard.” Nadine is thus clearing a path for herself where she can be seen and heard. She loves music, and at one moment, during the interview, she sings in a confident, well-placed, and resounding voice. She sings at family gatherings or with friends, dressed as a boy, and incarnates male stars of French pop music from the seventies. Several of their portraits adorn the walls of her apartment. As she is squeezed into the position of a child, which contrasts with her chronological age, Nadine mobilizes her body to “show herself ” and “be heard,” and her entire account is structured on these different sensory matrixes. ## Corinne: Going through life with a persistent feeling of illegitimacy Corinne is a 60-year-old biologist who works in the agro-food industry, is married and has adopted a child who is now 15 years old. The family lives in an individual house with a small garden, in a suburban town in the Southeast of France. She comes from an upper-class family with a father working in the industrial sector, a mother who was a homemaker and a younger brother, making up a family marked by the silence surrounding her “illness.” After “having been stretched out in every direction” to trigger her growth, the Turner syndrome diagnosis was given when she was 14, by a renowned Parisian doctor who did not deem it necessary to inform the young patient about her condition. However, her mother would immediately tell her, since, Corinne being an intellectually precocious child, she was about to pass her baccalaureate at age 15. This institutional threshold therefore influenced her illness trajectory (Corbin and Strauss, 1988). At the time, all the information she received centered on the absence of fertility: “no growth, no puberty, you won't have children.” Corinne discusses the violence of the diagnosis, and how her mother's view of her would from then on transform her into an “a-sexual being” trapped in the generational position of the “girl” who had no right to a sex-life, love, or motherhood. The hormone treatments, which were still in their early days in the 1970s, changed her body: she gained weight, and the presence of male hormones preoccupied her: “what am I going to turn into?” The stages in the treatment and her own transformations are both discussed in terms of the chemical products that acted on “[her] fabricated body”: the nandrolone phase, the trophobolene phase, and the estradiol-progesteron phrase. Her “accelerated puberty” was overwhelming and depressing. Biological and social stages succeeded one another—puberty, college, first job, very tardy first amorous relationships, adoptive motherhood—but she felt out of sync and often saw “an overcast horizon.” Being out of step with time, and having her intellectual qualities dissociated from her childlike appearance, contributed to amplifying the feeling of not “being in her place,” which constitutes the red thread of her narrative. “Considering that I was intellectually precocious and at the same time I was physically not developing, I cannot tell you how hard it was to deal with that gap. I was at university with people who were 19–20, had no breasts, and I was 1.35 meters tall. How do you find your place in that? How do you find your place among them? Some of them were already in a couple, anyway (knocks on the table). You ask yourself: what am I doing here?” In her testimony, her small stature is constantly tied to a femininity that is perceived to be defective (often expressed through references to the lack of breasts) and a sexuality that does not follow the same stages as the others: her very late first kiss, and her stormy sentimental relationships. The fact that physical development, belonging to an age and gender, and being in the “right” stage of life are all out of sync, manifests itself in a variety of areas. When she gained a lot of weight due to a bad dosage of her treatment, her mother would dress her in pregnant women's clothes. At work, she is not taken seriously. When she decided to adopt, the employee turned to her husband to place the baby in his arms, which strengthened her feeling of illegitimacy: “I felt illegitimate, completely (Pause). Because you are always asking yourself the question, whether you are legitimate. […] You never really feel completely legitimate, as a woman. I never feel completely legitimate as a woman.” The illegitimate body is, in Corinne's experience, entangled in age and gender non-appropriateness. The gap between body and time is here described as a difficulty to settle in the conventional stages of life (Settersten, 2002). As in the case for Nadine, it is possible to find a place at the margin or to occupy a void. That way, with friends, “we have a specific place, one that does not belong to anyone else. […] We are not a threat, neither for the boys nor for the girls. We're the good friends, we make everyone laugh, we accommodate everyone, we smile all the time and accept everything.” ## On the other side of reproductive life: Emilie and the menopausal turn-off These cases can be associated with a time of late diagnoses and limited treatments. However, the feeling of illegitimacy caused by a body that does not show the “right” markers of gender and age is still just as pervasive among our younger respondents. Moreover, being assigned to childhood is then doubled by another positioning “outside of any age,” namely menopause. During one of the first meetings we attended between Turner syndrome women and girls and gynecologists, one of the criticisms patients voiced was that the hormone substitution treatment mentions “women who could be 51,” and this is “very problematic.” This discussion, though it seemed banal at the time, would subsequently become quite meaningful. Emilie is a 36-year-old manager. She lives in a big city in the West of France and is now going through some great existential changes: she has new professional responsibilities, and she has entered a relationship in which she lives with her partner as a couple, after a deferred love-life and a “period of great mistrust toward men.” Her parents were farmers, and they both suffered from other chronic illnesses themselves. She was diagnosed at age 14 because of slow growth and a lack of any signs of puberty: “I never had let's say the physical passage … you progress in age, in life, but your body itself does not evolve, it is at a standstill.” Between 1999 and 2019, the treatment of the syndrome happened haltingly, with many stops and starts, and periods of waiting. In 2019, Emilie says she felt “ready without really being ready” to follow a treatment and it was only gradually that “in my head it had matured, well I'm soon going to be 35, I will have to do something. So I began the treatment in January 2020.” This interior time was a long, personal and rugged road that clashed with the rapid physical changes induced by the first growth hormone cycle, which had made her grow over 20 cm in a very short time. The fragmented way in which the treatment was then followed up was enhanced by the discovery that she suffers from epilepsy, which for a while relegated the syndrome to the background and with it, her relation to “Emilie the woman.” When she discusses this, she talks in the third person: “that part of me that is the syndrome, that part of Emilie, but Emilie as a woman, not as a person, was beginning to fall asleep … and also the relation with men, and with her own body.” The absence of a menstrual cycle, combined with a fragile bone structure and a disposition of the sexual organs that does not facilitate sexual relations, gave her the feeling that she had “the status of a woman in her menopause.” Emilie finds this idea “embarrassing” and “psychologically complicated” and associates it with the absence of a “woman's life”6: “I did not have a woman's life as such … I had pushed it away somewhat, see? I was Emilie, a person, but I was not … I was not a woman (She stops, tears in her eyes) this is sort of the hardest part.” These words revive widespread social ideas that associate the end of the menstrual cycle with a passage [Skultans ([1970] 2007)] and the decline of femininity (Lock, 1993). The lack of the life of a woman is here translated too into a difficulty to approach sexuality, and the grief over the loss of maternity and a feeling of being “mismatched” and “out of place.” Family gatherings or reunions with friends strengthen the feeling of being “the weak link, the ugly little duckling, the odd number.” Evoking menopause thus also stirs up another question, which is the one about filiation and one's genealogical position. The menopause for mothers and the first period for girls constitute a focal transmission point in contemporary France (Vinel, 2008). In many societies, the end of fertility means handing over one's legitimacy to procreate, even if that sometimes means, at least formally, to stop having sexual relations (Beyene, 1986; Delanoë, 2006). These rules, which can be transgressed through more or less explicit practices, aim to dissipate any possible confusion or ambiguity in the succession of generations. Mobilizing the image of the menopausal woman thus highlights another question: that of one's “place” in the order of generations, and of the contribution of women to the process of succession. ## Françoise, or the trouble in kinship The inability to have children has serious consequences for the family group, because the transfer of reproductive power constitutes a structuring element of generational succession. The infertility linked to the total absence of X chromosomes is not an individual question: it also puts the continuity of the lineage at risk. As Radkowska-Walkowicz and Maciejewska-Mroczek (2023: p. 6) pointed out with regard to Poland, mothers whose children have Turner syndrome see their daughter's infertility as “as an interruption of the intergenerational transfer of norms and values [that] jeopardizes women's hope for future grand-motherhood”. That makes it one of the consequences of Turner syndrome that are the most difficult to deal with within the family. The silence around what is often an open secret goes beyond differences linked to age, different diagnoses, or therapeutic and biographical trajectories. Indeed, the silence surrounding infertility feeds into and is fed by a concern around the ability to procreate that also applies to other members of the family, among whom it induces a desire to investigate their own chromosome types, or a fear of maternity-linked events like pregnancy or late periods. Relationships among siblings seem to be particularly troubled by sterility. The infertility of one is seen as a potential threat to the fertility of others. Moreover, because of the confusion between genetic and hereditary illnesses, the children of a brother or a sister can also be preoccupied that their aunt's sterility could spill over onto them. Lastly, the pregnancy of a sister, or the new fatherhood of a brother can bring about a lack of equality of place, and an asymmetry in generational ranking, which the women we interviewed experience with much apprehension. Françoise is 50; she has an intellectually oriented profession and lives in the south of France. She is very active in the local branch of a not-for-profit, she welcomes us, puts us into contact with people likely to be of value to the enquiry, and encourages us to pursue. As opposed to the many silences that have parsed our fieldwork, she underlines the importance of “talking, talking, talking” about it. Psychoanalysis helped her to become aware of the questions of sexuality, to which she returns often during our meetings and which just as often are avoided in collective discussions. “ The question of infertility is a major worry for us; as is the question of sexuality, but people don't talk about it. When you have learned to dissociate womanhood from maternity, and maternity from sexuality, you are OK. […] But, you don't have tits you don't get a guy; and to have words to explain all this, that helps.” Her experience of “being small” is told with a mixture of tenderness and concern. She is tender when she evokes her relationship with her brother when they were children: “He was very protective of me and his friends too, I was small, they never heckled me or pushed me around, they were very brotherly and adorable.” However, she was impatient, “worrying about a body that wouldn't grow” and this becomes clear from a dialogue with her youngest sister during a workshop the patients' organization: Françoise: Marie-Laure had her period, and I was expecting to be next, my sister had gotten ahead of me. Marie-Laure: we called her the “munchkin” (la puce). Françoise: yes, I had a grandma who was small. Marie-Laure: Françoise didn't see anything coming, and I thought, lucky her. Françoise: and I would cry […] At the time of the diagnosis, we knew something was not right with me, but until then I had my place among the siblings. Marie-Laure: and then I became a mom, in 1980 we did not know that it was genetic. Moreover, in all this I was worried about her, about my big sister. “Finding one's place” by comparing the changes of one's body to those that are happening to other family members, especially those of the same sex, constitutes one of the ways in which people take up their place in a family (Diasio, 2014). Turner syndrome, however, turns these relations upside down. The youngest sister's first period had an impact on Françoise's status as the oldest daughter. The birth order and its connection to gender are fundamental in the relationship between siblings, even in European societies where the prevailing social norm is to consider siblings as equal (Segalen and Ravis-Giordani, 1994; Fine, 2011). Françoise's infertility subsequently comes to blur her place in the family. Nevertheless, the birth of nieces and nephews is told with much humor as a way of settling back into a genealogical order and compensating for the lack of motherhood without suffering its burdens. ## The complex interplay of bodily markers In the experience of Nadine, the gap between chronological and age status, her small size and the childlike body discredit her and make her invisible as an adult woman. Corinne discusses how important it is to settle at the right moment of one's life course, in order to avoid a lack of legitimacy as a female adult, as a professional, and as a mother. Emilie's experience shows a tension between her life as a young woman and her menopausal status. The infertility and chaotic development of Françoise bring about disorder among the siblings and question her place in the generational rank. The four narratives presented here show how the relationship between body and time is not a well-oiled mechanism of biological data and social roles. They also reveal how the significant markers for the adjustment or misadjustment between body and time vary according to the moment of the life course, the social interactions and the particular temporality that is at stake. The choice of age and gender markers is far from arbitrary or coincidental. As women with Turner syndrome deal with the numerous ways in which the social making of age and gender is expressed: in their accounts, the height, the breast, the infertility, and the absence of menses are the most relevant phenomena. These, however, may be differently stressed according to the biographical and social context of the women's experience, and to the tactics (de Certeau, 1980) they deploy to “make do” with this genetic condition. Height is certainly the body feature that comes up most frequently. It is mentioned in relation to the altered rhythm of individual growth, it reveals the presence of the syndrome, and it underlines the difficulty of being part of a life stage, particularly in relation to peers. The misalignment between height and age thus refers to different temporalities: that of growing up, of the illness trajectory, and of the succession of age stages. However, stature materializes social, family, emotional and sexual relationships too. Height constitutes one of the first indicators brought to bear in measuring growth (Tersigni, 2015), but it is also a primordial expression of sexual dimorphism. It is the result of several genetic variants and social practices, such as unequal access to food resources (Guillaumin, 1992) or matrimonial choices, which have had an impact on genomes by selecting tall men and small women (Touraille, 2008). Height therefore constitutes a double operator in classifications of both gender and age. It indicates childhood, but also the fact of belonging to the female gender. In the case of women and girls with Turner syndrome, the question of height is ambivalent. Many of our respondents told us how their own mothers or other female members of the family are “small” and that this resemblance delayed diagnosis. The problem of height seems to become more acute when the child leaves the family circle, for instance at middle school, in their professional life, or in public spaces. In these situations, height is an element that materializes the disconnection between age and the fact of being hemmed into the children's category. As the word “small” signifies this double assignation, both to a physical dimension and to a state of social and psychological immaturity, the short stature embodies the asymmetrical and hierarchical relationship between adults and children. However, when these women evoke their love heterosexual relationships or interactions with male members of family (such as brothers or cousins), the fact of “being small” is less likely to be a source of discrimination. The “male taller norm” that dictates that men should be taller than women [Bozon, [1991] 2006] is still important in matrimonial arrangements and constitutes a materialization of the gender hierarchy. That is why in a group interview we saw smiles and laughter of agreement when one of our respondents said, “We Turner girls always go out with tall men!” A gender perspective then subverts the stigma of an “age-non appropriate” height. Therefore, in some narratives, the small stature is considered as part of the “normality” of gender-based dimorphisms, which is in turn reinforced by the “normality” of heterosexual relationships. This legitimization of small stature through heterosexual relationships may explain why the question of height comes up more painfully in the narrative of Nadine. She refers half-heartedly to her long condition of loneliness, repeatedly dodging the question of sexuality and love relationships, and justifying her single state by referring to infertility. Françoise's attitude, on the other hand, is different: in her account, the short stature is the trigger for the diagnosis, the proof that “something is going wrong,” but it also gives rise to a protective attitude on the part of her brother, which is recalled with tenderness. In her account, especially when she talks about her heterosexual relationships, infertility plays the main role. Infertility also gave rise to her psychoanalytical treatment in order to learn to dissociate femininity from motherhood. Should we see this difference as an effect of social class and level of education between two persons with very different social positions? Maybe, even if our data do not allow us to make definitive interpretations. The absence of breasts and infertility are particularly emphasized in sexual and love affairs, in interactions with friends, especially in youth transitions that put gender identity at stake (Fingerson, 2006), or in family relationships that involve intergenerational transmission. Nevertheless, while menstrual blood is often socially considered as a gendered matrix of experience, which permits “a shared subjectivity” (Pandolfi, 1991: p. 155) and a bodily mapping of gender difference (Prendergast, 2000), the girls and women we met undermine the importance of this fuzzy web. In fact, the attitude toward menstruation is closer than one might imagine to that of French adolescents encountered in other research (Mardon, 2009). The menarche is part of the definition of growing up and indicates a good state of health, nevertheless the presence of first menses is not a sufficient condition to become “women” (Diasio, 2014). However, the importance given to periods depends, more than other factors, on the moment in which these women and girls are interviewed. In adolescence, the onset of menarche is considered rather as a way of aligning with the experience of other girls and finding one's place among peers or in the family, whereas over time the presence of monthly menstrual blood may be considered as an unnecessary bother. However, menstruation may regain relevance in the context of a relationship with a male partner who may regard it “as a sign of womanhood”, as a 40 years old woman says. The approach to menstruation also depends on the generation to which the girls and women interviewed belong. For Corinne, who was treated in the 1980s with hormone therapies that were still in their trial and error stages, the onset of her first period came late and followed periods of “self-manipulation,” as she calls them, which were particularly painful. For Nadine, the menarche was experienced in misunderstanding and the passivity of a pill silently swallowed in the school canteen. These experiences invite us to situate the construction of a legitimate body in another temporality, which is the history of the discovery of the syndrome, its care and the evolution of treatments. A whole history of the patient and his or her participation in care is also intertwined with these transformations. Thus, the youngest girls in our population live in an age in which a new vision of children as present beings (Lee, 2001) leads doctors and parents to encourage their participation in therapeutic choices.7 Children and teens can then be consulted to know if and when to initiate treatment with sex hormones and to dissociate, for example, the growth of the breasts from the onset of menstruation. Physical transformations that happen over time are thus a part of a continuous and multidimensional process of biological and social facts which is open to interpretation, appropriation, and even conflict: “Over time, the combination can at times be harmonious, and at other times dissonant, and the individual is confronted with contradictory injunctions and “moral tensions” (Peatrick, 2003: p. 16). In the biological continuum, some markers will be socially selected (or not), physical qualities will be encouraged (or not), and certain practices valued, while others will be left by the wayside. We can therefore adopt, for age, the same statements that feminist biologist Fausto-Sterling [2000] dedicates to gender: “Our bodies are too complex to provide clear-cut answers about sexual difference. The more we look for a simple, physical basis for ‘sex', the more it becomes clear that ‘sex' is not a pure, physical category. What bodily signals and functions we define as male or female are already entangled in our ideas about gender.” ## Out of the obvious: The body against “nature” While the syndrome causes distress and a sense of lack of legitimacy, it also gives rise to another form of “presence in the world” (De Martino, 1948) that leads to a critical re-examination of hegemonic models of womanhood and their intersection with life stages. Radkowska-Walkowicz writes of an “emancipatory model of femininity” (Radkowska-Walkowicz, 2019: p. 138). Instead, we noted a reflexive stance: being aware of the normative power that the measurement of bodies obtained in contemporary society, the women and girls we met assiduously exert a distanced and critical gaze on their life path and the intertwining of bodily manifestations, age and gender positions.8 From one account to another, we find recurrent statements: “Turner girls think a lot,” “We have to think twice,” “We think more than those who have no disability.” Thus, Corinne claims that the syndrome brings “a different view of femininity, and of men. […] We think a lot about sexuality, about…well, about many things, about the couple, about the family, we think a lot. And young girls who have nothing [i.e., who have no syndrome], who haven't thought about it at all, who throw themselves into their life, their love life without having thought about it for a moment, well they don't ask themselves: “can something else exist, can we get there in another way? So they put on make-up, they flirt, they appeal and that's it.” In these words, it is less a matter of distancing themselves from forms of coquetry or so-called “feminine” aesthetic practices than of questioning their obviousness.9 Even if the experiences and illness trajectories are heterogeneous, we can observe the practice of a “bioreflexivity” (Memmi, 2003), which encourages women, at different moments of their existence, to question the influence of the syndrome on bodily states intertwined with age status and gender role. The stages of existence, which seem to occur “naturally” for those not affected by the syndrome, are submitted to a relentless evaluation to understand “how one stage succeeds another.” The body, its changes over time, and in particular its gendered expressions, such as the presence of menstruation or the development of breasts, are often described as “artificial,” “manipulated,” “weird,” “fake.” This lack of “naturality” flushes out the apparent correspondence between biological and social age and raises questions to understand “where we stand.” As Maëlle (21, assistant nurse) says, “growing up, becoming an adult, means growing in awareness of what Turner is, of what the relation to the syndrome is.” It is interesting to note that in the discourse the term “phase” is frequently used in place of the “age.” The phase eludes naturalization. It is constructed at the intersection of a type of treatment, such as the “nandrolone phase” mentioned by Corinne, a step in the care trajectory, an experience of the body, such as a slowed or accelerated stature growth, and an existential bifurcation: leaving one's parents' home, a break-up in love, entering or quitting a professional activity. Ages and their thresholds are then thought otherwise than in reference to the apparent concordance of biological transformations and social positions. Thus, if being assimilated to a menopausal woman when one is young is rather uncomfortable, as we have seen with Emilie's narrative, the onset of aging and the cessation of hormone treatments are interpreted as an alignment with the experience of other women in their menopause. Far from challenging the sense of womanhood10 the moment of entering an artificial menopause thus constitutes a sort of return to the circle of so-called “normality.” This process of demystification of the obvious leads our interviewees to unravel some dimensions of being a woman, which, from their point of view, are wrongly considered interdependent. “ The first thing people ask you when you talk about not being able to have children is if you have your period. It is weird, like it is a sign of fertility. That's why it's problematic […] you can have your period and be infertile […] for me it's decorrelated” (Ariel, 40 years old, employee). As we have seen in Françoise's narrative, a cascade of decorrelations challenges common associations that women with Turner syndrome affirm enduring in everyday life: the association between chromosomal sex and femininity, between femininity and seduction, between femininity and maternity, between maternity and sexuality, or between the menstrual cycle and fertility. Furthermore, women with Turner syndrome deal with the numerous ways of conceptualization of sexual polymorphism (chromosomic, gonadic, hormonal, morphological…) and split up the variable expressions of gender. Throughout the interviews, the researcher witnesses the attempts to unfold, as much as possible, these gender attributions and overlaps that are perceived as problematic. The questioning of the social and cultural evidence of “femininity” is often expressed through fighting metaphors. Becoming an adult woman is often associated with learning to fight, to struggle, to not be crushed, and above all to start talking, to no longer be suffocated by the blanket of silence in which one was enveloped (and cloaked) during childhood (Laiacona, 2019). Nadine's narrative is an example of this warrior attitude: “I'm a fighter and I'm going to do everything I can to…to succeed if I fail. I am really a fighter. It is long, but I fight, I fight. That is what is good [in Turner syndrome], I try to show that I am here (she pounds) that I can.” The words are often punctuated by gestures, such as the pounding of the fist on the table, which materialize these claims of fighting spirit. Becoming a woman means learning to “be seen” and “be heard,” where Turner's syndrome amplifies the invisibilization and minorization of women in relation to men, namely in the public space. Thus, one interviewee recalls how, faced with her repeated requests for a document to the administrative services, she would have had to ask her husband to intervene (which she did not) with “his big size and his big voice […] Not only are we women, we are also small and childlike!” This reflexive and fighting womanhood may constitute an example for other female relatives. The older women in our population point out how they establish special relationships with some nieces who turn to them for counsel or advice. They may be girls who have not yet had children, who are late in their first period, or who are complaining about family difficulties. These elective bonds reinstate our interviewees in a generational position. Thus, the body, in its multiple expressions, may be a resource for resistance to these classifications, and to naturalization. For instance, while Nadine's height inferiorizes her, her voice, whether through singing or through her rants at physicians (“I'm sure they heard me!”), comes to her rescue to allow her to assert herself with family, friends, and medical or professional milieu. In Françoise's case, her small stature and gender asymmetry generate a protective attitude on the part of her brother, her brother's friends and in the close ties which she entertains with her nephews and nieces. This means that the experiences of women who have Turner syndrome demonstrate how “the body is not only shaped by social relations, but also enters into their construction as both a resource and a constraint” (Prout, 2000: p. 5). Taking this approach avoids the tendency to fall into the double reductionism of naturalization or radical constructivism, both in age (ibidem) and in gender (Touraille, 2011; Raz, 2019). It thus enables us to reconsider the “materialization of sex and the sexuation of matter” (Kraus, 2000: p. 190). While society acts upon the “unfinished body” (Schilling, 1993; Remotti, 2003) through cultural practices and a range of technologies of the self (Foucault, 1988), the body acts on society as a matrix of possibilities and limitations: its changes, its troubles, the play of its matters and contingencies, solicit choices and practices, and provoke social responses, power positions, and resistance.11 ## Conclusion To be an adult with a small stature and a childlike morphology, to become a “complete” woman at an inappropriate age, to go from late growth into an accelerated onset of puberty caused by hormone treatments: suffering from Turner syndrome brings about desynchronizations between several times. The temporality of syndrome, treatments, growth and aging, and filiation disrupted by partial or total infertility, are not coincidental, and the gaps between them may give rise to experiences of liminality (Turner, 1969: p. 95) and stigma. The social norms that govern the “right” development of a body in time do not only establish appropriate age status. They are also at the heart of the ways in which gender is processed: it is not only a question of being at the right time, but of arriving there by negotiating the codes of a socially defined femininity. Lastly, the point is also to establish oneself in the long time of generations, whose renewal is threatened by infertility. The troubled body is thus defined by a disorder in time, which is also a disorder in status that produces a lack of legitimacy in several areas: family, work, relations with medical staff, friends, or lovers. This discloses the link between body and time as one of the last bastions where social and individual existences are naturalized and essentialized. The experience of women having Turner syndrome also reveals the obvious, elusive, undiscussed character of adulthood, and partially of womanhood, in contemporary French society, and the silent force of the entanglement of age, gender, and generation in the course of the life. Nevertheless, these narratives also reveal that in the thickness of the body and in its multiple manifestations, there are resources that go through the social evidence and put it into question. The body's materiality, with its complexity and singularity, plays against “nature”. The different markers used to denote age or gender only become meaningful and effective if they are situated in social situations and relations, including generational ones. For the girls and women we met, age and gender are not stable categories, they are rather forms of action that are expected in the context of a given relationship (Alès and Barraud, 2001): for example as daughter, sister, partner, aunt and so on. Thus, day by day, with inventiveness, reflexivity, and humor, these women measure themselves to the established categories, find a place in their interstices, and continue to struggle, brave and pugnacious, for their own legitimacy. ## Data availability statement The datasets presented in this article are not readily available in order to protect participant privacy. Requests to access the datasets should be directed to ND-nicoletta.diasio@misha.fr. ## Ethics statement The research is registered with the CNIL data protection service of the University of Strasbourg at the following address: https://cil.unistra.fr/registre.html#proc-555. The CNIL is the National Commission on Informatics and Freedom (CNIL), which in turn is advised by the Advisory Committee on Information Processing in Health Research (CCTIRS). The studies involving human participants were reviewed and approved by Comite d'Ethique des Facultés de Médecine, d'Odontologie, de Pharmacie, des Ecoles d'Infirmières, de Kinésithérapie, de Maïeutique et des Hôpitaux CE-2022-137. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. ## Author contributions The author confirms being the sole contributor of this work and has approved it for publication. ## Conflict of interest The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Alanen L.. “Generational order,”. The Palgrave Handbook of Childhood Studies (2010) 159-174 2. Alès C., Barraud C.. Sexe relatif ou sexe absolu? 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--- title: Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy authors: - Huandi Zhou - Lin Mu - Zhifen Yang - Yonghong Shi journal: Frontiers in Immunology year: 2023 pmcid: PMC10030848 doi: 10.3389/fimmu.2023.1113212 license: CC BY 4.0 --- # Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy ## Abstract ### Background The study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis. ### Methods GSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion \|log2FC\| >0.5 and adjusted $P \leq 0.05.$ KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients. ### Results A total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. “ chemokine signaling pathway” and “cell adhesion molecules” were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap. ### Conclusions CCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN. ## Introduction Diabetic nephropathy (DN), which accounts for about $20\%$–$40\%$ of diabetes mellitus (DM), represents the most frequent and devastating microvascular complications caused by DM and is the leading cause of end-stage renal disease (ESRD) worldwide, especially in developing countries [1]. It is characterized by injury to both the renal tubules and glomeruli. DN at the early stage can be reversed after treatment, while DN at the late stage will develop into ESRD. Early diagnosis and intervention might maximize the delay of disease progression, which is particularly important for clinical treatment. Traditionally, DN’s diagnosis depended on the presence of microalbuminuria. But growing evidence shows that many of the DN patients with microalbuminuria can return to normal urine, and only a few patients progress to proteinuria. In addition, in nearly one-third of DN patients with a normal range of albuminuria, a progressive decline in renal function like the glomerular filtration rate (GFR) was found. These indicate that it is not enough to detect proteinuria alone to monitor the incidence and progression of DN [2]. Besides, the decline in GFR without microalbuminuria was caused by renal tubular injury [3]. Unlike tradition, some studies have shown that the injury of renal tubules and renal interstitium may exist in the early stage of DN and play an important role in disease progression. In the past decade, our understanding of the pathogenesis of DN has expanded from glomerular to tubular pathobiology. Renal tubular injury has been increasingly recognized as an early characteristic of DN. Therefore, the study of relevant biomarkers targeting diabetic tubular injury can reveal the renal structure and dysfunction of patients with diabetes earlier, better monitor the progress of DN, and judge the prognosis [4, 5]. As an inflammation and immune-related disease, immune cells in renal tissues with DN, including resident and infiltrating immune-related cells and types, play a vital role in the occurrence and development of DN. Evidence accumulated from experimental and clinical studies indicates that renal inflammation plays a key role in determining whether renal injury progresses during diabetes. Increasing research reveals that many macrophages, lymphocytes, and mast cells exist in the kidney tissue of DN patients [6], which secrete many inflammatory mediators, cytokines, and oxygen free radicals that can directly or indirectly induce kidney tissue damage and accelerate the process of renal fibrosis. Predominantly, macrophages are one of the main infiltrating leucocytes found in diabetic kidneys and are associated with declining renal function in patients with DN [7]. There are high correlations between the aggregation of macrophages and the degree of glomerulosclerosis, proteinuria, SCR, and the presence of renal interstitial fibrosis [8]. Following this, T cell recruitment to kidney tissues in diabetic patients was correlated with the development and progression of DN at a degree of function second only to macrophages [9]. In addition, there was also growing evidence that even in the early stages of DN, B cells, neutrophils, and DCs accumulated in the glomeruli and interstitium, which played a remarkable regulatory role in the pathogenesis of DN. Significantly, it is of great value to evaluate the contribution of immune cells and explore key genes related to immune cells for clarifying the molecular mechanism underlying DN and developing novel and promising immunotherapeutic targets (9–11). In this study, gene expression data information from the GEO public database, GSE30529, GSE33925, and GSE104954, was merged to seek DEGs. Two datasets, GSE175759 and GSE47184, were used as validation datasets. After merging, functional analysis was conducted by GO, KEGG, and GSEA, and hub genes were identified by PPI and LASSO regression. Following this, ROC was performed to screen efficient diagnostic biomarkers in DN with a cut-off criterion of AUC >0.8 used both sensitivity and specificity >$75\%$. Next, ROC logistic regression was conducted to explore the predictive value of a combination of screened core biomarkers. Moreover, IHC was used to detect the expression levels of core biomarkers in 30 paracarinoma kidney tissues and 40 kidney tissues of patients with DN. Besides, CMap was used to seek promising compounds for treating renal tubulointerstitial injury in DN patients based on the enriched genes from functional analysis. Furthermore, ssGSEA was performed to calculate the immune-related contribution using three merged microarray datasets. Two algorithms, the Wilcoxon test and LASSO regression, were further applied to determine significant immune signatures with different infiltrates. Together, Spearman’s correlation was also used to analyze the correlation between biomarkers and significantly different infiltrates of immune cells. The findings will provide a new view of diagnostic signatures and immune therapeutic targets for DN. ## Data collection, preprocessing, and differential expression gene screening The flow chart of the study is presented in Figure 1. For screening DEGs related to tubulointerstitial injury in patients with DN, three datasets, GSE30529, GSE9325, and GSE104954, were retrieved from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. In addition, GSE175759 and GSE47184 were also downloaded from GEO for validation. The details are shown in Table S1. Figure 1: The flow diagram of this study. DEGs, differentially expressed genes; GSEA, gene set enrichment analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein–protein interaction; ROC, receiver operating characteristic. After the three test microarray datasets were downloaded from GEO, the probe expression matrixes were converted to gene expression matrixes using the platform annotation file. The values of probe IDs were averaged when genes with ≥1 probe and probes with multiple gene symbols were removed. Then, the three datasets were merged by the “inSilicoMerging” package and batch effects were removed using the method of Johnson et al. [ 12]. After performing batch normalization, the R package “limma” was used to screen DEGs between controls and renal tubulointerstium tissues of DN patients based on \|log2FC\| >0.5 and adjusted $P \leq 0.05.$ The heat map of DEGs was calculated and mapped using the “Pheatmap” R package. ## Gene ontology and kyoto encyclopedia of genes and genomes pathway analysis The R package “clusterProfiler” was used to perform GO and KEGG enrichment analyses on DEGs, respectively. R software “org.Hs.eg.db” was used for gene ID conversion, and the “goplot” package was used for calculating the Z score. The results were visualized by the R package “ggplot2.” $P \leq 0.05$ and p.adjust <0.05 were statistically significant. ## Gene set enrichment analysis GSEA was conducted to explore the differentially activated biological pathways between the control and DN groups. The 82 samples in the merged datasets, which belonged to two groups of 37 control samples and 45 DN samples, underwent enrichment analysis using GSEA software (GSEA_4.2.3) on the Java version 8.0 platform. The reference set, gene set c2.cp.kegg.v7.5.symbols.gmt, was obtained from the GSEA official website (http://www.gsea-msigdb.org/gsea/index.jsp) to calculate the enrichment score (ES). It was set at 1,000 permutations, and the gene size was from 5 to 500. A normalized $P \leq 0.05$ and a false discovery rate (FDR) <0.25 were set as significant thresholds. ## Connectivity map analysis The intersection genes from the intersected pathways between KEGG-DEGs and GSEA-KEGG analysis were uploaded to the query tool of the Cmap online platform (https://clue.io/query) to predict promising compounds that may improve tubulointerstitial lesions in DN patients. ## Screening hub genes The STRING platform (http://string-db.org) was used to conduct a protein–protein interaction (PPI) network with medium confidence (score >0.4). Then, the interaction file downloaded from the STRING platform was further analyzed using Cytoscape version 3.9.1 software. The CytoHubba [“CytoHubba: identifying hub objects and subnetworks from complex interactome,” BMC Systems Biology], a Cytoscape software plugin, was used to calculate the node genes using the top five algorithms: MCC, DMNC, MNC, Degree, and EPC. Subsequently, hub genes were screened based on the intersection among the top 60 node genes of each algorithm. ## Receiver operating characteristic curve In public data, ROC analysis was performed by MedCalc software for Windows 20.1.0. The area under the curve (AUC) value >0.8 and both sensitivity and specificity >$75\%$ were considered to have better diagnostic effectiveness. ## Immunohistochemical staining A total of 40 patients’ paraffin-embedded samples that were histopathologically and clinically diagnosed as DN were collected at the Second Hospital of Hebei Medical University. A total of 30 samples of paracancerous tissues from normoglycemic renal cancer patients without a history of DN were obtained as normal controls. For the study, patients’ informed consent and approval were obtained from the Ethics Committee of the Second Hospital of Hebei Medical University. The expression of three biomarkers was detected by the IHC method as described in the instructions of ZSGB-BIO (PV-9000, Beijing, China). The immunohistochemical staining score was based on previously published articles [13, 14]. The staining estimation was assessed by the ImageJ software (National Institutes of Health). ## Single-sample gene set enrichment A total of 29 immune-related cells and types, representing immune cell species, immune function, and immune-related pathways, were obtained [15]. Then a ssGSEA was performed to analyze the enrichment of 29 immune signatures in each sample in the expression file of the merged dataset using the “GSVA” R package. ## Screen significant differential immune cells Based on the infiltration of 22 immune cells in control and DN samples calculated by the CIBERSORT algorithm, two methods, the Wilcoxon test and the least absolute shrinkage and selection operator (LASSO) logistic regression, were performed to screen the differential immune signatures. LASSO was conducted with the “glmnet” package. ## Correlation analysis between biomarkers and significant differential immune signatures The analysis of correlations between biomarkers and significant differential immune signatures was conducted with spearman analysis using the Sangerbox platform, an online tool (http://www.sangerbox.com/tool) [16]. ## Statistical analysis Statistical analyses were conducted with R and GraphPad Prism 8.0 (GraphPad Software, Inc.). The correlation between three biomarkers and clinical indicators was performed by GraphPad Prism software (8.0) using Pearson or Spearman analysis based on whether they satisfied the normal distribution or not. The unpaired t test or Mann–Whitney U test was used to evaluate the differences between two groups. ROC was done by MedCalc software (20.1.0) to detect the diagnostic efficiency of biomarkers along with calculated AUCs to evaluate the efficacy of core genes in diagnosing DN. All tests were two-tailed, and the definition of statistical significance is $p \leq 0.05.$ ## Identifying the DEGs involved in tubulointerstitial injury between control and DN samples According to the research flow chart (Figure 1), three datasets, GSE30529, GSE9325, and GSE104954 were downloaded from GEO, and a total of 82 samples (37 controls and 45 DN samples, including 12 controls and 10 DN samples from GSE30529, four controls and 18 DN samples from GSE99325, and 21 controls and 17 DN samples from GSE104954, respectively) containing 10,635 genes (Figure 2) were merged to screen DEGs. The PCA (Figure 2), density (Figure 2), and boxplot (Figure 2) diagrams showed that the batch effect of the merged data was better removed. After that, differential expression genes (DEGs) between control samples and DN samples were calculated and screened using the “limma” R package with adjusted $P \leq 0.05$ and \|log2FC\| >0.5. A total of 509 DEGs were obtained, which included 338 upregulated genes and 171 downregulated genes. The result was visualized by a volcano map (Figure 2), and the top 20 upregulated and top 20 downregulated DEGs were shown in the heatmap (Figure 2). Figure 2: Data preprocessing and DEG screening. (A) Upset graph was conducted to obtain the intersection genes in the merge of GSE30529, GSE99325, and GSE104954. Three datasets showed an overlap of 10,635 genes. (B–D) The PCA (B), density (C), and box plot (D) figures before or after removing batch. (E) The final DEGs were visualized by the volcano map, Log2FC >0.5, and adj.P <0.05. (F) The top 20 upregulated and top 20 downregulated DEGs were visualized by the heatmap. Red, upregulated differential genes; blue, downregulated differential genes. ## Functional analysis To explore the mechanism related to tubulointerstitial injury in DN patients, after being converted into gene ID, 509 DEGs were analyzed using GO analysis containing BP (biology process), MF (molecular function), CC (cellular component), and KEGG analysis. GO annotation analysis showed a significant correlation with the biological activity of immune cells, for example, “leukocyte cell–cell adhesion,” “T-cell activation,” “neutrophil activation involved in immune response” in BP, “MHC protein complex,” “MHC class II protein complex” in CC, “integrin binding,” “chemokine receptor binding,” “cytokine binding” in MF, and so on (Figures 3A). Coincidentally, KEGG analysis of DEGs showed an apparent correlation with immune system and immune disease-related signaling pathways, for example, “complement and coagulation cascades,” “rheumatoid arthritis,” “chemokine signaling pathway,” “autoimmune thyroid disease,” “antigen processing and presentation,” and so on (Figures 3B). Moreover, based on the expression profiles of 37 controls and 45 DN samples, GSEA was further employed to explore the gene pathways enriched in different control and DN groups using an annotated gene set (c2.cp.kegg. v7.5.1. symbols) and revealed two intersected pathways with KEGG-DEGs: “chemokine signaling pathway” (NES = 1.48, $$P \leq 0.048$$, FDR = 0.230), and “cell adhesion molecules” (NES = 1.48, $$P \leq 0.038$$, FDR = 0.233), which is shown in Figure 3. Subsequently, the intersection genes from the two intersected pathways both in KEGG-DEGs and GSEA-KEGG were calculated, and the correlation of each gene was visualized in the circle graph (Figure 3). It was shown that 36 intersection genes had a conspicuous positive correlation. Figure 3: Functional analysis. (A, B) The bubble graph (A) and circle graph (B) of GO analysis for BP, CC, and MF, respectively, based on DEGs. (C, D) The bar plot (C) and circle charts (D) of KEGG analysis based on DEGs. (E) Multi-GSEA plot showing the intersection pathways between KEGG analysis of DEGs and GSEA-KEGG-enriched gene sets in the DN group. (F) The circle chart shows the correlation of intersection genes between KEGG analysis of DEGs and GSEA–KEGG analysis. ## Identification of hub genes related to renal tubulointerstitial injury in DN group To identify the hub genes from DEGs, a PPI network was carried out, and the node relationship among genes was obtained from the STRING tool. Then, the score of each node gene was calculated depending on the top five algorithms (MCC, DMNC, MNC, Degree, and EPC) in CytoHubba, a plug-in of Cytoscape software. The top 60 node genes of each algorithm were intersected to screen hub genes, of which a total of 16 genes were selected, such as LCP2, CXCL1, CD53, CXCL12, VCAM1, TLR1, CD1C, CSF1R, FCER1G, FCGR2B, CD48, LY86, SELP, CCR2, CX3CR1, and IL10RA (Figures 4A). Furthermore, LASSO regression was conducted to determine the hub genes, and then six genes were screened, such as CCR2, CX3CR1, CXCL1, CXCL12, SELP, and TLR1 (Figures 4C). Comparing with control samples, all six genes were upregulated in the DN group in the merged dataset, as shown in the violin chart (Figure 4) and the heatmap (Figure 4). Figure 4: Identification of hub genes related to renal tubulointerstitial injury in DN. (A, B) Five algorithms in CytoHubba, a plug-in of Cytoscape software, to screen hub genes. The Venn diagram (A) and the Upset graph (B) of intersected genes were analyzed by five algorithms: MCC, DMNC, MNC, Degree, and EPC. A total of 16 genes were screened, such as LCP2, CXCL1, CD53, CXCL12, VCAM1, TLR1, CD1C, CSF1R, FCER1G, FCGR2B, CD48, LY86, SELP, CCR2, CX3CR1, and IL10RA. (C, D) LASSO regression was conducted to screen further the hub genes, and six genes were screened, such as CCR2, CX3CR1, CXCL1, CXCL12, SELP, and TLR1. (E) Wilcoxon test of six hub genes in control and DN samples. (F) Six hub genes were visualized by the heatmap. ## Diagnostic effectiveness of six hub genes and validation of screened core genes To validate the diagnostic of six hub genes, ROC was conducted to calculate the AUC, specificity, and sensitivity. As shown in Figure 5, all six hub genes had an efficient diagnostic value with an AUC >0.75. Especially for CCR2, CX3CR1, and SELP, the three core genes were screened as biomarkers of DN with AUC >0.8, and both sensitivity and specificity >$75.00\%$. Amazingly and meaningfully, the combined AUC of CCR2, CX3CR1, and SELP reached an incredible 1.000 ($95\%$ CI 0.956–1.000), with sensitivity = $100\%$ and specificity = $100\%$ (Figures 5B). To identify the diagnostic effectiveness of the three biomarkers and their combination, two datasets, GSE175759 and GSE47184, were used to conduct external validation. As shown in Figures 5D, each of the three core biomarkers had significantly upregulated expression in DN samples compared to controls in both GSE175759 and GSE47184 (Figures 5D). As shown in Figures 5E, the AUC values of CCR2, CX3CR1, SELP, and the combination in GSE175759 were 0.939 ($95\%$ CI 0.766–0.996), 0.939 ($95\%$ CI 0.766–0.996), 0.909 ($95\%$ CI 0.725–0.986), and 1.000 ($95\%$ CI 0.863–1.000), and the AUC values of CCR2, CX3CR1, SELP, and the combination in GSE47184 were 0.931 ($95\%$ CI 0.737–0.995), 0.917 ($95\%$ CI 0.718–0.991), 0.931 ($95\%$ CI 0.737–0.995), and 1.000 ($95\%$ CI 0.846–1.000), respectively (Figures 5E). Figure 5: ROC analyze the diagnostic value of six hub genes and external validation of screened biomarkers. (A) ROC analyzing the diagnostic value of six hub genes, three core genes of which were screened as biomarkers of DN based on the AUC >0.8, and both sensitivity and specificity >75.00% in merged datasets. (B) ROC analysis of the combination model based on three core genes in merged datasets. (C) ROC analysis comparing the diagnostic effectiveness among three core genes and the combination model in merged datasets. (D, E) The expression validation (D) and ROC analysis validation (E) of core genes in GSE175759. (F, G) The expression validation (F) and ROC analysis validation (G) of core genes in GSE47184. (H) IHC staining examined the expression of three core biomarkers in 30 paracarinoma kidney tissues (right) and 40 kidney tissues of patients with DN (left), (scale bar, 100 μm). (I) Expression statistics of IHC staining in 30 paracarinoma kidney tissues and 40 kidney tissues of patients with DN, *p <0.001 vs control. (J) ROC analysis comparing the values of three core genes and the combination model in 30 paracarinoma kidney tissues and 40 kidney tissues of patients with DN. (K) The correlation among CCR2, CX3CR1, and SELP in 40 kidney tissues of DN patients. (L) The correlation between three biomarkers and clinical indicators in 30 kidney tissues of DN patients. In addition, to further explore the role of three core biomarkers in DN, protein expression levels detected by IHC were performed on 30 renal cancer paracancerous tissues and 40 DN patients’ biopsy tissues. The clinical characteristics of DN patients are summarized in Table 1. According to the degree of 24 h-proteinuria, DN patients were divided into two groups based on the degree of overt proteinuria ($$n = 16$$, <3.5 g/24 h) and heavy proteinuria ($$n = 14$$, >3.5 g/24 h). There was no difference in diabetes history, age, BMI, FBG, SBP, DBP, urea nitrogen, HbA1c, UA, TC, or LDL levels among the two groups. Additionally, the values of 24-h urinary protein, Scr, and TG in the heavy proteinuria group were significantly higher than those in the overt proteinuria group. In contrast, Hb, Alb, and eGFR levels in the heavy proteinuria group were dramatically decreased compared with overt proteinuria ($p \leq 0.05$) (Table 1). As a result, CCR2, CX3CR1, and SELP were strongly stained by IHC in the DN group, especially in the renal tubules (Figures 5H). The further ROC confirmed the efficient diagnostic capabilities of all three biomarkers, CCR2, CX3CR1, and SELP. Similarly, the combination model showed the highest diagnostic efficiency for DN (AUC = 1.000, $95\%$ CI 0.949–1.000, sensitivity = $100.00\%$, specificity = $100.0\%$, $p \leq 0.0001$) (Figure 5). Furthermore, there were remarkable positive correlations among CCR2, CX3CR1, and SELP (CCR2 vs CX3CR1, $R = 0.5208$, $$p \leq 0.0006$$; CCR2 vs SELP, $R = 0.3354$, $$p \leq 0.0344$$; CCR2 vs CX3CR1, $R = 0.8678$, $p \leq 0.0001$) in the DN group (Figure 5). As shown in Figure 5, according to information on clinical parameters, 30 out of 40 DN patients were used to further analyze the correlation. There were substantial positive connections between CCR2 and age ($R = 0.4731$, $$p \leq 0.0083$$), or Scr ($R = 0.3647$, $$p \leq 0.0475$$), and a significant negative correlation between CCR2 and Hb (R = −0.4774, $$p \leq 0.0076$$), or Alb (R = −0.3896, $$p \leq 0.0333$$), or eGFR (R = −0.4350, $$p \leq 0.0163$$). We also found that CX3CR1 positively correlated with urea nitrogen ($R = 0.4176$, $$p \leq 0.0217$$), 24-h urinary protein ($R = 0.3762$, $$p \leq 0.0405$$), or Scr ($R = 0.5158$, $$P \leq 0.0035$$), negatively correlated with Hb (R = −0.4587, $$p \leq 0.0108$$), or eGFR (R = −0.5525, $$p \leq 0.0015$$). SELP had a confirmed positive correlation with Scr ($R = 0.4052$, $$p \leq 0.0263$$) and a negative connection with eGFR (R = −0.3788, $$p \leq 0.0390$$) (Figure 5). Therefore, CCR2, CX3CR1, SELP, and their combination were capable of diagnosing control and DN with excellent specificity and sensitivity, especially for the combination. Table 1 \| Parameter \| Overt proteinuria (n = 16) \| Heavy proteinuria (n = 14) \| P-value \| \| --- \| --- \| --- \| --- \| \| Diabetes history (years) \| 7.00 ± 3.95 \| 8.71 ± 6.74 \| 0.3954 \| \| Age \| 47.69 ± 8.48 \| 51.50 ± 11.69 \| 0.311 \| \| BMI (kg/m2) \| 27.08 ± 4.23 \| 27.23 ± 3.52 \| 0.9391 \| \| FBG (nmol/L) \| 8.20 ± 3.48 \| 10.17 ± 5.61 \| 0.2617 \| \| SBP (mmHg) \| 143.56 ± 22.46 \| 152.57 ± 23.58 \| 0.2934 \| \| DBP (mmHg) \| 91.44 ± 13.76 \| 92.36 ± 17.52 \| 0.8733 \| \| Urea nitrogen (mmol/L) \| 7.71 ± 2.42 \| 9.69 ± 4.36 \| 0.107 \| \| 24-h urinary protein \| 3.27 ± 4.61 \| 6.87 ± 1.83 \| 0.0002 \| \| HbA1c (%) \| 8.20 ± 1.86 \| 9.53 ± 2.58 \| 0.1131 \| \| Hb (g/L) \| 126.13 ± 20.82 \| 103.86 ± 26.04 \| 0.0147 \| \| Alb (g/L) \| 36.81 ± 6.97 \| 28.23 ± 6.64 \| 0.0121 \| \| Scr (μmol/L) \| 129.00 ± 113.20 \| 185.86 ± 141.16 \| 0.0393 \| \| eGFR (ml/min/1.73 m2) \| 66.81 ± 26.15 \| 41.81 ± 18.62 \| 0.006 \| \| UA (mmol/L) \| 387.38 ± 95.61 \| 406.14 ± 90.01 \| 0.5859 \| \| TC (mmol/L) \| 4.71 ± 1.52 \| 5.78 ± 1.59 \| 0.0698 \| \| TG (mmol/L) \| 1.79 ± 0.55 \| 2.70 ± 1.37 \| 0.0198 \| \| LDL (mmol/L) \| 3.2 ± 1.37 \| 3.86 ± 1.75 \| 0.2627 \| ## Immune-related cells and type infiltration difference in renal tubulointerstitial tissues between control and DN tissues Since KEGG and GO analysis of DEGs were both enriched to be related to immune cells, the ssGSEA algorithm was applied to evaluate the immune signature infiltration difference so that we could explore the immune microenvironment of DN and further clarify immune signatures closely related to renal tubular injury in patients with diabetes nephropathy. Three datasets, including 37 control and 45 DN samples, were selected to conduct a single-sample gene set enrichment analysis based on a gene set including 29 immune-related cells and types. As shown in Figure 6, the heatmap revealed that there was more evident immune infiltration in renal tubular tissue in the DN group than in controls (Figure 6). Following this, the correlation of 29 immune-related cells and types was estimated. Preeminently, general positive correlations were observed among immune signatures (Figure 6). Especially for some immune-related cells and types, including CCR, checkpoint, cytolytic activity, HLA, inflammation-promoting, macrophages, MHC class I, parainflammation, pDCs, T-cell co-inhibition, T-cell co-stimulation, and TIL, highly positive correlations with a correlation coefficient (cor) >0.8 were found. For example, CCR had a strongly positive correlation with checkpoint, cytolytic activity, HLA, inflammation-promoting, MHC class I, neutrophils, parainflammation, pDCs, T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. Checkpoints were significantly positively related to cytolytic activity, HLA, inflammation-promoting, MHC class I, parainflammation, pDCs, T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. There were positive correlations between cytolytic activity and HLA, or inflammation-promoting, or MHC class I, or parainflammation, or pDCs, or TIL. HLA had positive correlations with inflammation-promoting, parainflammation, pDCs, T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. There were positive correlations between inflammation-promoting MHC class I, parainflammation, pDCs, T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. Macrophages were positively correlated with TIL. MHC class I had a positive correlation with parainflammation, pDCs, TIL, and type I IFN responses. Parainflammation was positively correlated with pDCs, T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. There were positive correlations between pDCs and T-cell co-stimulation, TIL, type I IFN response, and type II IFN response. T-cell co-stimulation was positively correlated with TIL, and T-cell co-inhibition. TIL was positively correlated with type I IFN response, and type II IFN response (Figure 6). In sharp contrast, there were declines or reverse correlations among the 29 immune-related cells and types in the control group (Figure S1). Figure 6: Immune-related cells and types of infiltration difference in renal tubulointerstitial tissues between control and DN tissues. (A) The heatmap of the composition of immune signatures in control and DN samples. (B) Correlation analyses among the immune signatures calculated by ssGSEA in the DN group: red, positive correlation; blue, negative correlation. P <0.05, P <0.01, P <0.001, **P <0.0001. Furthermore, two kinds of algorithms, the Wilcoxon test and LASSO regression, were applied to identify the most related immune signatures. As shown in Figure 7, 20 kinds of immune-related cells and types, namely APC co-stimulation, CCR, CD8+ T cells, checkpoint, cytolytic activity, HLA, inflammation-promoting, macrophages, MHC class I, neutrophils, parainflammation, pDCs, T-cell co-stimulation, Tfh, Th1 cells, Th2 cells, TIL, Treg, type I IFN response, and type II IFN response, differed significantly between DN and control group based on the Wilcoxon test (Figure 7). In addition, the results from LASSO regression with lambda. min = 0.02789 presented 10 types of immune signatures with $p \leq 0.05$, such as aDCs, APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, iDCs, macrophages, mast cells, MHC class I, and parainflammation (Figure 7). After being intersected, seven significantly different types of immune signatures were extracted, namely APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation (Figure 7). Compared with the control group, there were higher infiltrations of APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in DN tissues. Figure 7: Identifying the significantly different infiltrates of immune-related cells and types related to renal tubulointerstitial injury in DN. (A) The violin diagram of 20 types of significant differential immune signatures analyzed by the Wilcoxon test. (B) The LASSO regression of immune signatures in control and DN samples. (C) The Upset diagram about intersected immune cells between Wilcoxon and LASSO, which showed seven kinds of immune signatures, such as APC co-stimulation, CD8+ T cell, checkpoint, cytolytic activity, macrophages, MHC class I, and proinflammation, were significantly different between control and DN samples. P < 0.05, P < 0.01, P < 0.001, **P < 0.0001. ## Correlation between biomarkers and differential immune signatures in DN patients The correlation between three core biomarkers (CCR2, CX3CR1, and SELP) and seven differential immune-related signatures (APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation) was analyzed by Spearman. There were general positive correlations between three biomarkers and seven immune signatures in the DN group (Figure 8), and weakened or opposite correlations among them in controls (Figure 8). Besides, a strong positive correlation among these three biomarkers was also observed. CCR2 was positively correlated with CX3CR1 (cor = 0.83, $p \leq 0.0001$) and SELP (cor = 0.54, $p \leq 0.0001$), and CX3CR1 had a positive correlation with SELP (cor = 0.59, $p \leq 0.0001$) (Figure 8). It was resulting that CCR2 had a significantly positive correlation with all of these seven types of immune signatures, especially for checkpoint (cor = 0.80, $p \leq 0.0001$), cytolytic activity (cor = 0.76, $p \leq 0.0001$), macrophages (cor = 0.72, $p \leq 0.0001$), MHC class I (cor = 0.73, $p \leq 0.0001$), and parainflammation (cor = 0.81, $p \leq 0.0001$) (Figure 8). CX3CR1 significantly and positively correlated with five out of these seven types of immune signatures, such as checkpoint (cor = 0.75, $p \leq 0.0001$), cytolytic activity (cor = 0.80, $p \leq 0.0001$), macrophages (cor = 0.64, $p \leq 0.0001$), MHC class I (cor = 0.71, $p \leq 0.0001$), and parainflammation (cor = 0.74, $p \leq 0.0001$) (Figure 8). SELP had significant positive correlations with six out of these seven kinds of immune-related cells and types, especially checkpoint (cor = 0.67, $p \leq 0.0001$), cytolytic activity (cor = 0.67, $p \leq 0.0001$), MHC class I (cor = 0.62, $p \leq 0.0001$), and parainflammation (cor = 0.68, $p \leq 0.0001$) with high correlations (Figure 8). Figure 8: Correlation between biomarkers and differential immune signatures in DN patients. (A) Spearman analysis of three core biomarkers and seven significant differential immune signatures in the DN group. (B) Spearman analysis of three core biomarkers and seven significant differential immune signatures in the control group. (C) The correlation among CCR2, CX3CR1, and SELP in the DN group. (D–F) Significant and strong positive correlation between biomarkers and immune signatures. R >0.6 & P <0.05. DN, diabetic nephropathy; P <0.05, P< 0.01 p <0.001. ## Exploration of potential compounds to improve diabetic tubulointerstitial injury by CMap analysis To research the promising drugs for treating the tubulointerstitial injury in DN patients, 36 intersected genes from two intersected pathways were uploaded to CMap, which showed the top 20 negative correlation compounds based on median score in Figure 9, indicating that these could be reversing the gene alterations in different cell lines (Figure 9). As shown in the heatmap, tetrabenazine (vesicular monoamine transporter inhibitor), dilazep (adenosine reuptake inhibitor, calcium channel antagonist, platelet aggregation inhibitor), tomelukast (leukotriene receptor antagonist), KIN001-220 (Aurora kinase inhibitor), azacytidine (DNA methyltransferase inhibitor, antimetabolite, DNA methylase inhibitor, DNA synthesis inhibitor, RNA synthesis inhibitor), umbelliferone (carbonic anhydrase inhibitor, cyclooxygenase inhibitor), lysylphenylalanyl-tyrosine (heparin activation inhibitor), memantine (glutamate receptor antagonist, glutamate release inhibitor), phensuximide (anticonvulsant), and BIBU-1361 (EGFR inhibitor) ranked in the top 10. Figure 9: The promising compounds for tubulointerstitial injury in DN analyzed by CMap. The 36 intersection genes in two intersected pathways between KEGG-DEGs and GSEA-KEGG analyses were analyzed as potential compounds by the Query tool from the Cmap online platform (https://clue.io/query). The top 20 negative compounds were shown in the heat. ## Discussion Diabetes mellitus (DM) is an endocrine and metabolic disease that can lead to dysfunction of all organs in the body, of which DN is a highly prevalent and serious chronic microvascular complication in patients with diabetes. About $20\%$–$40\%$ of diabetes can progress to DN [1]. It is a leading contributor of DN to kidney failure in developed countries [17, 18]. Therefore, early diagnosis and intervention in DN are particularly important. At present, renal biopsy histopathology is still the gold standard for diagnosing DN, but it is traumatic and limited in clinical application. Urinary microalbumin (UmAlb) is a widely used indicator for diagnosing DN. However, about $30\%$–$45\%$ of type 2 diabetes patients were observed to have reduced GFR with no increase in UmAlb (>30 mg/g) [19]. In addition, the common comorbidities of T2DM, such as hypertension or obesity, may also damage the glomerular filtration barrier, leading to an increase in UmAlb, suggesting that the sensitivity and specificity of UmAlb in diagnosing DN are insufficient. So far, it has been hard for us to accurately predict which one with diabetes will develop DN. Consequently, searching for novel and capable biomarkers for diagnosing DN is of great significance for early treatment and improving the prognosis of patients. In the development of DN, tubulointerstitial injury plays a pivotal role, even prior to glomerular injury. It is characterized by renal tubular atrophy and tubulointerstitial fibrosis, which are considered the main pathological features of renal dysfunction in patients with DN. Tubulointerstitial injuries are more appropriate and useful in predicting renal disease status in DN patients than glomerular or vascular damage. Momentously, more attention should be given to the biomarkers of renal tubule lesions, which are of great value to the diagnosis and treatment of patients in the early stages of DN. Growing studies showed that biomarkers based on renal tubules can early reveal the renal structure and dysfunction of diabetes patients and better monitor the progress of DN and judge the prognosis, such as kidney injury molecule-1(KIM-1), β2-microglobulin (B2M), N-acetyl-β-D-glucosaminidase (NAG), osteopontin (OPN), etc. KIM-1, a transmembrane glycoprotein of proximal tubular epithelial cells of the kidney, cannot be detected when the kidney is structurally or functionally normal, but it can be significantly upregulated with tubular damage. Therefore, KIM-1 can be used as a potential biomarker for proximal tubule injury [20]. B2M is a small subunit of major histocompatibility class I molecules that exists in all nucleated cells. B2M is fully filtered at the glomerulus and then almost completely reabsorbed in the proximal tubule [20]. NAG is a hydrolase widely distributed in organs. NAG, with a molecular weight of 130,000, is not easy to filter by the glomerulus. However, when the renal convoluted tubules are damaged, lysozyme will release a large amount of NAG, resulting in a significant increase in NAG in urine [21]. OPN, one of the proinflammatory cytokines, was observed to be upregulated in the kidneys of diabetic animals and patients with nephropathy [22]. The association of these biomarkers with DN has been found in many studies. There were different capabilities to detect DKD. One study showed an AUC of 0.68 for KIM-1 in diagnosing moderately increased albuminuria [23]. B2M had moderate to low AUCs of 0.58 [24], 0.652 [25], and 0.792 [26] to predict early DN in three separate studies. According to the results of two studies, NAG exhibited modest predictive ability for assessing renal tubulointerstitial injury with AUCs of 0.636 [24] and 0.783 [27]. OPN exhibited qualified performance with AUCs of 0.692 [28] and 0.73 [29], and did not associate with albuminuria levels, $p \leq 0.05$ [30]. Recently, with the development and widespread application of the human genome project, technologies such as transcriptomics, proteomics, and metabolomics have emerged in succession. Bioinformatic analysis has been a new way to identify novel genes and early diagnosis/prognosis biomarkers for many diseases [4, 31, 32]. Liu et al. [ 33] found that LUM, ELN, and FMOD had the potential abilities to diagnose DN with AUCs of 0.897, 0.624, and 0.983, respectively. A negative correlation with eGFR in R of −0.658, −0.176, and −0.628, respectively, in the GSE30528 dataset. Zhou et al. [ 4] identified CAV1, COL1A2, VWF, FN1, and ITGB2 as having an advantage in assessing DN with an AUC >0.8. Many other studies also screened a series of biomarkers that increased expression in DN compared to controls, and there was certain relevance between biomarkers and clinical parameters like eGFR, ACR, and so on (34–37). All the findings lacked experimental validation and correlation analysis with immunity. In this article, three DN expression profile datasets from GEO were downloaded and merged. After removing the batch effect, 509 DEGs were obtained with a cut-off standard at \|log2FC\| >0.5 and adjusted $P \leq 0.05.$ According to the results of functional analysis, both GO and KEGG analysis were tied to the immune system, such as “leukocyte cell–cell adhesion,” “T-cell activation,” “MHC protein complex,” “MHC class II protein complex,” “integrin binding,” “chemokine receptor binding,” “rheumatoid arthritis,” “chemokine signaling pathway,” “antigen processing and presentation,” and so on, suggesting a high correlation between the development of renal tubular injury in DN and the infiltration differential of immune cells. Following this, a GSEA algorithm using an KEGG-annotated gene set based on 37 controls and 45 DN sample expression profiles was performed to further identify the key pathways. After comparing with KEGG-DEGs, the two crossed pathways, namely “chemokine signaling pathway” and “cell adhesion molecules,” were determined, including 36 intersected genes with an obvious positive correlation. Finally, based on PPI network analysis and strict screening using two independent methods, LASSO regression and ROC, three core genes, CCR2, CX3CR1, and SELP, were identified as biomarkers with efficient diagnostic capability for DN, which also included the above 36 intersected genes from the two crossed pathways. No matter the training data or validated data, there were high AUC, sensitivity, and specificity in the diagnosis of DN in both the three independent factors and their combination. Amazingly and surprisingly, it merits our attention that the AUC of the combination of CCR2, CX3CR1, and SELP could reach sensitivity = $100\%$, and specificity = $100.00\%$ in both merged training datasets, GSE30529–GSE99325–GSE104954, two independent validated datasets, GSE175759 and GSE47184, and IHC detection of biopsy tissues. CCR2, namely C–C motif chemokine receptor 2, located on chromosome 3, is a member of the G protein-coupled receptor (GPCR) superfamily and a receptor of monocyte chemoattractant proteins (MCP) 1–4, which are chemical inducers of proinflammatory response [38]. CCR2 exists on the surface of a variety of immune cells and can guide immune cells to reach inflammatory and tumor sites. By connecting with ligands, including MCP-1, CCR2 recruits the movement and activation of inflammatory cells. As is well known, MCP-1, the main ligand of CCR2 and named CCL2, has emerged as a very vital regulator of DN and has an increasing expression in the renal tissues of diabetic animals [39]. There was strong evidence that MCP-1 is significantly upregulated and positively correlated with the degree of tubulointerstitial injury in patients with DN, suggesting that MCP-1 may be involved in the development process of DN and could be a potential diagnostic marker (40–42). As the major receptor of MCP-1, CCR2-expressing macrophages promote renal injury and fibrosis in DN [43]. Furthermore, the knockout of CCR2 could reduce the incidence of glomerulosclerosis and secondary tubulointerstitial damage [43]. In diabetic db/db mice, inhibiting CCR2 using a small-molecule antagonist can alleviate proteinuria, glomerulosclerosis, and kidney failure [44]. Prominently, blocking the CCL2/CCR2 pathway in diabetics and targeting CCR2 have been potential therapeutic interventions and hot topics to limit progressive renal injury. Awad et al. [ 45] showed that both pharmacological blockade and genetic deficiency of CCR2 could alleviate renal tissue injury in diabetic mice by reducing albuminuria, blood urea nitrogen (BUN), plasma creatinine, histological changes, kidney fibronectin expression, macrophage recruitment, and inflammatory cytokine production in Ins2Akita and STZ-induced diabetic kidney disease. Du et al. [ 46] found that DN kidney damage could be mitigated by inhibiting macrophage infiltration and downregulating the MCP-1/CCR2 signaling pathway in DN. In addition, two kinds of CCR2 antagonism, rs504393 and ro5234444, could block the development of DN by decreasing macrophage infiltration of the kidney in type 2 diabetes mice [44, 47]. A multicenter, randomized trial conducted by de Zeeuw’s team showed that compared to 111 DN patients treated by placebo, 221 patients with DN had a secondary decline in albuminuria given CCX140-B, a selective inhibitor of CCR2, based on standard care with angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) [48]. In our article, CCR2 was upregulated in renal tubular tissues of DN than controls and has a high effective diagnostic ability for DN (AUC = 0.859, sensitivity = $77.78\%$, specificity = $94.59\%$ in a merged dataset; AUC = 0.939, sensitivity = $100.00\%$, specificity = $90.91\%$; and AUC = 0.931, sensitivity = $88.89\%$, specificity = $100.00\%$ in two validation datasets, respectively, GSE175759 and GSE47184; AUC = 0.958, sensitivity = $92.5\%$, specificity = $96.7\%$ in IHC validation). CX3CR1, C-X3-C motif chemokine receptor 1, is a specific membrane-bound receptor of fractalkine (CX3CL1) and belongs to the chemokine receptor superfamily. Currently, CX3CR1 is expressed on the membranes of natural killer cells (NK cells), tubular cells, mast cells, platelets, dendritic cells (DCs), effector T cells, renal cancer cells, vascular smooth cells, mesenchymal cells, and monocytes/macrophages [49, 50]. Resembling CCR2, it has seven transmembrane G-protein coupled domains, and it is close to the CCR gene family; it is located at 3p21-3pter [51]. Both CX3CR1 and its exclusive ligand, CX3CL1, were upregulated in the kidney in the DN group [50, 52, 53]. Accompanying CX3CL1, which is mainly located in the renal tubular epithelium, especially in inflammatory kidney tissues, CX3CR1+ T cells and monocytes are ubiquitously expressed in renal tissues with inflammation in patients [54, 55]. Kikuchi et al. [ 56] tested that CX3CR1 mRNA expression was increased in STZ-diabetic rats at 4 weeks, and the distribution of CX3CR1-positive cells in diabetic glomeruli was also raised at 8 weeks. Moreover, the upregulation of fractalkine and CX3CR1 in the early stages of DN suggested that they may play a crucial role in the progression of DN [56]. Furthermore, Song and his colleague showed that there were no obvious changes in plasma glucose level in diabetic CX3CR1−/− mice, while the decline in markers of renal inflammation fibrosis and ECM, such as collagen, fractional mesangial area, and fibronectin, was markedly observed compared with diabetic WT mice [57]. Proverbially, the CX3CL1/CX3CR1 axis is significantly related to anti-inflammatory, anti-fibrosis, anti-rejection, and anti-cancer activities in the treatment of renal diseases. Once activation of the CX3CL1/CX3CR1 axis by their combination occurs, a cascade through multiple signaling pathways in the kidney system is initiated, including ROS/MAPKs, Raf/MEK$\frac{1}{2}$-ERK$\frac{1}{2}$-AKT/PI3K, and NF-κB. The CX3CL1/CX3CR1 axis directly upregulates the expansion of mesangial cells in diabetes nephropathy through ROS and MAPK [58]. So far, no study has focused on the biomarker CX3CR1 in DN. In this study, the results reveal that CX3CR1 expression may be a promising and valuable diagnostic efficiency hallmark in kidney tissues of DN patients with high diagnostic efficacy at AUC = 0.921, sensitivity = $82.22\%$, specificity = $97.30\%$ in a merged dataset, AUC = 0.939, sensitivity = $100.00\%$, specificity = $90.91\%$, and AUC = 0.917, sensitivity = $83.33\%$, specificity = $100.00\%$ in two validation datasets, respectively, GSE175759 and GSE47184, and AUC = 0.993, sensitivity = $97.5\%$, specificity = $100.0\%$ in experimental validation. SELP, also named CD62 or P-selectin, is a kind of glycoprotein and the largest of the selectins with 140 kDa, stored in the α-granules of platelets and in the Weibel–Palade bodies of endothelial cells, and functions on leukocyte recruitment, leukocyte rolling, and platelet adhesion [59]. Functionally, as part of the role of cell adhesion, P-selectin could promptly move to the plasma membrane, interacting with its ligands during inflammation [60]. Structurally, P-selectin is composed of an extracellular region with an N-terminal lectin domain, an epidermal growth factor motif (EGF), and specifically nine regulatory protein repeats (SCRs), a transmembrane section, and a short intracytoplasmic tail [60, 61]. The relationship between P-selectin and DN has been reported by some scholars. A study reported that P-selectin in biopsy kidney tissue of patients with DN was higher than in other glomerular diseases [62]. Wang et al. [ 63] found that the expression level of plasma P-selectin in patients with type 2 diabetes was raised, and with DN development, accompanied by the progressive elevation of plasma p-selectin, the highest expression levels existed in patients with significant renal insufficiency, suggesting a positive correlation between P-selectin and the severity of DN. Bavbek’s team also found higher plasma levels of P-selectin in DM patients compared with controls and in DM patients with proteinuria than without proteinuria [64]. Another study reported that P-selectin expression in DN may be induced by NF-κB activation through P50 to participate in the pathogenesis of DN [65]. Like the above studies, our research also found a higher expression of SELP in DN than in the control, and the diagnostic value of SELP was assessed by ROC, which showed high AUC, sensitivity, and specificity both in the training dataset, two validated datasets, and biopsy tissue validation. Proverbially, inflammatory processes with immune modulation are dramatically involved in both the development and progression of structural deterioration in DN. There is undoubtedly evidence that inflammatory cell recruitment, infiltration, and activation play a crucial role in the development and progression of DN. Once released by scathed kidney cells, the inflammatory remodeling progress would be triggered, and the DN progression would be mediated by those lesion or danger signals by initiating immune cells. Growing evidence was reported that pro-inflammatory chemokines, cytokines, growth factors, adhesion molecules, nuclear factors, as well as immune cells, play a major role in the pathogenesis of DN and its complications. Infiltration of immune cells, including lymphocyte cells, macrophages, monocyte cells, and mast cells, into the kidney has been reported. A large amount of evidence supports that the inflammatory components of the tubulointerstitium, especially the proximal tubular epithelial cells, play a central role in the pathogenesis of DN [66, 67]. Generally, the present results highly confirm those previous studies. In this study, kidney tissues in DN had a broader and higher infiltration of immune-related cells and types in comparison with control tissues. Interestingly, it showed general positive correlations among these immune signatures, especially for type I IFN response, MHC class I, cytolytic activity, type II IFN response, pDCs, T-cell co-stimulation, HLA, inflammation-promoting, parainflammation, TIL, CCR, and checkpoint, among which highly positive correlations existed. Moreover, 20 kinds of 29 immune signatures had significant differential distributions based on the Wilcoxon rest. More accurately, consequently, based on the intersection of Wilcoxon test and LASSO regression, seven immune-related cells and types, namely APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group, exhibited a marked infiltration advantage. Noteworthily, the upregulated infiltration of macrophages in DN had been found from both animal models and kidney biopsy specimens of DN patients. It was reported that macrophage accumulation had been found in both glomeruli and interstitium (68–70). The quantity of macrophages in the interstitium is in direct proportion to the proteinuria level in the STZ model of type I diabetes [69]. Once recruited to the kidney, macrophages have been proposed to mediate renal injury through a variety of mechanisms, including the production of reactive oxygen species (ROS), cytokines, and proteases, which lead to tissue damage and ultimately to fibrosis [71]. Gradually, studies have shown that the expression of ICAM-1 and MCP-1 in renal tubular cells was elevated due to high blood glucose levels and stimulation of advanced glycation end products, and then infiltration of macrophages followed. Infiltrating macrophages mediate renal injury by releasing lysosomal enzymes, nitric oxide, ROS, transforming growth factor, vascular endothelial growth factor, and cytokines [72, 73]. Moreover, the accumulation of macrophages in DN indicates the decline of renal function, followed by inflammation progression in DN induced by the macrophage-derived products. As a result, there was a close connection between macrophage accumulation and the development of renal lesions and the decline of renal function [70, 74]. Besides, a growing number of studies have reported that targeting CCR2, one of three selected biomarkers in our study, could relieve macrophage infiltration and ameliorate inflammation to inhibit DN progression (44–47). It is consistent with our results that CCR2 was significantly positive in macrophages in DN. As for T cells, recent studies have suggested a momentous role for T-cell recruitment into kidney tissue, accompanied by the recruitment of macrophages, in diabetic nephropathy [75]. Higher accumulations of CD4+ and CD8+ T cells had been detected in the glomeruli of diabetic NOD mice than controls [76]. Moon [77] reported an observed increase in CD4+, CD8+, and CD20+ cells in renal interstitial tissues of Type II diabetic patients and close links between CD4+ and CD20+ cells and proteinuria, indicating the underlying immunopathological correlations in DN with disorderly infiltration and the activation of T cells in renal interstitial tissues. Another study found higher infiltration of CD4+T cells, CD8 T cells, and macrophages in the kidney tissues of STZ-induced diabetic rats and significantly higher expression of CD4, CD8, MHC classes I and II, and the proinflammatory cytokines tumor necrosis factor-a, interferon-γ, and nitric oxide (NO) in diabetic kidneys in comparison with control [69]. Notably, CD8+ T cells, the subcategory of leukocytes, have a strong pro-inflammatory effect and are involved in mediating immunity by direct cell–cell signaling via surface molecules and indirect signaling via cytokines in kidney damage. It is markedly elevated in DN (69, 76–78) and has gradually become a potential therapeutic target of DN [78, 79]. Zhang et al. [ 62] exhibited the therapeutic value of mesenchymal stem cells by suppressing CD8+ T-cell proliferation and activation mediated by CD103+ DCs in DN rats. Seo [79] and his colleagues reported that Mycophenolate Mofetil can alleviate diabetic nephropathy in db/db mice, followed by decreased albuminuria, attenuated mesangial expansion, and profibrotic mRNA expressions through downregulating the infiltrated CD4+ and CD8+ T cells. Besides, as cytotoxic T lymphocytes, CD8+ T cells might be responsible for the kidney damage in DN. After the secretion of cytokines, CD8+T cells can be recruited to the inflammatory location by interacting with MHC class I antigen, which is commonly expressed on all nucleated cells [69, 80]. It can be a logical explanation for our study that CD8+ T cells and MHC class I antigen were coincidentally elevated in the DN group. Moreover, dendritic cells, HLA, neutrophils, Th1 cells, Th2 cells, and so on, were demonstrated to play a crucial role in the development and process of DN (11, 76, 81–84). In view of this, our research is consistent with previous reports and highlights the importance of those immune-related cells and types in the pathogenesis of DN through bioinformatic analysis. Given the pivotal role of immune infiltrating cells and biomarkers in DN, the relationships between three biomarkers and seven significant immune signatures were analyzed further by the Spearman algorithm. Meaningfully, three biomarkers are highly and positively correlated with these immune-related cells and types, which is highly consistent with the crucial role of pro-inflammatory factors and immune-related cells and types in kidney damage in patients with DN. Collectively, all of these findings provide logical ideas about how the immune system modulates in DN. This may lead to the discovery of earlier and more reliable biomarkers and, hopefully, the identification of new therapeutic targets in diabetic kidney disease. Besides, impossible therapeutic compounds were also explored using CMap, an online tool analyzing underlying drugs based on the 36 intersection genes in two intersected pathways between KEGG-DEGs and GSEA-KEGG analysis in this article. The CMap database (https://clue.io/) is a gene expression database built by researchers from Harvard, Cambridge University, and the Massachusetts Institute of Technology. It is a biological application database related to distractors, gene expression, and diseases that was established based on gene expression differences using different distractors (including small molecules) to deal with human cells [4]. According to the correlation between genes, diseases, and drugs established by gene expression profiles, it is helpful for researchers to quickly use gene expression profile data to compare drugs highly related to diseases, infer the main structure of most drug molecules, and summarize the possible mechanism of action of drug molecules in the field of drug research and development. In this research, tetrabenazine, dilazep, tomelukast, KIN001-220, azacytidine, umbelliferone, lysylphenylalanyl-tyrosine, memantine, phensuximide, and BIBU-1361 were the top 10 compounds with negative correlations, which may reverse the alterations. Specifically, dilazep, as an antiplatelet drug, is a kind of adenosine reuptake inhibitor, calcium channel antagonist, and platelet aggregation inhibitor. It has a vasodilator effect, and it can selectively expand the coronary arteries and increase coronary blood flow. It has been reported that dilazep could improve kidney function. Nakazawa et al. [ 85] reported that dilazep dihydrochloride could significantly suppress glomerulosclerosis and glomerular adhesion to Bowman’s capsules in rats with Masugi nephritis. Dilazep dihydrochloride was also found to improve proteinuria in patients with DN (86–88), which suggested that platelet activation played a pivotal role in the development and process of DN [89]. Another study reported that dilazep may be useful in preventing renal deterioration in the early stages of type II DN [90]. In a multicenter study that was researched the clinical efficacy of dilazep dihydrochloride in the microalbuminuria stage of DN, 37 DN patients with microalbuminuria were given orally 300 mg/day of dilazep dihydrochloride. Compared with before, the mean albuminuria was noticeably lower, and the urinary NAG activity improved after treatment with the drug. Meanwhile, no renal function damage was found at this stage. It appears that early administration of dilazep dihydrochloride may contribute to improving proteinuria and preventing renal dysfunction in patients with DN [86]. In Ebihara’s study, 22 patients with IgA nephropathy and 20 healthy controls were recruited, and among them, 14 patients in stage II or III were treated with dilazep dihydrochloride. In the study, the P-selectin expression level in plasma and urine in patients with IgA nephropathy was detected, and the relationship between the patients’ histology and urinary protein excretion was analyzed. Therefore, plasma P-selectin is a helpful biomarker for the activity of IgA nephropathy, and dilazep dihydrochloride is an efficacious drug for reducing plasma soluble P-selectin levels in patients with IgA nephropathy [91]. Interestingly and coincidentally, P-selectin, also named SELP, as a marker representing disease activity, cellular activation, and inflammatory mediators, is one of three selected biomarkers related to DN in our study. Dilazep is a potential therapeutic agent for the treatment of DN patients, as analyzed by CMap in our study. The high similarity with Ebihara’s study confirms the reliability and accuracy of the results, which suggest that P-selectin is a very promising biomarker for DN and that dilazep is a prospective drug to improve renal function in DN with the decline of p-selectin. ## Conclusion Conclusively, the present article identified three core and prospective biomarke<rs implicated in diabetic tubulointerstitial lesions, which had close links with immune cells and types and would be a future underlying target for the diagnosis and immunotherapy of DN. Besides, dilazep, a small molecular agent, was found to be promising therapeutic drug in diabetic renal disease. However, the present study also had some limitations. As biomarkers, plasma levels detection in clinic and deeper basic mechanism studies in vitro and in vivo are needed to validate the feasibility of transformation applied to diabetic tubule lesions. Most importantly, the analysis of the relationship between immune cell infiltration and diabetes tubulointerstitial injury provides a novel potential approach and strategy for immunotherapy to improve diabetic tubulointerstitial injury in DN patients. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by The Ethics Committee of Second Hospital of Hebei Medical University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YS and HZ designed the research and collected the data. HZ analyzed the data and wrote the paper. HZ, LM, and ZY helped interpreted the data. HZ and LM prepared all figures and tables. LM and ZY revised the language of the article. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer authors: - Seema Khadirnaikar - Sudhanshu Shukla - S. R. M. Prasanna journal: Scientific Reports year: 2023 pmcid: PMC10030850 doi: 10.1038/s41598-023-31426-w license: CC BY 4.0 --- # Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer ## Abstract Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1–C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics. ## Introduction Non-small cell lung cancer (NSCLC) with three subtypes, namely, squamous-cell carcinoma (LUSC), adenocarcinoma (LUAD), and large-cell carcinoma contributes to the majority of the lung cancer-related deaths every year1. It is projected that in the US alone, for the year 2022, there will be 1,918,030 new cancer cases1. Lung cancer alone will contribute to 236,740 new cases (both sexes combined) and will be a leading cause of cancer related deaths1. The first line of treatment for lung cancer is determined based on the histopathological stage and includes chemotherapy, surgery, radiation, targeted therapy, and their combinations2. Even with the advancements in therapies, the 5-year survival rate for lung cancer remains minimal1. The poor survival rate can be attributed to the ineffectiveness of the first line of therapy due to the lack of understanding of underlying tumor heterogeneity at the molecular level2–5. The heterogeneity of the tumor is largely determined by the genetic and epigenetic makeup of the tumors6,7. Therefore, precise identification of the molecular subtypes (subgroups) using molecular data is essential in order to effectively use the existing treatment strategies and improve the patient care3. With the rapid development of high-throughput sequencing (HTS) technologies, massive amounts of molecular data are being generated at various levels of evidence (single-omic level)8,9. Projects like The Cancer Genome Atlas (TCGA) have successfully used the HTS technologies to generate genomic, epigenomic, transcriptomic, and proteomic data to characterize cancer and normal samples across 33 cancer types10. Several studies have attempted subgroup identification using the TCGA data. The initial studies used statistical methods to develop models for subgroup identification and prognosis11–13. As these studies are based on single-omic, they do not take into account the inter-dependencies between different omics. It is necessary to consider information from multiple levels of evidence while subgrouping to model complex biological phenomena14,15. Besides providing additional information, adding multiple levels of evidence will increase the dimension of the data. In the case of machine learning (ML) models, the large dimension of the data may lead to overfitting due to the relatively small number of samples16. To overcome this, first, the large-dimension data needs to be converted into a lower dimension. This can be done using linear projection approaches like principal component analysis (PCA). However, disease phenotype is the resultant of a combination of genetic and epigenetic factors which may not be linear17,18. Therefore, ML techniques can be used to integrate different levels of evidence and project it to a lower dimension in a non-linear manner using models like autoencoders (AE)19. Several attempts have been made to use multi-omics data for various applications, including patient stratification16,20,21. Chaudray et al. made one of the early attempts in the direction of early data integration using ML in cancer to predict the survival in hepatocellular carcinoma (HCC) samples using mRNA, miRNA, and methylation data20. The authors identified prognostic subgroups with a significant difference in survival by explicitly applying Cox-regression as the loss function to retain the features contributing to survival. Baek et al. carried out their work in the same direction on pancreatic cancer (PAAD) using mRNA, miRNA, and methylation data to cluster the patients16. Here, mutation data along with multi-omics data and clinical data is used to build a classification model to predict the five-year recurrence and survival. Recently, Zhan et al. combined the information from histopathology images (H and E) and transcriptomic data to predict the survival in HCC patients22. They proved that imaging based predictions are more accurate than Cox-PH based predictions alone. All these works demonstrated that multi-omics data conveys more information than single-omic. We hypothesize that addition and non-linear processing of distinct levels of information will further improve the discriminative ability. In this work, in addition to mRNA, miRNA, and DNA methylation data, protein expression data is also integrated. Proteins have a crucial role to play in cellular signaling and phenotype determination23,24. Expression patterns of proteins carry vital diagnostic and prognostic information25. Besides survival prediction as done in16,20,22, multi-omics data integration strategy can also be used for subgroup identification. Several studies have discussed the significance of subgroup identification from the point of view of precision therapy3. One of the important directions in the application of ML to multi-omics data is to use it for the identification of the subgroup to which the samples belong. This will help the clinicians decide on the treatment regimen. Our aim in this work is to identify the novel molecular subgroups in NSCLC to convey additional information, besides the existing histopathological grades. This additional information about subgroups will help in the effective utilization of the existing treatment strategies. Also, we aim to build classification models to predict the class labels for new samples. The final classification label will be obtained in two steps. In the first step, the most widely used classification models, support vector machine (SVM), Random forest (RF), and feed-forward neural network (FFNN) (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0), will be used to obtain the prediction probabilities. As each of these classification models are based on different principles, the prediction probabilities will be concatenated and used as input to train the decision-level fused classifiers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1). The decision-level fused classifiers include linear and non-linear (logistic regression and FFNN) classification models26–28. As different levels of evidence convey complementary information, classification models will be built based on the feature-level fusion technique. In these models, the features originating from different omic levels will be fused to obtain a single representation which in turn will be used to train the classification models17,29. The features from different levels of evidence will be concatenated to obtain the fused feature representation and train the classification models. Figure 1Overall pipeline followed in this work. ( a) Each level of evidence (single-omic) was preprocessed and multi-omics representation was obtained by stacking the features for feature-vectors (samples) common across them. ( b) The latent representation of multi-omics data (F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE) was obtained using an autoencoder (AE). ( c) Consensus K-means clustering was applied on the reduced dimension representation to obtain the cluster labels. ( d) Molecular characterization of samples in clusters obtained was carried out to understand the subgroups. ( e) Decision-level fused classifiers obtained by the combination of classification models including, support vector machines (SVM), random forest (RF), and feed-forward neural network (FFNN) was proposed for subgroup identification. ## Results The overview of various steps involved in this work are outlined in Fig. 1. An outline of the steps followed for preprocessing the mRNA (F1), miRNA (F2), methylation (F3), and protein expression (F4) data is shown in Supplementary Figure S1. The details of the data used for subsequent analysis is summarized in Supplementary Table S1.Figure 2(a) Architecture of the autoencoder (AE) used in this study. Here, H\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_1$$\end{document}1, H\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, and H\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_3$$\end{document}3 are the first, second, and third hidden layers with 2000, 1000, and 500 nodes, respectively. F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE is the encoded representation from the bottleneck layer with 100 nodes. ( b) Proportion of ambiguously clustered pairs (PAC) values obtained from the CDF curve for consensus clustering of reduced dimension data obtained from AE and PCA. ( c) Consensus clustering heatmap for $K = 5.$ ( d) and (e) t-SNE plots for samples in original dimension, and reduced dimension obtained using AE. Samples are colored based on the labels obtained by consensus K-means clustering. ( f) and (g) Kaplan-Meier plots for overall (OS) and disease-free survival (DFS) in the clusters obtained by consensus K-means clustering. ## Dimensionality reduction and clustering In this work, an under-complete autoencoder (AE) with three hidden layers, each with 2000, 1000, and 500 nodes, and bottleneck layer with 100 nodes was used (Fig. 2 a, and Supplementary Figure S2). This architecture was chosen as it had the least difference between training and validation losses (Supplementary Table S2). The reduced dimension multi-omics representation from AE was clustered, and the proportion of ambiguously clustered pairs (PAC) values were obtained using Eq. [ 1] with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_{1}=0.1$$\end{document}u1=0.1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_{2}=0.9$$\end{document}u2=0.9 (Supplementary Figure S3 a and Fig. 2b). Although the least PAC value was obtained for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$K = 2$$$\end{document}$K = 2$ (PAC = 0.06), the clusters here represented the two known histological NSCLC subtypes, LUAD and LUSC (Supplementary Figure S3b and c). Hence, the subsequent smallest PAC value was examined. As the cluster with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$K = 5$$$\end{document}$K = 5$ had the next smallest PAC value (PAC = 0.14), the cluster labels obtained for this case were considered for subsequent analysis. Besides having a small PAC value, the consensus heatmap for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$K = 5$$$\end{document}$K = 5$ was also consistent (Fig. 2c). To visualize the distribution of samples in these five clusters, both before and after dimensionality reduction by AE, t-SNE plots were generated. It was evident from the t-SNE plots that there was a significant overlap between the samples in the original feature space (Fig. 2d). Also, the samples can be distinguished with minimal overlap when the dimension of the data was reduced using AE (Fig. 2e). We also used UMAP to visualize the sample distribution and found it to be similar to t-SNE (Supplementary Figure S4)30. The PAC value obtained by clustering the multi-omics data without dimensionality reduction by AE (PAC = 0.31) was higher as compared to the case of dimensionality reduction by AE (PAC = 0.14) (Table 1). This observation indicated that the AE model was able to combine and capture the variation of information in the muti-omics data, and dimensionality reduction is an essential step in obtaining consistent clusters. Additionally, we compared our AE based technique with the widely used unsupervised linear dimensionality reduction technique, principal component analysis (PCA). The top 100 principal components (PCs) were obtained by applying PCA on the multi-omics data matrix (standardized by mean and standard deviation). These PCs were then clustered using consensus K-means clustering. The number of clusters was varied from 2 to 10. The PAC values thus obtained were consistently high (closer to 1). This indicated that none of the clusters obtained were consistent (Fig. 2b, PAC = 0.98 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$K = 5$$$\end{document}$K = 5$). This result validates the hypothesis that non-linear dimensionality reduction is required for biological data, which has also been shown in earlier studies31. We also carried out the clustering of the subset of selected features from individual levels of evidence (single-omic) and their combinations. Clustering was carried out on these chosen features with and without dimensionality reduction by AE and PCA (Table 1). The PAC values obtained for these cases were higher than the multi-omics case (with all the four factors combined). This result signifies that the multi-omics clusters were more consistent than single-omic. Also, multi-omics with protein expression (F4) had smaller PAC value (PAC = 0.14) when compared to the combination of mRNA (F1), miRNA (F2), and methylation (F3) only (PAC = 0.28) (Table 1). This observation supported the hypothesis that protein expression indeed has a significant role to play in addition to other omics. Hence, strengthening the assumption that the combination of different omics conveys more information than the individual levels of evidence. Table 1Summarizing the PAC values obtained for $K = 5$ for each level of evidence for the subset of selected features, when clustered without dimensionality reduction, and with dimensionality reduction using PCA and AE (F1: mRNA (PcGs) expression, F2: miRNA expression, F3: DNA methylation, F4: protein expression).OmicDimensionWithout dimensionality reductionWith dimensionality reductionPCAAEF120000.360.980.37F24070.460.970.49F320000.250.940.25F42160.430.970.44F1 + F224070.350.950.42F2 + F324070.260.940.24F1 + F340000.350.980.28F1 + F2 + F344070.360.960.28F1 + F2 + F3 + F446230.310.980.14 Further, we compared the proposed technique with iClusterPlus32, an existing and widely used statistical multi-omics data integration technique33–35. iClusterPlus was applied to multi-omics data, and the parameters were tuned using tune.iClusterPlus as recommended by the authors. The clusters obtained using our technique, and iClusterPlus were compared using two cluster evaluation methods, Silhouette coefficient, and Calinski-Harabasz index. The closer the value of the Silhouette coefficient to one and the higher the Calinski-Harabasz index, the better is the clustering. Both these scores indicated that the clusters obtained using the proposed algorithm were better separated than iClusterPlus (Supplementary Table S3). These evaluation measures were also computed to compare the consensus K-means clustering with hierarchical clustering (HC), Gaussian mixture models (GMM), and regular K-means clustering algorithm. The clustering scores obtained for consensus K-means and regular K-means were comparable in this case (Supplementary Table S4). But literature shows that consensus clustering outperforms regular clustering techniques33,36. In addition, we performed the ablation study by varying the number of features from F1 and F3, and evaluated the performance of the AE model. The number of input features from F1 and F3 levels were varied (from 1000 to 4000), and the complete pipeline was repeated for different architectures of AE’s. The performance was compared using the PAC values for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$K = 5$$$\end{document}$K = 5$ in each of the cases (Supplementary Table S5). It was observed that the PAC value was smallest when the top 2000 most varying features were considered from F1 and F3. ## Clinical and biological characterization of clusters To understand the clinical significance of the different clusters obtained, we compared the survival times among the five clusters (Fig. 1d). The comparison of survival time using the log-rank test showed a significant difference in the survival of the patients (OS p: 0.019 and DFS p: 0.050). This suggests that there was at least one group whose survival was significantly different from the rest. Further, we used Kaplan-Meier (KM) plots to visualize the difference in the survival curves. We observed that the patients in Cluster 2 (C2 median survival 40.37 months) had significantly lower overall survival (OS). In comparison, patients in Cluster 3 (C3 median survival not reached i.e., more than half of the samples did not experience the event (death)) had the best OS rate. Patients in Cluster 1 (C1), Cluster 4 (C4), and Cluster 5 (C5) showed intermediate OS (Fig. 2f). This observation was also true for DFS (Fig. 2g). The survival analysis of the clusters obtained via PCA did not yield a significant difference in survival time (OS p: 0.169 and DFS p: 0.446). This indicates that the groups obtained were not clearly separable. This is in phase with the conclusion drawn based on the PAC value as well, that the clusters obtained via PCA were inconsistent. This also validates the consistency of our method over PCA. The differences in survival might be the resultant of underlying genetic and epigenetic variation among the clusters. To understand the molecular differences among the clusters, and to identify the molecular features specific to each subgroup, we compared the mRNA, miRNA, DNA methylation, and protein expression among the newly identified clusters (Fig. 3 and Supplementary Figure S5). We identified 672 PcGs that were differentially expressed across the five clusters (Supplementary Table S6 and Fig. 3a). Network analysis using the differentially expressed genes identified important biological pathways that were regulated, specifically in each cluster type (Supplementary Table S7). Further, we also identified 127 long non-coding RNAs (LncRNAs), nine miRNAs, and 719 CpG probes as differentially expressed (Supplementary Table S6 and Fig. 3a). The clinical characteristics including lung cancer subtype (LUAD and LUSC), the AD differentiation37, patient stage, tumor purity38, smoking status (NS: never smokers; LFS: long-term smokers greater than 15 years; SFS: shorter-term smokers; CS: current smokers) and mutation rate were obtained from Chen et al. study33 (Fig. 3b). It showed that patients in cluster 3 had a lower mutation rate and lower purity, i.e., a lower proportion of tumor cells in the tumor microenvironment. Figure 3Characterization of different molecular levels of evidence. ( a) Heatmap indicating the expression of protein coding genes (PcGs), LUAD-LUSC signature genes (NKX2-1, KRT7, KRT5, KRT6A, SOX2, TP63), long non-coding RNAs (lnc RNAs), CpG probes, CIMP probes, and protein expression in the subgroups obtained by multi-omics clustering. ( b) Heatmap showing TCGA subtype, AD differentiation, pathological stage, tumor purity, smoking status (NS, lifelong never-smokers; LFS, longer-term former smokers greater than 15 years; SFS, shorter-term former smokers; CS, current smokers), and mutation rate in the multi-omics subgroups. Furthermore, to understand the genetic differences and to identify the significantly different driver genes, we compared the CNV and mutation among the clusters (Fig. 4a–f). The steps followed for these analysis are outlined in Supplementary Figure S533,39. C1 had significantly higher focal amplification of Chr 8 (8q24.21, $q = 0.004$) and Chr 1 (1q21.3, $q = 0.001$) (Fig. 4a). C2 also had amplification of Chr 8(8q24.21), and C4 of Chr 3 (3q26.33) and Chr 8 (8p11.23, $q = 0.001$) (Fig. 4b and d). C5 has significantly higher focal deletion of Chr 8 (8p23.2, $q = 0.002$) (Fig. 4e). As expected, TP53 had a higher mutation rate in all clusters compared to other genes. Cluster 1 (C1) had higher mutation of KEAP1 ($q = 0.020$), KRAS ($q = 0.020$), and STK11 ($q = 0.020$). EGFR was most mutated in cluster 2 (C2) ($q = 0.020$), PTEN in cluster 4 (C4) ($q = 0.020$), and CDKN2A in cluster 5 (C5) ($q = 0.020$) (Fig. 4f). Interestingly, cluster 3 (C3) had a lower mutation rate and copy number alteration as compared to other subgroups (Fig. 4c, Supplementary Table S8).Figure 4Molecular characters of samples with class labels obtained using consensus K-means clustering. ( a)–(e) Frequency plots for copy number variation corresponding to clusters 1–5 (y-axis: proportion of copy number gain/loss, x-axis: Chromosome number) and (f) Mutation of driver genes in the subgroups. ( g) Box plot showing the distribution of stromal, immune, and ESTIMATE scores in each subgroup. ( h) Bar plot showing the distribution of significantly enriched immune cell types in the subgroups. Tumor growth, invasion, and metastasis is largely determined by the tumor microenvironment (TME)40,41. The infiltration of different immune cells also defines the clinical and biological nature of the cancers. Hence, we performed ESTIMATE analysis in the newly identified subgroups of the NSCLC patients42. The ESTIMATE analysis showed the highest infiltration of immune cells in C3 (Fig. 4g). To understand the infiltration of individual immune cell types, CIBERSORT analysis was carried out using the LM22 signature gene set43. The CIBERSORT results further confirmed the ESTIMATE analysis results with the highest enrichment of monocytes, B cells, and neutrophils in C3 (Fig. 4h). Further, to understand the pathways enriched in C3, Gene Set Enrichment Analysis (GSEA) was carried out using the signature gene sets obtained from MSigDB44,45. The GSEA analysis of C3 vs. rest, carried out using the hallmark gene sets, showed significant enrichment of immune-related pathways in C3 (Supplementary Table S9 and S10). ## Subgroup identification by classifier combination To help in the identification of class labels for a new sample, decision-level fused classification models were built. Each level of evidence is known to convey different information controlling different aspects of phenotype17,29. Hence, the classification models were trained using each molecular level of evidence. Based on the classification accuracy obtained on the test data set, it was observed that F3 (DNA methylation) had the highest classification accuracy for both base classifiers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0) and decision-level fused models (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1) (Table 2, Fig. 5, and Supplementary Figure S6).Figure 5Classification accuracy of different base classifiers tested on different omic-levels and their combinations (F1: mRNA (PcGs) expression, F2: miRNA expression, F3: DNA methylation, F4: protein expression, F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE: features from bottleneck layer of autoencoder, SVM: support vector machine, RF: random forest, FFNN: feed-forward neural network). As each level of evidence conveys complementary information, classification models were also obtained for the feature representation obtained by fusing features from different levels of evidence. F3 was combined with other levels as it had the highest classification accuracy at the single-omic level. It can be observed from Table 2 that the decision-level fused classifier trained with feature-level fused molecular features from F3 and F4 had the highest classification accuracy among all the decision-level fused models. The presence of a small number of samples to train the learners might be one of the reasons for the poor performance of the non-linear decision-level fused model over the linear decision-level fused model. The classification models were also built for the combination of features from all four factors. But there was no improvement in accuracy as compared to the combination of F3 and F4. We also trained the classification models with the reduced dimension features obtained from the AE. We observed that the classification accuracy was highest for these features (Table 2). Hence, we concluded that the AE was able to capture the variation present in the multi-omics data effectively. Table 2Summarizing the test accuracy from different classifier combination techniques for different levels of evidence (F1: mRNA (PcGs) expression, F2: miRNA expression, F3: DNA methylation, F4: protein expression, F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE: features from bottleneck layer of autoencoder, LR: logistic regression, FFNN: feed-forward neural network).OmicDimensionDecision-level fused model w/o holdoutDecision-level fused model with holdoutLinearLRFFNNLRFFNNF167285.9773.6977.1970.1870.18F2989.0050.8850.8849.1252.63F371992.9892.9894.7496.4996.49F415368.4270.1857.9070.1861.40F1 + F3139194.7492.9894.7492.9892.98F2 + F372894.7496.4994.7494.7494.74F4 + F387298.2596.4998.2596.4996.49F1 + F2 + F3 + F4155398.2596.4998.2591.2391.23F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE100100.0098.25100.00100.00100.00 To further validate the classification models, we used those samples for which only the methylation data was available. These samples were not used for cluster identification or classification as other levels of evidence were not available (i.e., incomplete data samples with respect to other levels of evidence). We obtained the subgroup label for these samples using the single-omic methylation non-linear decision-level fused model, as this model had the highest classification accuracy for single-omic data. The overall molecular characteristics of these samples, as expected, followed a similar trend as other samples. The samples in cluster 3 had the least copy number and mutational changes, and the highest immune cell infiltration (Fig. 6). This highlights that the proposed model can be used for the identification of the subgroups even in the case of incomplete data. Figure 6Molecular characters of samples with class labels obtained using methylation data. ( a)–(e) Frequency plots for copy number variation corresponding to clusters 1–5 (y-axis: proportion of copy number gain/loss, x-axis: Chromosome number) and (f) Mutation of driver genes in the subgroups. ( g) Box plot showing the distribution of stromal, immune, and ESTIMATE scores in each subgroup. ( h) Bar plot showing the distribution of significantly enriched immune cell types in the subgroups. Classification models were built to identify the subgroup to which a new sample will belong. Three supervised classification models (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0), support vector machine (SVM), Random forest (RF), and feed-forward neural network (FFNN) were built separately for each single-omic level. These models were trained using the class labels obtained from consensus K-means clustering as output labels. The input to the models were the molecular features specific to each subgroup (DE features) selected from individual omic levels (as described in previous section and Supplementary Figure S5 and Supplementary Tables S16–S19). The train-test split of 90–$10\%$ was used to build these models. As the data was non-linearly separable, a radial kernel was used for SVM. The hyperparameters for SVM and RF were obtained by 5-fold cross-validation (CV) repeated ten times. For the FFNN, appropriate number of layers and neurons were chosen based on the dimension of the input vector. Categorical cross-entropy was used as the loss function with Adam optimizer while training the FFNN. To avoid overfitting, each fully connected layer was followed by a dropout layer (0.1), and L2 activity regularizer (1e-04) and L1 weight regularizer (1e-05). The models were trained with different learning rates (0.1, 1e-02, 1e-03, 1e-04, and 1e-05), and the one with the best accuracy was chosen. To obtain an unambiguous prediction model, the prediction probabilities from each of these classifiers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{SVM}$$\end{document}PSVM, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{RF}$$\end{document}PRF, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{FFNN}$$\end{document}PFFNN) were concatenated and a new representation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{C}$$\end{document}PC) was obtained. Decision-level fused classifiers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1) were built with this new feature representation as input and subgroup labels obtained by clustering as the target. The prediction probabilities were combined linearly and non-linearly to obtain linear and non-linear decision-level fused classifiers (Supplementary Figure S6). In the case of linear decision-level fused model, the prediction probabilities obtained from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0 models (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{SVM}$$\end{document}PSVM, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{RF}$$\end{document}PRF, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{FFNN}$$\end{document}PFFNN) were weighted by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ, respectively17,29. The final classification probability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{L}$$\end{document}PL) was obtained by the weighted summation of individual prediction probabilities using Eq. [ 2]57.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} P_{L} = \alpha \times P_{SVM} + \beta \times P_{RF} + \gamma \times P_{FFNN}. \end{aligned}$$\end{document}PL=α×PSVM+β×PRF+γ×PFFNN.The values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ were varied from 0 to 1 in steps of 0.05 by ensuring that they sum up to 1 (Supplementary Algorithm I). In the case of the non-linear decision level fused model, the concatenated prediction probabilities (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P_{C}$$\end{document}PC) from the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0 models were used to train the non-linear classifiers like logistic regression (LR) and FFNN to identify the subgroup labels58. Here, two non-linear decision-level fused models with different train-test splits were trained. In the first model, both \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1 learners were trained with the complete training data set (without holdout). For the second model, a hold-out set was created by splitting the training data set. Here, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document}L0 learners were trained using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$60\%$$\end{document}$60\%$, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1 learners using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40\%$$\end{document}$40\%$ of the training data set. As different levels of evidence carry complementary information, the combination of features from different omic levels will provide additional insights. Hence, the technique of feature-level fusion can help in better classification17,29. Here, features from different molecular levels were concatenated to obtain a new feature representation. This fused representation was then used to train each of the ML classifiers. ## Discussion Subgroup identification is required for better management and treatment of cancer patients3–5. The availability of various molecular features as a consequence of the advancements in high-throughput genomic technologies has enabled the better subgrouping of cancer patients. We know that the phenotype of a patient is the resultant of various molecular features interacting non-linearly. To exploit this non-linear relation of molecular features, we used machine learning (ML) based methods. We used mRNA (F1), miRNA (F2), methylation (F3), and protein expression (F4) data from NSCLC samples. The latent representation of this multi-omics data was obtained using AE, a non-linear dimensionality reduction technique. This hidden representation was then clustered using consensus K-means clustering to identify five clusters. The clusters obtained with autoencoder (AE) based clustering were better than those obtained by clustering the preprocessed molecular features directly (Table 1). This indicates that AE was able to capture the interaction between the different levels of evidence effectively. We also showed that the AE-based clusters were more stable than the ones obtained using PCA, suggesting non-linear interaction between the molecular features (Table 1). Further, biological and clinical characterization of the clusters showed that cluster 3 showed better survival than other subgroups (Fig. 2f and g). This could be due to fewer genetic and epigenetic aberrations in the subgroup (Fig. 4). Two subgroups, cluster 1 and cluster 2, which had more LUAD patients showed poor survival, high genetic aberration, and also lower immune infiltration suggesting the highly aggressive nature of these tumors (Fig. 3 and Fig. 4). ML based classification models (SVM, RF, and FFNN) were built using each level of evidence to predict the class labels. Linear and non-linear decision-level fused models were used to integrate the prediction probabilities from different classifiers and obtain the final subgroup label. DNA methylation (F3) based model had the best predictive ability among all (Table 2). DNA methylation carries epigenetic information, which is shown to play a vital role in cancer progression, metastasis, and prognosis. As different levels of evidence convey complementary information and work in conjunction, molecular features from different omic levels were fused at the feature-level to train the ML models. The combination of epigenetic information with proteomic information gave the best results in our experimental setup (Table 2). This suggests that protein expression carries more information than other single-omic levels. To the best of our knowledge, this is the first study proving that the combination of methylation and protein expression outperforms the other combinations. The model trained with feature-level fusion performed better than that with individual levels of evidence, and the decision-level fused model performed better than individual classification models. These results confirmed our hypothesis that the phenotype is the resultant of a combination of molecular features across different omics. The better performance of the linear decision-level fused model when compared to the non-linear decision-level fused model may be attributed to the less number of samples available to train the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document}L1 non-linear classifiers. The decision-level fused models trained using the features from the autoencoder (F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE) have high classification accuracy (Table 2 and Fig. 5). One of the reasons for the better performance of the AE-based features, besides the ability of AE to capture the variation in the data, might be attributed to the fact that the classification labels were obtained by clustering the F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{AE}$$\end{document}AE. Also, the ML algorithms were able to effectively model the class-specific decision boundaries generated by the clustering algorithm. To summarise, this work proposed an end-to-end pipeline for machine learning-based subgroup identification in non-small cell lung cancer (NSCLC). We also proposed and validated the fusion-based classification models for the identification of subgroups in new samples. Since the classification models were built for individual levels of evidence, they can be used in the presence of single omic data as well. The generalizability of our model is yet to be validated due to the limitation in terms of the availability of an independent dataset. Also, exposure to more samples both in terms of heterogeneity and the number of samples, might provide better insights into the resulting subgroups. Therefore, the future work would include validating the proposed method in an independent cohort of data. The performance in the current work is based on several assumptions made at different levels. These include preprocessing of the data to reduce dimensionality, using the most well-known ML models, and using cluster labels for subgroup identification. All these need independent evaluation, which may further help to better understand the non-linear processing happening in ML. Also, the better unearthing of biological knowledge using ML models. The comparable performance of regular K-means and GMM with consensus K-means in terms of Silhouette coefficient and Calinski Harabasz index needs further analysis and will be considered for future studies. Further, including additional information from whole slide histopathological (H and E) images as an additional level of evidence can provide better insights. ## Datasets and data preprocessing The proposed pipeline was applied on the TCGA NSCLC (LUAD and LUSC) samples. TCGA multi-omics data comprising mRNA, miRNA, methylation, mutation, and copy number variation were downloaded from the GDC data portal. TCGAbiolinks (v 2.18.0) package in R46 was used to obtain this data for samples from LUAD and LUSC tumor types. Protein expression (RPPA level - 4) data was downloaded from the TCPA data portal47,48. Further, cBioPortal49 was used to obtain the clinical data. In this study, each level of evidence (single-omic) is referred to as a factor. The mapping from omic levels to the factors is shown in Supplementary Table S1. In the initial part of this work, only the samples which had data from all the four levels of evidence were considered. It can be observed from Supplementary Table S1 that the dimension of data (p) was high compared to the number of samples (n). Hence, the preprocessing of data was carried out to ensure reliability besides reducing the dimension of the data27,50. Preprocessing of raw data which included, selecting a subset of features, imputing the missing values, and data transformation, was carried out as outlined in Supplementary Figure S1. All the protocols followed to carry out the preprocessing were obtained from previous studies16,20,33,50,51. Briefly, in the case of F1 (FPKM values of protein coding mRNAs) and F2 (RPKM values of miRNAs), genes with zero expression in more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document}$20\%$ of the samples were dropped16. Genes in F1 were then sorted based on the standard deviation, and the top 2000 most variable genes were considered for further analysis33. Features retained in both the cases were scaled by min-max normalization to ensure that the data ranged between the values of 0 and 1. In the case of F3 (DNA methylation), beta values were used for analysis. The CpG probes on X and Y chromosomes, those mapping to SNPs or cross hybridized were dropped. The preprocessing was carried out using the DMRCrate (v 2.4.0) package52 in R. Samples and probes with more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\%$$\end{document}$10\%$ of the data missing were dropped20,33,50. Further, the NAs in the retained probes were imputed using K-nearest neighbors (KNN) ($K = 5$)20,33,50. The selected probes were then sorted in the decreasing order based on their standard deviation and the top 2000 probes were considered for further analysis33. As beta values range from 0 to 1, further normalization was not required. For F4 (protein expression level-4), proteins whose expression was missing in more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\%$$\end{document}$10\%$ of the samples were dropped. And as before, the missing values in the retained dimensions were imputed by KNN ($K = 5$). Normalization was not needed in the case of F4, as level-4 data was already normalized. The preprocessed features corresponding to the feature-vectors (samples) common across all the four different levels of evidence (F1–F4) were stacked to obtain the multi-omics data matrix (Fig. 1 a, Supplementary Table S1, and Supplementary Tables S11–S15). This multi-omics matrix was then used further for dimensionality reduction (Fig. 1 a). ## Multi-omics data integration and cluster identification Even after selecting the subset of features by preprocessing, the dimensionality (p) of the various factors was still high compared to the sample size (n). This (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,p>> \,n$$\end{document}p>>n) may lead to overfitting when modeled using machine learning algorithms27. We also know that the biological features from different levels of evidence interact non-linearly to produce the final cancer phenotype17,18. Hence, to reduce the dimension of multi-omics data by retaining the non-linear interaction among the biological features, we used an autoencoder (AE) (Fig. 1b)16,20. Multi-omics data was split with the train-validation split of 90–$10\%$ and used to train the AE model. The AE model was trained for 100 epochs with early stopping criteria, i.e., the model training was stopped if the validation error did not reduce for five subsequent epochs. The input data was fed in batches of 24 samples each. Rectified linear unit (ReLU) was used as the activation function, mean-squared error (MSE) as the loss function, and adaptive moment estimation (Adam) as an optimizer, as the input data was continuous. The AE model was built using the KERAS (2.4.0) library in Python 3 in Google Colab. Different architectures of AEs were obtained by varying the number of layers, and the number of nodes in each layer. The performance of AE model was measured in terms of training and validation loss (Supplementary Table S2). The model tends to overfit the data when the difference between the training and validation loss is large19. Hence, the model which had the smallest difference between the training and validation loss was considered for subsequent analysis. The lower-dimensional representation of the multi-omics data was obtained from the bottleneck layer of the trained AE model (Fig. 1b). Consensus K-means clustering was then applied to this representation to identify the clusters (Fig. 1c)33,53. Cluster labels were obtained for different number of clusters (K) by varying K from 2 to 10. The process of clustering was repeated 1000 times using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$80\%$$\end{document}$80\%$ of the samples each time33. The most consistent cluster was identified based on the proportion of ambiguously clustered pairs (PAC). This metric is quantified with the aid of the cumulative distribution function (CDF) curve54. The section lying in between the two extremes of the CDF curve (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_1$$\end{document}u1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u_2$$\end{document}u2, Supplementary Figure 2a) quantifies the proportion of samples that were assigned to different clusters in each iteration. PAC is used to estimate the value of this section. It represents the ambiguous assignments and is defined by Eq. [ 1], where K is the desired number of clusters.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} PAC_K = CDF_K(u_2) - CDF_K(u_1). \end{aligned}$$\end{document}PACK=CDFK(u2)-CDFK(u1).Lower the value of PAC, lower the disagreement in clustering during different iterations, or in other words, more stable are the clusters obtained54. ## Characterization of clusters To determine if there exists any difference in the survival between the clusters obtained, Kaplan-Meier (KM) survival curves and log-rank test were used (Fig. 1d). The end points for survival analysis was defined by overall survival (OS) and disease-free survival (DFS). OS is defined as the period from the day of initial diagnosis till death. DFS is defined as the time period from the day of treatment till the first recurrence of tumor in the same organ55. Survival analysis was carried out in R using the Survival (v 3.2-7) package. To identify the features specific to each cluster in each level of evidence, feature selection was carried out by statistical tests as described in Supplementary Figure S520,33. To summarize, the features with zero expression in more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document}$20\%$ of the samples in F1, F2, and F4, were dropped. To identify the differentially expressed (DE) features describing each subgroup, ANOVA with Tukey’s post-hoc test was used. In the case of F3, preprocessing was carried out as mentioned before (section: Datasets and data preprocessing). Further, the probes with standard deviation of more than 0.2 were quantile normalized, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$log_2$$\end{document}log2 transformed, and limma was used to compare the expression of probes (Supplementary Figure S5). Additionally, mutation and copy number variation data were also used to characterize each cluster. A binary mutation matrix indicating the presence or absence of mutation in the driver genes was obtained. Fisher’s test was carried out on the driver genes with non-silent mutations. The genes with FDR \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q~\le ~0.05$$\end{document}q≤0.05 were used for further interpretation. Copy number variation (CNV) data (segment mean) obtained from TCGA was analyzed using GISTIC 2.056. The cytobands with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$abs(SegMean)~\ge ~0.3$$\end{document}abs(SegMean)≥0.3 were considered as altered and were subjected to Fisher’s test. The cytobands with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p~\le ~0.01$$\end{document}p≤0.01 were considered for characterization. Immune, stromal, and estimate score for each sample was obtained from ESTIMATE analysis42 and subjected to ANOVA. CIBERSORT analysis was carried out using the LM22 signature gene set43. ANOVA with Tukey’s post-hoc test was carried out on these immune cells, and those with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$log_2(FoldChange)\ge 1$$\end{document}log2(FoldChange)≥1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q\le 0.05$$\end{document}q≤0.05 were considered for further interpretation of the characteristics of each cluster. Gene Set Enrichment Analysis (GSEA) was also carried out using the Hallmark signature gene sets obtained from MSigDB44,45. 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--- title: One year follow-up on a randomized study investigating serratus anterior muscle and pectoral nerves type I block to reduced neuropathic pain descriptors after mastectomy authors: - Eva M. Flores - Flavia V. Gouveia - Marcio Matsumoto - Tomás H. F. S. Bonacif - Mayra A. Kuroki - Geiza Fernanda Antunes - Ana Carolina P. Campos - Pedro P. Kimachi - Diego O. Campos - Claudia M. Simões - Marcelo M. C. Sampaio - Felipe E. M. Andrade - João Valverde - Alfredo C. S. D. Barros - Rosana L. Pagano - Raquel C. R. Martinez journal: Scientific Reports year: 2023 pmcid: PMC10030852 doi: 10.1038/s41598-023-31589-6 license: CC BY 4.0 --- # One year follow-up on a randomized study investigating serratus anterior muscle and pectoral nerves type I block to reduced neuropathic pain descriptors after mastectomy ## Abstract Breast cancer is the second most common diagnosed type of cancer in women. Chronic neuropathic pain after mastectomy occurs frequently and is a serious health problem. In our previous single-center, prospective, randomized controlled clinical study, we demonstrated that the combination of serratus anterior plane block (SAM) and pectoral nerve block type I (PECS I) with general anesthesia reduced acute postoperative pain. The present report describes a prospective follow-up study of this published study to investigate the development of chronic neuropathic pain 12 months after mastectomy by comparing the use of general anesthesia alone and general anesthesia with SAM + PECS I. Additionally, the use of analgesic medication, quality of life, depressive symptoms, and possible correlations between plasma levels of interleukin (IL)-1 beta, IL-6, and IL-10 collected before and 24 h after surgery as predictors of pain and depression were evaluated. The results showed that the use of SAM + PECS I with general anesthesia reduced numbness, hypoesthesia to touch, the incidence of patients with chronic pain in other body regions and depressive symptoms, however, did not significantly reduce the incidence of chronic neuropathic pain after mastectomy. Additionally, there was no difference in the consumption of analgesic medication and quality of life. Furthermore, no correlation was observed between IL-1 beta, IL-6, and IL-10 levels and pain and depression. The combination of general anesthesia with SAM + PECS I reduced the occurrence of specific neuropathic pain descriptors and depressive symptoms. These results could promote the use of SAM + PECS I blocks for the prevention of specific neuropathic pain symptoms after mastectomy. Registration of clinical trial: The Research Ethics Board of the Hospital Sirio-Libanes/Brazil approved the study (CAAE 48721715.0.0000.5461). This study is registered at Registro Brasileiro de Ensaios Clinicos (ReBEC), and ClinicalTrials.gov, Identifier: NCT02647385. ## Introduction Breast cancer is the second most common cancer in women with a reported incidence of 19.3 million cases worldwide and 10 million cancer-related deaths1. A potentially debilitating problem that afflicts patients with breast cancer after surgery is the chronic pain after mastectomy, which affects 25–$60\%$ of the patients2,3, severely impacts the quality of life4, and is often comorbid with depression5. Post-mastectomy pain syndrome or post-breast surgery pain syndrome has been described as a mixed syndrome that occurs 6 months after the procedure, characterized by persistent moderate pain with neuropathic characteristics located in the anterior thorax, axilla, and/or medial upper arm6,7. The chronic neuropathic pain has different characteristics depending on the surgical treatment or nerve damage8,9. The intensity of pain after surgery and consumption of analgesic medication have been reported to increase the risk of neuropathic pain10. The complexity and difficulty of its treatment could be because of neuroinflammation involving the release of inflammatory interleukins, such as interleukin (IL)-1 beta, IL-6, and IL-1011. “ Plasmatic inflammatory markers could be used as prognostic predictors of pain in large interval of time12, it has been proposed that patients who developed neuropathic pain syndrome seem to have higher levels of IL-6 in plasma13,14. Also, IL-6 concentration is positively correlated with pain severity13, and is a risk factor for increased intensity of pain15. IL-1 Beta has been used as prognostic biomarker predictors for the severity of pain in breast cancer16,17. In the same line of thinking, inflammatory biomarkers have been considered effective potential biomarkers for depression18–20”. Regional anesthesia has been suggested to play a protective role in the development of chronic neuropathic pain after mastectomy21,22. The serratus anterior plane (SAM) block and pectoral nerves block type I (PECS I) are safe and effective in breast surgery, resulting in excellent postoperative analgesia23–25. The combination of SAM and PECS I blocks promotes effective analgesia and reduction of intraoperative fentanyl and intravenous morphine usage during mastectomy with the axillary approach and reconstruction26. Despite all the advances in regional anesthesia for breast cancer, the role of SAM and PECS I blocks in the development of chronic pain has not been evaluated. The present report describes a prospective follow-up study of a previously published study26 to investigate the presence of chronic neuropathic pain 12 months after mastectomy in patients who received general anesthesia only or general anesthesia with SAM + PECS I blocks. As secondary goals, the use of analgesic medication, quality of life, depressive symptoms, and possible correlations between plasma levels of interleukin (IL)-1 beta, IL-6, and IL-10 as predictors of pain and depression were evaluated. The rational for the choice of those outcome variables is that chronic pain in general could be exacerbated by neuropathic pain-generating mechanisms leading to both the physical and emotional suffering of patients that negatively impacts the quality of life. ## Study design A prospective follow-up study of a previously published randomized controlled clinical study was conducted26. The Ethics in Research Committee of the Hospital Sirio-Libanes/Brazil Platform approved the project (CAAE 48721715.0.0000.5461), it is registered at Registro Brasileiro de Ensaios Clinicos (ReBEC), ClinicalTrials.gov, Identifier: NCT02647385, and the date of registration is $\frac{06}{01}$/2016. All methods were performed in accordance with relevant guidelines and regulations. Twelve months after the surgical procedure, the blinded patients included in the previous study26 were interviewed, by a blinded experimenter, with the NRS, DN4, SF-36, PHQ-9, and asked about the use of analgesic medication at that time set point. The plasma levels of IL-1 beta, IL-6, and IL-10 were correlated with the NRS, DN4, and PHQ-9 scales as predictors of pain and depression. All data were entered into the REDCap (Research Electronic Data Capture) database. ## Participants The main inclusion criterion was patients enrolled in our previous study26. Female patients within the age range of 18–75 years, with American Society of Anesthesiology (ASA) physical status I or II, suitable for radical mastectomy with axillary node dissection and breast reconstruction, who provided written informed consent were included. Exclusion criteria for enrollment in the previous study were allergy to medications used in the study, history of mental disorders and chronic pain. The reason is that history of chronic pain is an independent risk factor for neuropathic chronic pain after mastectomy27. Additional exclusion criteria for this report was lost to follow-up. ## Interventions performed in the previous published study In our previous study26, patients were randomly allocated to general anesthesia alone or general anesthesia with SAM + PECS I blocks. ## Randomization The block randomization method for the clinical study was designed to randomize subjects into 1 of the possible groups, i.e. group I: general anesthesia, group II: SAM + PECS-1 block. After generation of the randomization codes by the corresponding author, the allocation was registered in opaque sealed envelopes, and the patients were kept blind to this process. An anesthesiologist recruited participants from the ambulatory care unit of Hospital Sirio-Libanes. ## General anesthesia The patients received midazolam (7.5 mg) orally as a premedication 1 h before surgery. Anesthesia was induced using fentanyl (2–3 mcg kg−1), propofol (2–3 mg kg−1), and cisatracurium (0.1 mg kg−1) or rocuronium (0.6 mg kg−1). Anesthesia was maintained using sevoflurane (1.5–$2.0\%$) and $50\%$ oxygen delivered via a circle system. Additional fentanyl boluses were administered if necessary. ## SAM + PECS I blocks The SAM and PECS I blocks were performed by a team composed of three experienced anesthetists, as described previously23,26,28. Briefly, for SAM block the 12.5 MHz linear probe was positioned in the midaxillary line at the level of T5, and an in-plane needle was inserted into the fascia between the latissimus dorsi and the serratus anterior muscle for the injection of ropivacaine (20 mL of $0.375\%$). For the PECS I block, 10 mL of $0.375\%$ ropivacaine was injected in the fascia between minor and major pectoral muscles. Needle position was confirmed using visualization of the separation of the layers with dispersion of the injected volume. ## Postoperative analgesia All patients received a standardized postoperative analgesic regimen consisting of metamizole (1 g every 6 h), ketoprofen (100 mg every 12 h), and patient-controlled analgesia (PCA) rescue with intravenous morphine at the end of the surgical procedure. ## Scales The patients were interviewed using the following scales:Numeric Rating Scale (NRS) is a standard scoring system to assess immediate pain levels, ranging from scores 0 (no pain) to 10 (worst pain). Chronic pain can be defined as NRS score > 0 on a 0–10 scale at least 3 months after the surgical procedure29.Douleur Neuropathique 4 (DN4) questionnaire accesses seven domains of neuropathic pain (i.e. burning, painful cold, electric shocks, tingling, pins and needles, numbness, and itching) and tree domains related to sensorial examination (hypoesthesia to touch, hypoesthesia to pinprick and pain caused by brushing)30,31. In this study, the surgical area, axilla, medial arm, breast, or chest wall were evaluated, and a score equal or greater than 4 points characterized neuropathic pain32.Short-Form Health Survey (SF-36) is a health status questionnaire consisted of eight domains including functional capacity, physical aspects, general state of health, vitality, social aspects, pain, emotional aspects, and mental health ranging from zero (decreased quality of life) to 100 (increased quality of life).Patient Health Questionnaire-9 (PHQ-9) is an instrument assessing nine depressive symptoms along with a functional health assessment. Scores range from 0 (none) to 27 (severe) to identify depressive states33. ## ELISA Blood samples were collected in EDTA-Vacutainer tubes before and 24 h after the surgical procedure, and the plasma levels of IL-1 beta, IL-6, and IL-10 were measured using commercial enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems), as described previously26. ## Sample size The sample size for the clinical study was previously calculated and can be found in our previous publication26. In this study, patients that completed 1-year follow-up were included in the analysis. Power analysis for this new study was calculated based on using www.openepi.com, based on the results of Qian et al.29, who evaluated SAM block (lidocaine) versus control group (saline) and produced an effect size of 1.5 for NRS. Twenty patients per group were required to achieve significant results with an alpha of 0.05 and a beta of $90\%$. In this study, 22 patients were included in each treatment group. ## Primary outcome The primary outcome measure was evaluation of chronic neuropathic pain development 12 months after the mastectomy. All patients were evaluated by an anesthesiologist specialist in pain management. The diagnosis of neuropathic pain is based on clinical evaluation based on the history and physical examination to evaluate signs and symptoms. The DN-4 survey was used as a screening tool to better understand the chronic neuropathic pain after mastectomy and was performed as previous described30. The patients were evaluated using the Numeric Rating Scale (NRS) and the Douleur Neuropathique 4 (DN4) questionnaire Also, the incidence of pain in other body regions was performed. ## Secondary outcomes Demographic, clinical, surgical, and nonsurgical treatment data were collected. The current use of analgesic medication and the quality of life indicator of overall health status were evaluated using the Short-Form Health Survey (SF-36), and depressive symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9). IL-1 beta, IL-6, and IL-10 levels before and 24 h after the surgical procedure were evaluated as predictors of pain and depression. ## Statistical methods The Shapiro–Wilk test was used to investigate data distribution and showed that the data analyzed in this study presented with normal distribution. Demographic, quality of life (SF-36), and pain (NRS) data were analyzed using the Mann–Whitney test. Analgesic medication and neuropathic pain (DN4) were analyzed using Pearson’s chi-square test and corrected with Yates’ continuity correction where applicable. Depressive symptoms (PHQ-9) were analyzed using Cochran’s Q test. Correlations were analyzed using Pearson’s correlation coefficient. Statistical significance was set at p ≤ 0.05. ## Results From 182 breast cancer surgeries performed between December 2015 and April 2016, 133 cases did not meet inclusion criteria because different types of surgery were performed including mastectomy only, lumpectomy or other breast-sparing surgery. A total of 49 patients were randomized and allocated to the Group II—general anesthesia with SAM + PECS I protocol ($$n = 25$$) or Group I general anesthesia only protocol ($$n = 24$$). For the follow-up in this study, which was performed 12 months after surgery, five patients were excluded. A total of 44 patients (22 in each group) were included in the analysis (Fig. 1).Figure 1CONSORT flowchart of the surgeries performed during study development. General + SAM/PECS I: patients submitted to general anesthesia with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy. General anesthesia only: patients submitted to general anesthesia only during mastectomy. ## Baseline data No complications related to nerve block procedure were reported. There were no differences between the two groups in terms of age, body mass index, age at menopause, duration of mastectomy surgery, duration of the reconstruction procedure, and the number of lymph nodes removed, as presented in Table 1.Table 1Demographic, clinical, and surgical data. GroupGeneral anesthesiaGeneral + SAM/PECS IMann–WhitneyMedian$25\%$$75\%$Median$25\%$$75\%$pAge52.048.064.057.046.067.00.488Body mass index26.023.928.327.825.929.40.260Menopause age48.043.050.051.040.052.00.308Duration of the mastectomy (min)907712087.5601000.319Duration of the reconstruction (min)180103.8240180117.5241.30.802Number of lymph node removal13.09.020.022.54.027.00.165Data showing in median and quartiles. General + SAMP/PECS I: patients submitted to the general anesthesia associated with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy procedure ($$n = 22$$ patients). General anesthesia: patients submitted to general anesthesia only during mastectomy procedure ($$n = 22$$ patients). There were no statistically significant differences between the two groups regarding the use of neoadjuvant therapy, neoadjuvant hormonal therapy, neoadjuvant trastuzumab, adjuvant therapy, adjuvant chemotherapy, adjuvant hormonal therapy, adjuvant trastuzumab, and adjuvant radiation therapy, as shown in Table 2.Table 2Data regarding the non-surgical treatment. GroupGeneral anesthesiaGeneral + SAM/PECS IX2n%n%pNeoadjuvant therapyNo1254.51150.00.763Yes1045.51150.0Neoadjuvant hormonal therapyNo2090.922100.00.469Yes29.100.0Neoadjuvant trastuzumabNo1986.41672.70.455Yes313.6627.3Adjuvant therapyNo14.500.01.00Yes2195.522100.0Adjuvant chemotherapyNo1150.01672.70.122Yes1150.0627.3Adjuvant hormonal therapyNo418.2313.61.00Yes1881.81986.4Adjuvant trastuzumabNo2195.51777.30.187Yes14.5522.7Adjuvant radiation therapyNo627.3627.31.000Yes1672.71672.7General + SAMP/PECS I: patients submitted to the general anesthesia associated with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy procedure ($$n = 22$$ patients). General anesthesia: patients submitted to general anesthesia only during mastectomy procedure ($$n = 22$$ patients). ## Primary outcome: pain No significant difference ($$p \leq 0.54$$) was observed in the NRS pain scores reported by the two patient groups 12 months after mastectomy (Fig. 2A). Figure 2B presents the data regarding the pain subscale of the SF-36 scale 12 months after surgery, no significant difference ($$p \leq 0.138$$) was observed between groups. Figure 2Pain scores and medication consumption in both experimental groups: general anesthesia ($$n = 22$$) and general anesthesia with SAM + PECS I blocks ($$n = 22$$). ( A) Pain levels measured using the Numeric Rating Scale (NRS) 12 months after surgery. ( B) Short-form Health Survey (SF-36) scores regarding the pain domain. ( C) Percentages of patients reporting values above 4 on the Douleur Neuropathique 4 (DN4) scale. ( D) Percentage of affirmative answers in the items of the DN4 questionnaire. Likewise, no significant difference ($$p \leq 0.379$$) was observed in the percentage of patients reporting DN4 punctuation > 4, as illustrated in Fig. 2C. Figure 2D presents the percentage of affirmative answers for each of the 10 items in the DN4 questionnaire. No significant differences were observed in the domains burning ($$p \leq 0.674$$), cold in pain ($$p \leq 1.00$$), electric shocks ($$p \leq 0.272$$), tingling ($$p \leq 0.961$$), pins and needles ($$p \leq 0.295$$), itching ($$p \leq 0.295$$), hypoesthesia to pinprick ($$p \leq 0.229$$) and brushing ($$p \leq 0.317$$) between the groups. There was a reduction in numbness and hypoesthesia to touch in the Group II in comparison with the Group I ($$p \leq 0.047$$ and $$p \leq 0.049$$ respectively, Fig. 2D). Table 3 presents the comparison of the DN4 tool versus clinical evaluation for the probable chronic pain after mastectomy showing no difference between the evaluations. Table 3Comparison of the DN4 tool versus clinical evaluation for the probable chronic neuropathic pain after mastectomy. Probable chronic neuropathic pain after mastectomyStatisticsDN4 testClinical evaluationp valueGeneral$\frac{4}{226}$/220.16General + SAM/PECS I$\frac{2}{223}$/220.32Data regarding the number of patients that presented probable chronic neuropathic pain after mastectomy. General + SAMP/PECS I: patients submitted to the general anesthesia associated with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy procedure ($$n = 22$$ patients). General anesthesia: patients submitted to general anesthesia only during mastectomy procedure ($$n = 22$$ patients). ## Primary outcome: incidence of chronic pain in other body regions Group II showed a reduction in the percentage of patients showing pain in other body regions in comparison with Group I ($$p \leq 0.014$$), as shown in Fig. 3A. Also, Table 4 presents data regarding the clinical evaluation specifying the body region in which the chronic pain occurred. Figure 3(A) Incidence of chronic pain in other regions. ( B) Percentage of patients reporting the use of analgesic medication. ( C) Percentage of patients reporting the use of nonopioid analgesics, opioid analgesics, anxiolytic drugs, antidepressants, anticonvulsants, and muscle relaxants. ( D) Percentage of patients reporting depressive symptoms according to the Patient Health Questionnaire-9 (PHQ-9). The data are presented as the mean ± standard deviation or in terms of percentage of patients. * $p \leq 0.05$ compared with the general anesthesia-only group. Table 4Data specifying the incidence of chronic pain in other body regions. General anesthesiaGeneral + SAM/PECS In%n%Ankle14.5500Legs313.6400Hands14.5514.55Low back14.5529.10Hip14.5500Forearm14.5500Knee14.5500General + SAMP/PECS I: patients submitted to the general anesthesia associated with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy procedure ($$n = 22$$ patients). General anesthesia: patients submitted to general anesthesia only during mastectomy procedure ($$n = 22$$ patients). ## Secondary outcome: analgesic medication No significant difference in the use of analgesic medication 12 months after the surgical procedure was noted between groups ($$p \leq 0.112$$), as shown in Fig. 3B. Figure 3C illustrates the data regarding the use of analgesic medications, categorized into five types according to their drug class. Among nonopioid analgesics, metamizole and paracetamol were included. No significant difference between groups were observed regarding the use of nonopioid analgesics ($$p \leq 0.4121$$), opioid analgesics ($$p \leq 0.99$$), anxiolytic drugs and antidepressants ($$p \leq 0.6981$$), anticonvulsants ($$p \leq 0.99$$) and muscle relaxants ($$p \leq 0.99$$). ## Secondary outcome: depressive symptoms Data referring to the PHQ-9 scale are shown in Fig. 3D. There was no difference between groups before the surgery ($$p \leq 0.65$$). A lower percentage of patients allocated in Group II had depressive symptoms 1 year after surgery, compared with those who received general anesthesia only ($$p \leq 0.012$$). ## Secondary outcome: SF-36 domains Table 5 presents the data from the SF-36 questionnaire 12 months after the surgical procedure. No significant differences between the two groups were observed in all aspects related to quality of life. Table 5Data obtained in the SF-36 domains including functional capacity, physical aspects, general state of health, vitality, social aspects, emotional aspects, and mental health. GroupGeneral anesthesiaGeneral + SAM/PECS IMann–WhitneyMediana$25\%$$75\%$Mediana$25\%$$75\%$pFunctional capacity82.560.090.080.055.085.00.637Physical aspects100.00.0100.00.00.0100.00.295General state of health55.050.070.060.050.065.00.878Vitality57.540.075.060.045.070.00.944Social aspects100.0100.0100.0100.075.0100.00.254Emotional aspects100.00.0100.0100.00.0100.00.824Mental health72.060.080.070.064.084.00.714Data showing in median and quartiles. General + SAMP/PECS I: patients submitted to the general anesthesia associated with serratus anterior muscle (SAM) block and pectoral nerves (PECS) block type I during mastectomy procedure ($$n = 22$$ patients). General anesthesia: patients submitted to general anesthesia only during mastectomy procedure ($$n = 22$$ patients). ## Secondary outcome: interleukins as the predictor of pain and depression Table 6 presents the data correlating the plasma levels of IL-1 beta, IL-6, and IL-10 before and after the surgical procedure versus the pain score obtained in the NRS and DN4 questionnaires, and depression score obtained in the PHQ-9 1 year after surgery. No statistically significant correlations were observed between these parameters. Table 6Multiple regression data correlating the levels of interleukin before and after the surgical procedure with the pain score obtained in the Numeric Rate Scale (NRS), Douleur Neuropathique 4 (DN4) questionnaire, and with the depression score obtained in the Patient Health Questionnaire-9 (PHQ-9) 12 months after surgery. InterleukinNumeric rate scale (NRS)Douleur neuropathique 4 (DN4)Patient health questionnaire-9 PHQ-9IL-1β before mastectomyR2 = 0.0215, $$p \leq 0.34$$R2 = 0.0041, $$p \leq 0.68$$R2 = 0.0136, $$p \leq 0.55$$IL-1β after mastectomyR2 = 0.0263, $$p \leq 0.29$$R2 = 0.0051, $$p \leq 0.64$$R2 = 0.0171, $$p \leq 0.51$$IL-6 before mastectomyR2 = 0.0018, $$p \leq 0.78$$R2 = 0.0138, $$p \leq 0.44$$R2 = 0.0295, $$p \leq 0.38$$IL-6 after mastectomyR2 = 0.0021, $$p \leq 0.76$$R2 = 0.0070, $$p \leq 0.58$$R2 = 0.0281, $$p \leq 0.39$$IL-10 before mastectomyR2 = 0.0019, $$p \leq 0.77$$R2 = 0.0139, $$p \leq 0.44$$R2 = 0.0319, $$p \leq 0.36$$IL-10 after mastectomyR2 = 0.0001, $$p \leq 0.98$$R2 = 0.0257, $$p \leq 0.29$$R2 = 0.0282, $$p \leq 0.39$$Data showing in R2 value and the corresponding p value. ## Discussion To our knowledge, this is the first follow-up study evaluating chronic neuropathic pain after mastectomy in patients who were administered general anesthesia with SAM and PECS I blocks compared with those who received general anesthesia during the surgical procedure. The results showed that 12 months after mastectomy with axillary approach and reconstruction, the patients who received general anesthesia with SAM and PECS 1 blocks did not have a decreased incidence of chronic neuropathic pain; however, they presented a reduction in the descriptors numbness and hypoesthesia; and in the incidence of patients that present chronic pain in other body regions. Importantly, there was a reduction in depressive symptoms in the general anesthesia with SAM and PECS I blocks group. We had chosen not including different types of surgical procedures in our study to have a homogenous sampling and being able to further understand and describe34 the role of SAM + PECS1 focus on this subgroup of interest. Demographic data showed no difference between the groups regarding age, body mass index, menopausal age, and duration of the surgical procedure. Our data agree with the median age at diagnosis of breast cancer in middle-aged and older women35. It is suggested that body mass index may be an important predictor of post-mastectomy chronic pain development36. The age of onset of menopause is important for guiding treatment strategies37. However, in the present study, these parameters did not influence the development of chronic pain. Regarding axillary lymph node dissection, which has been considered a major risk factor for chronic neuropathic pain after mastectomy36, our data showed no statistically significant difference between groups. Chemotherapy and radiation may lead to the development of chronic persistent post-surgical pain38. Also, the lymphatic system dysfunction could affect lymph drainage39 and contribute to chronic pain in general, functional impairment, and depression40,41. It is important to point that our data did not showed difference between groups in those parameters. Additionally, the duration of surgery could increase the severity of acute pain and consequently, has been associated with persistent pain42. Therefore, it is important to emphasize that there were no significant differences between the two groups in terms of these parameters that could influence the development of chronic neuropathic pain after mastectomy. Chronic neuropathic pain after mastectomy is caused by lesions or diseases of the somatosensory nervous system, which may lead to increased pain sensitivity, spontaneous pain, and loss of function43. Also, this type of chronic pain has no purposes of adaptation, devoid of biological value, not respond to typical treatment and last more than six months, causing suffering, distress and contributing to deteriorate the quality of life of the patients44. Several screening tools have been developed to identify possible symptoms of neuropathic pain owing to their varied manifestations45. The DN4 questionnaire is a valuable tool for investigating neuropathic pain profile and assessing its possible mechanisms30,46. DN-4 questionnaire has high sensitivity and specificity in distinguish chronic neuropathic pain from chronic nonneuropathic pain47,48 and is a reliable tool for evaluating chronic neuropathic pain and the effect of regional anesthesia as protecting strategies after breast cancer surgery49. Our study performed a detailed analysis of DN4 items to better understand the chronic neuropathic pain after mastectomy of these patients. Our data are in accordance with the literature, showing that hypoesthesia to touch is a sensory dysfunction, followed by numbness30,50, also there was a reduction in the incidence of patients reporting chronic pain in other regions. General anesthesia with SAM and PECS I blocks reduced the occurrence of hypoesthesia to touch and numbness compared with general anesthesia alone. A possible explanation could be that the patients who received SAM and PECS I blocks experienced a reduction in postoperative pain26. Chronic pain in general is associated with inadequate postoperative pain control8. In addition, the analgesic pattern offered by the SAM + PECS 1 blocks provide excellent analgesia to the breast and axillary regions51–54. The use of the PECS block has been shown to induce a lower incidence of chronic postsurgical pain in general 3 months after breast surgery55. The use of SAM block was also shown to reduce its prevalence 6 months after mastectomy29, which is consistent with our findings. In the pathophysiology of chronic pain after mastectomy, the neuropathic categories are attributed to nerve damage or traction during surgery, particularly targeting the intercostobrachial, medial pectoral, lateral pectoral, thoracodorsal, and long thoracic nerves6,56,57. Thus, it has been postulated that nerve injury, neuroma, phantom breast pain, anatomic changes, axillary web syndrome, and lymphedema could be responsible for the neuropathic pain observed after breast surgery, and local anesthetic blockade of this region could be an alternative to prevent this type of pain56. It is important to highlight that most of the studies that evaluated the development of chronic pain in general were performed with paravertebral blocks and demonstrated controversial results, showing pain reduction58 or no effect59. The analgesic pattern of the paravertebral block reaches the sympathetic and intercostal nerves, while the combination of SAM + PECS I blocks can reach the thoracodorsal and long thoracic nerves, which are thought to be involved in the pathophysiology of chronic post-mastectomy neuropathic pain52. The analgesic pattern of SAM and PECS I blocks could explain the reduction in neuropathic symptoms, such as numbness and hypoesthesia. Our data showed no difference between the two groups regarding the SF-36 questionnaire domains. This tool has been routinely used in patients with breast cancer60 to evaluate quality of life and was used in the present study to identify possible confounding factors in the assessment of chronic pain. The diagnosis and identification of depression can be challenging in view of the wide variety of symptoms presented by patients. Pain and depression are common symptoms exhibited after mastectomy42,61. Psychological determinants, such as depression, is associated with more pain complaints and greater intensity of pain62. The PHQ-9 scale has been reported as a useful tool in assessing depressive symptoms33. Our data showed that patients who received general anesthesia with SAM and PECS I blocks had a lower incidence of depressive symptoms. To our knowledge, there is no association between the use of regional anesthesia and depression. A possible explanation could be that pain and depression share the same neuroanatomical substrate including biological pathways and neurotransmitters and the presence of pain negatively affect the recognition of depression62. In this sense, the use of regional anesthesia could decrease the neuronal reorganization contributing to simultaneously reduce pain and depression63. Also, it could be proposed that the use of SAM and PECS I blocks contributed to decreasing postoperative pain26 and consequently reducing the negative impact of the surgical procedure on the psychological aspects64. Because of the intrinsic clinical relationship between chronic pain in general and depression, this association has been called depression-pain syndrome or depression-pain dyad and has a direct impact on the quality of life and individual health care65. The mechanisms responsible for this depression-pain syndrome are not fully understood, but it is thought to involve neuronal reorganization in response to neuroinflammation in brain areas responsible for emotional processing and pain perception63,66,67. Considering the consumption of analgesic medication after mastectomy, a previous study showed that approximately $40\%$ of patients take analgesic medication for chronic pain in general68. These data are in accordance with our results regarding the group subjected to general anesthesia only, while the patients who received regional and general anesthesia had a lower percentage of analgesic medication consumption. However, there was no statistically significant difference between the two groups, probably because the sample size was calculated for acute pain26. Specifically, our patients had been using a wide variety of medications, including nonopioid analgesics, opioid analgesics, anxiolytics, antidepressants, anticonvulsants, and muscle relaxants. These data are in accordance with a systematic review focusing on analgesic medications for breast surgery, suggesting that anticonvulsants may decrease the incidence of chronic pain; however, the beneficial role of combinations of analgesic medications is not completely understood69. Treatment therapies are important variables that contribute independently to the prediction of chronic pain in general70. Chemotherapy, hormone therapy, and radiotherapy have been proposed to be significantly associated with higher scores for the development of chronic pain36. Our results showed no differences in these parameters, supporting that the reduction in neuropathic descriptors in the regional anesthesia group was independent of the treatment therapies. We performed correlation tests using the plasma levels of cytokines and the chronic pain and depression. The rationale for this correlation is that a sustained increase in cytokine levels could contribute to central sensitization that is responsible for the maintenance of chronic pain71. In addition, neuroinflammation could be responsible for the depletion of brain serotonin, dysregulation of the hypothalamus–pituitary–adrenal axis, and hippocampal neurogenesis alteration, which contribute to depressive symptoms. It has been shown that IL-6 has a pivotal role in the development of pathological pain72, IL-1 drives chronic pain73 and IL-10 could suppress proinflammatory cytokines that modulates chronic pain74. Also, considering depression, IL-6 and IL-10 could be a potential biomarker of prognosis and severity75–77, while IL-1 levels could predict probability of response to antidepressants78. However, in this study, no correlation was observed between the levels of IL-1 beta, IL-6, and IL-10 before and 24 h after surgery, and the NRS, DN4, and PHQ-9 scores obtained 12 months after surgery. A possible explanation for the lack of differences could be attributed to the fact that we evaluated plasma cytokines, which serve as biomarkers, but do not directly reflect the neuroinflammation process, which could be better visualized using the cerebrospinal fluid79. Our current study had some limitations. First, we evaluated chronic neuropathic pain at a single time point. Second, sample size calculation was performed for acute pain and is relatively small but is important to highlight the homogeneity of our patient sample. Also, postoperative pain therapies were not standardized and could be a potential source of bias in the results. Future research could address these limitations. 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--- title: Lysosomal Ca2+ as a mediator of palmitate-induced lipotoxicity authors: - Soo-Jin Oh - Yeseong Hwang - Kyu Yeon Hur - Myung-Shik Lee journal: Cell Death Discovery year: 2023 pmcid: PMC10030853 doi: 10.1038/s41420-023-01379-0 license: CC BY 4.0 --- # Lysosomal Ca2+ as a mediator of palmitate-induced lipotoxicity ## Abstract While the mechanism of lipotoxicity by palmitic acid (PA), an effector of metabolic stress in vitro and in vivo, has been extensively investigated, molecular details of lipotoxicity are still not fully characterized. Since recent studies reported that PA can exert lysosomal stress in addition to well-known ER and mitochondrial stress, we studied the role of lysosomal events in lipotoxicity by PA, focusing on lysosomal Ca2+. We found that PA induced accumulation of mitochondrial ROS and that mitochondrial ROS induced release of lysosomal Ca2+ due to lysosomal Ca2+ exit channel activation. Lysosomal Ca2+ release led to increased cytosolic Ca2+ which induced mitochondrial permeability transition (mPT). Chelation of cytoplasmic Ca2+ or blockade of mPT with olesoxime or decylubiquinone (DUB) suppressed lipotoxicity. Lysosomal Ca2+ release led to reduced lysosomal Ca2+ content which was replenished by ER Ca2+, the largest intracellular Ca2+ reservoir (ER → lysosome Ca2+ refilling), which in turn activated store-operated Ca2+ entry (SOCE). Inhibition of ER → lysosome Ca2+ refilling by blockade of ER Ca2+ exit channel using dantrolene or inhibition of SOCE using BTP2 inhibited lipotoxicity in vitro. Dantrolene or DUB also inhibited lipotoxic death of hepatocytes in vivo induced by administration of ethyl palmitate together with LPS. These results suggest a novel pathway of lipotoxicity characterized by mPT due to lysosomal Ca2+ release which was supplemented by ER → lysosome Ca2+ refilling and subsequent SOCE, and also suggest the potential role of modulation of ER → lysosome Ca2+ refilling by dantrolene or other blockers of ER Ca2+ exit channels in disease conditions characterized by lipotoxicity such as metabolic syndrome, diabetes, cardiomyopathy or nonalcoholic steatohepatitis. ## Introduction Palmitic acid (PA) is an important molecule acting as an effector of lipid injury and metabolic stress in vitro and in vivo. Mechanisms of PA-induced lipotoxicity have been ascribed to endoplasmic reticulum (ER) stress, mitochondrial stress, production of ceramide or lysophosphatidylcholine [1–4], leading to JNK activation, apoptosis or necrosis, depending on the experimental condition and cellular context [3, 5–7]. While ER and mitochondria are well-established target organelles of PA-induced injury [2, 4, 8, 9], lysosomal changes have recently been reported to occur such as decreased lysosomal acidity, altered lysosomal enzyme activity or impaired lysosomal integrity [10–13]. We recently reported that lysosomal Ca2+ is released after treatment with PA, while the cellular consequences of lysosomal Ca2+ release could be distinct depending on the cell types or context of treatment [11, 12]. Since release of lysosomal Ca2+ can also lead to increased cytosolic Ca2+ content ([Ca2+]i) [11, 12] and Ca2+ is one of the most important inducers of mitochondrial permeability transition (mPT) and subsequent cell death [14, 15], we conducted this investigation based on our hypothesis that lysosomal Ca2+ release by PA can lead to lipotoxic cell death through mPT. ## PA elicits lysosomal Ca2+ release PA can induce reactive oxygen species (ROS) by compromising mitochondrial complex I and III [11, 16], which can activate lysosomal Ca2+ channel [12, 17]. Lysosomal Ca2+ release due to lysosomal Ca2+ exit channel activation can increase cytosolic Ca2+ content ([Ca2+]i), which, in turn, can induce mitophagy, lysosomal stress response [11, 12] or diverse types of cell death [18–20]. Hence, we studied whether PA-induced lipotoxicity entails mitochondrial ROS-induced lysosomal Ca2+ release and increased [Ca2+]i. When we treated HepG2 cells with PA, significant lipotoxic cell death was observed in a dose range of 500 ~ 1,000 μM, as revealed by SYTOX Green staining (Fig. 1A). Furthermore, accumulation of mitochondrial ROS stained with MitoSOX was well visualized in the same dose range of PA, which was significantly reduced by MitoTEMPO, a mitochondrial ROS quencher (Fig. 1B), indicating mitochondrial ROS generation by PA. As PA doses higher than 500 μM could be unphysiologically high [21], we employed 500 μM of PA in the following experiments to minimize unphysiological effects of high dose of PA. In an experiment to find causal relationship between lipotoxic cell death and mitochondrial ROS, PA-induced death of HepG2 cells was significantly reduced by MitoTEMPO (Fig. 1C), suggesting that mitochondrial ROS generation is a critical event in PA-induced lipotoxicity. When we studied possible changes of lysosomal Ca2+ after PA treatment, concentration of lysosomal Ca2+ ([Ca2+]Lys) stained with Oregon Green-488 BAPTA-1 dextran (OGBD) was significantly decreased in HepG2 cells treated with PA (Fig. 1D), suggesting release of lysosomal Ca2+ by PA treatment, similar to the results using other types of cells [11, 12]. When we indirectly estimated lysosomal Ca2+ content by calculating area under the curve (AUC) of cytosolic Ca2+ concentration ([Ca2+]i) tracing using Fluo-3 AM staining after treatment with Gly-Phe β-naphthylamide (GPN), a lysosomotropic agent [22], lysosomal Ca2+ content was again significantly reduced by PA treatment (Fig. 1E). Decrease of [Ca2+]Lys was abrogated by MitoTEMPO (Fig. 1D), suggesting that mitochondrial ROS produced by PA treatment induces lysosomal Ca2+ release. We also studied whether perilysosomal Ca2+ release can be observed after PA treatment employing GCaMP3-ML1, a probe detecting perilysosomal Ca2+ release [22]. In GCaMP3-ML1-transfected HepG2 cells, perilysosomal Ca2+ release was not directly visualized by PA treatment (Fig. 1F). However, perilysosomal Ca2+ release after treatment with GPN was markedly reduced (Fig. 1F), suggesting pre-emptying or release of lysosomal Ca2+ after PA treatment, similar to the results using other types of cells [12, 17].Fig. 1PA reduces lysosomal Ca2+.A After treatment of HepG2 cells with 250–1000 μM PA for 24 h, cell death was determined using SYTOX Green. B After treatment of HepG2 cells with 250–1000 μM PA for 24 h in the presence or absence of 10 μM MitoTEMPO, PA-induced mitochondrial ROS was measured by MitoSOX followed by flow cytometry (lower panel). Representative scattergrams are shown (upper panel). C After treatment of HepG2 cells with 500 μM PA for 24 h in the presence or absence of 10 μM MitoTEMPO, cell death was determined using SYTOX Green. D Cells were loaded with Oregon Green-488 BAPTA-1 Dextran (OGBD) for 16 h and chased for 4 h. Cells were then treated with PA or BSA for 6 h in the presence or absence of 10 μM MitoTEMPO, followed by confocal microscopy to determine [Ca2+]Lys (right). Representative fluorescence images are shown (left panel). Scale bar, 20 μm. E After staining with Fluo-3 AM for 30 min, cells were treated in PA or BSA for 6 h, followed by confocal microscopy to monitor GPN-induced changes of lysosomal Ca2+ release and [Ca2+]i increase (left). AUC of the curve was calculated as an estimate of lysosomal Ca2+ content (right). F Cells transfected with GCaMP3-ML1 were treated with PA or BSA for 6 h, and then GPN-induced change of GCaMP3-ML1 fluorescence was determined by confocal microscopy to visualize perilysosomal Ca2+ release (right). Representative fluorescence images are shown (left panel). Scale bar, 10 μm. G [Ca2+]i in HepG2 cells treated with PA for 6 h was determined by staining with Fluo-3 AM (middle) or ratiometric analysis after Fura-2 loading (right). Representative Fluo-3 fluorescence images are shown (left panel). Scale bar, 20 μm. H [Ca2+]i in HepG2 cells treated with PA for 6 h in the presence or absence of 10 μM MitoTEMPO was determined by ratiometric analysis after Fura-2 loading. I After treatment of HepG2 cells with PA in the presence or absence of 10 μM CA-074Me for 24 h, cell death was determined using SYTOX Green. Data are expressed as the means ± SD of three independent experiments. Statistical comparisons were performed using one-way ANOVA with Tukey’s multiple comparison test (A–D, H, I) or two-tailed unpaired Student’s t-test (vehicle and PA comparisons in E–G). (* $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001$; ns not significant). When we determined [Ca2+]i and studied whether release of lysosomal Ca2+ leads to increased cytosolic Ca2+ content ([Ca2+]i), [Ca2+]i estimated by Fluo-3 AM staining was significantly increased by PA (Fig. 1G), supporting release of lysosomal Ca2+ to the cytosol. When ratiometric measurement of [Ca2+]i after Fura-2 staining was conducted to avoid interference due to uneven loading or photobleaching [23], increased [Ca2+]i by PA treatment was again well observed, which was reversed by MitoTEMPO (Fig. 1G, H), indicating that mitochondrial ROS-induced lysosomal Ca2+ release leads to increased [Ca2+]i. Since molecules other than Ca2+ might be released from lysosome and could affect cell viability, we studied effect of an inhibitor of cathepsin B that has been reported to be released from hepatocytes after PA treatment and to induce lipotoxic cell death [24]. Ca-074Me, a cell-permeable inhibitor of cathepsin B, did not inhibit cell death by PA, suggesting no role of lysosomal cathepsin B release in lipotoxic cell death of HepG2 cells (Fig. 1I). Taken together, these data demonstrate that PA-induced mitochondrial ROS leads to release of lysosomal Ca2+ to cytoplasm and increased [Ca2+]i, contributing to lipotoxic HepG2 cell death. ## mPT pore (mPTP) opening mediates lysosomal Ca2+ loss-driven cell death in response to PA We next studied whether PA-induced release of Ca2+ from lysosome into cytosol can induce mPTP opening because Ca2+ is one of the most important inducers of mPT which can lead to mitochondrial catastrophe and cell death [2, 25]. When cells were stained by calcein-AM/CoCl2 staining, mPTP opening visualized by Co2+ quenching of mitochondrial matrix calcein fluorescence [26] was well detected after PA treatment of HepG2 cells, showing occurrence of mPT by PA (Fig. 2A). PA-induced mPT was abrogated by BAPTA-AM chelating intracellular Ca2+ (Fig. 2A), indicating Ca2+-dependent mPT after PA treatment. Since mPT can lead to cell death such as necrosis or apoptosis depending on the severity of mPT and cellular context [15, 27, 28], we studied the role of mPT in HepG2 cell death by PA using inhibitors of mPT. Indeed, mPT inhibitors such as olesoxime [29] or decylubiquinone (DUB) [14], significantly reduced PA-induced cell death assessed by SYTOX Green staining or LDH release assay (Fig. 2B, C). Reduced lipotoxic cell death by olesoxime or DUB was accompanied by significantly reduced mPT as evidenced by decreased Co2+ quenching of calcein fluorescence (Fig. 2D, E), indicating that Ca2+-mediated mPTP opening is an important mechanism of PA-induced lipotoxicity. PA-induced HepG2 cell death was also significantly reduced by BAPTA-AM, confirming the role of increased [Ca2+]i and Ca2+-mediated mPTP in lipotoxicity of HepG2 cells (Fig. 2F). As a consequence of mitochondrial damage associated with mPT, mitochondrial potential determined by TMRE staining was significantly lowered by PA treatment of HepG2 cells (Fig. 2G).Fig. 2mPTP opening by PA due to lysosomal Ca2+ release. A After treatment of HepG2 cells with 500 μM PA in the presence or absence of 30 μM BAPTA-AM for 24 h, mPTP opening was assessed by flow cytometry after calcein-AM loading and Co2+ quenching (right). Representative histograms are shown (left). B, C After treatment of HepG2 cells with PA in the presence or absence of 100 μM olesoxime (B) or 200 μM DUB (C) for 24 h, cell death was determined using SYTOX Green (left) or LDH release assay (right). D, E After treatment of HepG2 cells with PA in the presence or absence of 100 μM olesoxime (D) or 200 μM DUB (E) for 24 h, mPTP opening was determined as in (A) (right). Representative histograms are shown (left). F After the same treatment as in (A), cell death was determined using SYTOX Green (left) or LDH release assay (right). G After treatment of HepG2 cells with PA as in (A), mitochondrial potential was determined by TMRE staining (right). Representative fluorescence images are shown (left). Scale bar, 20 μm. Data are expressed as the means ± SD of three independent experiments. All statistical comparisons were performed using one-way ANOVA with Tukey’s multiple comparison test. ( $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001$; ns not significant). ## ER → lysosome calcium flux and SOCE in lipotoxicity While we showed the role of lysosomal Ca2+ release in lipotoxicity, lysosomal Ca2+ might not be a sufficient source of Ca2+ required for full execution of cellular process requiring Ca2+, since lysosomal volume is $1\%$ of cell volume and lysosomal Ca2+ pool is a relatively small Ca2+ reservoir [30]. We thus wondered whether release or emptying of lysosomal Ca2+ content in HepG2 cells treated with PA could be replenished from endoplasmic reticulum (ER), the largest intracellular Ca2+ reservoir [31] which has been observed when lysosomal Ca2+ emptying occurs (i.e., ER → lysosome Ca2+ refilling) [12, 31]. When [Ca2+]ER was determined after PA treatment of HepG2 cells using GEM-CEPIA1er [32] in a Ca2+-free Krebs-Ringer bicarbonate (KRB) buffer to abolish possible store-operated Ca2+ entry (SOCE) [33], Ca2+ concentration in the ER ([Ca2+]ER) was significantly reduced (Fig. 3A), suggesting that ER Ca2+ is mobilized probably to replenish lysosomal Ca2+ loss. When [Ca2+]ER was determined without removal of extracellular Ca2+, [Ca2+]ER was not significantly reduced likely due to activation of store-operated Ca2+ entry (SOCE) after ER Ca2+emptying (Fig. 3A), which suggests that SERCA was not inhibited by PA since SERCA inhibition reduces [Ca2+]ER regardless of extracellular Ca2+ [32] and eliminates the possibility of increased [Ca2+]i after PA treatment due to SERCA inhibition. Fig. 3Role of ER → lysosome Ca2+ refilling in lipotoxicity. A After treatment of GEM-CEPIA1er-transfected HepG2 cells with PA for 6 h in a Ca2+-free KRBB (right) or a full medium (left), [Ca2+]ER was determined by confocal microscopy. B After treatment of OGBD-labeled cells with PA for 6 h, cells were incubated in a fresh medium without PA in the presence or absence of 10 μM dantrolene (Dan), 10 μM TPEN or 3 μM Xestospongin C (Xesto C). Recovery of [Ca2+]Lys after removal of PA was determined by confocal microscopy (lower). Representative fluorescence images are shown (upper). Scale bar, 20 μm. C After the same treatment as in B, [Ca2+]i was determined by radiometric analysis after Fura-2 loading. D After the same treatment as in (B), cell death was evaluated by SYTOX Green staining. E After treatment of GEM-CEPIA1er transfected cells loaded with OGBD with PA for 6 h and washout, recovery of [Ca2+]Lys and changes of [Ca2+]ER were monitored simultaneously in the absence of extracellular Ca2+. Data are expressed as the means ± SD of three independent experiments. Statistical comparisons were performed using two-tailed Student’s t-test (A) or one-way ANOVA with Tukey’s multiple comparison test (B–D). ( $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001$; ns not significant). Since these results suggested replenishment of lysosomal Ca2+ depletion by ER Ca2+, we next studied whether ER to lysosomal Ca2+ movement indeed occurs after PA treatment inducing lysosomal Ca2+ release. When PA was removed after treatment for 6 h, a decrease of [Ca2+]Lys by PA treatment was recovered (Fig. 3B). To study the role of ER Ca2+ in the recovery of reduced [Ca2+]Lys, we studied the effect of blockade of ER Ca2+ exit channels that could be routes of ER to lysosomal Ca2+ movement during the recovery of [Ca2+]Lys. When we employed Xestospongin C, an IP3R antagonist, recovery of decreased [Ca2+]Lys after removal of PA was not significantly affected (Fig. 3B). On the other hand, dantrolene, an antagonist of ryanodine receptor (RyR), another ER Ca2+ exit channel, significantly suppressed the recovery of suppressed [Ca2+]Lys after removal of PA (Fig. 3B), suggesting involvement of RyR channel in ER → lysosome Ca2+ refilling after lysosome Ca2+ release by PA. When we chelated ER Ca2+ with a membrane-permeant metal chelator N,N,N’,N’-tetrakis (2-pyridylmethyl) ethylene diamine (TPEN) that has a low Ca2+ affinity and can chelate ER Ca2+ but not cytosolic Ca2+ [34], recovery of [Ca2+]Lys after removal of PA was significantly inhibited (Fig. 3B), again supporting the role of ER Ca2+ in the recovery of lysosomal Ca2+. We next studied the effect of blockade of ER Ca2+ exit channels on the increase of [Ca2+]i after PA treatment. Again, dantrolene but not Xestospongin C, significantly suppressed the increase of [Ca2+]i after PA treatment determined by ratiometric measurement following Fura-2 loading (Fig. 3C), suggesting that ER → lysosome Ca2+ refilling through RyR channel contributes to the increase of [Ca2+]i after PA treatment by replenishing decreased lysosomal Ca2+ content and sustaining lysosomal Ca2+ release. TPEN that reduced recovery of decreased [Ca2+]Lys also attenuated increase of [Ca2+]i after PA treatment (Fig. 3C). When cell death was determined, dantrolene but not Xestospongin C, significantly suppressed the cell death after PA treatment (Fig. 3D), suggesting that ER → lysosome Ca2+ refilling through RyR channel contributes to the PA-induced cell death probably by supporting continuous increase of cytosolic Ca2+ and subsequent mPTP opening. TPEN also alleviated HepG2 cell death by PA (Fig. 3D), substantiating supportive role of ER Ca2+ in lipotoxicity. To study dynamic changes of [Ca2+]ER and its temporal relationship with lysosomal Ca2+ refilling, we simultaneously traced [Ca2+]ER and [Ca2+]Lys in cells transfected with GEM-CEPIA1er and loaded with OGBD. When organelle [Ca2+] was monitored in cells that have reduced [Ca2+]Lys after PA treatment for 6 h and subsequently were incubated in a Ca2+-free medium, a decrease of [Ca2+]ER occurred in parallel with an increase of [Ca2+]Lys (Fig. 3E), which strongly supports that ER → lysosome Ca2+ refilling occurs during HepG2 cell lipotoxicity. Decrease of [Ca2+]ER after PA treatment only in the absence of extracellular Ca2+ suggested SOCE without SERCA inhibition because SERCA inhibition would decrease [Ca2+]ER regardless of extracellular Ca2+ [32]. Thus, we next studied possible occurrence and role of SOCE in lipotoxicity by PA. Since ER luminal protein STIM1 oligomerizes and recruits plasma membrane SOCE channel ORAI1 to activate Ca2+ entry when ER Ca2+ stores are reduced [33], we studied expression pattern of STIM1 after PA treatment that reduced ER Ca2+ store due to ER → lysosome Ca2+ refilling. In control-treated cells, neither STIM1 oligomerization nor co-localization between STIM1 and ORAI1 was observed (Fig. 4A). In contrast, both STIM1 oligomerization and co-localization between STIM1 and ORAI1 were well observed after PA treatment (Fig. 4A), supporting that reduced ER Ca2+ content after PA treatment induced SOCE activation. To validate the role of SOCE in intracellular Ca2+ flux, we studied the effect of BTP2, a blocker of SOCE [35, 36] on [Ca2+]ER. Indeed, in the presence of BTP2, [Ca2+]ER was significantly reduced by PA even without removal of extracellular Ca2+. In contrast, without BTP2, PA-induced reduction of [Ca2+]ER was not observed in the presence of extracellular Ca2+ (Fig. 4B), suggesting the occurrence of SOCE after PA treatment likely due to ER → lysosome Ca2+ refilling and decreased [Ca2+]ER unrelated to SERCA inhibition. Furthermore, the increase of [Ca2+]i by PA determined by Fluo-3 AM staining or ratiometric measurement after Fura-2 loading was significantly attenuated by BTP2 (Fig. 4C, D), indicating the role of SOCE in the increase of [Ca2+]i by PA. Furthermore, PA-induced death of HepG2 cells was significantly reduced by BTP2 (Fig. 4E), supporting the role of SOCE in Ca2+-mediated lipotoxicity. EGTA chelating extracellular Ca2+ also significantly reduced increase of [Ca2+]i and death of HepG2 cells after PA treatment (Fig. 4C, F), further supporting the role of extracellular Ca2+ flux in the increase of [Ca2+]i and subsequent lipotoxicity. Fig. 4SOCE in lipotoxicity. A After treatment of HepG2 cells transfected with YFP-STIM1 and mCherry-Orai1 with PA for 6 h, STIM1 aggregation and its co-localization with ORAI1 were examined at the middle and bottom of the cells by confocal microscopy (left). Scale bar, 10 μm. Pearson’s correlation coefficient was calculated as an index of co-localization of YFP-STIM1 and mCherry-Orai1 (right). B After treatment of GEM-CEPIA1er-transfected cells with PA in the presence or absence of 10 μM BTP2 for 6 h, [Ca2+]ER was determined by confocal microscopy. C, D After the same treatment as in (B), [Ca2+]i was determined by confocal microscopy after Fluo-3 AM loading (C) or ratiometric analysis after Fura-2 loading (D). E, F After treatment of cells with PA in the presence or absence of 10 μM BTP2 (E) or 2 mM EGTA (F) for 24 h, cell death was evaluated using SYTOX Green. Data are expressed as the means ± SD of three independent experiments. Statistical comparisons were performed using two-tailed Student’s t-test (A) or one-way ANOVA with Tukey’s multiple comparison test (B–F). ( $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001$; ns not significant). ## In vivo lipotoxicity Based on our in vitro results showing the role of lysosomal Ca2+ release coupled with ER → lysosome Ca2+ refilling in mPT and cell death in vitro, we next studied the effect of blockade of lysosomal Ca2+-mediated mPT on lipotoxicity in vivo. When mice were injected with ethyl palmitate followed by LPS administration, death of hepatocytes was observed by TUNEL staining which was accompanied by elevated serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST) (Fig. 5A, B), similar to a previous paper [37]. When dantrolene that was able to inhibit PA-induced hepatocyte death through blockade of the increase of [Ca2+]i was administered to mice before ethyl palmitate injection, death of hepatocytes identified by TUNEL staining was significantly ameliorated, suggesting blockade of in vivo lipotoxicity by dantrolene (Fig. 5A). Elevated serum ALT/AST levels after ethyl palmitate followed by LPS administration were also significantly reduced by dantrolene pretreatment (Fig. 5B). In addition, accumulation of mitochondrial ROS stained by MitoSOX was observed in the liver tissue of mice treated with LPS + ethyl palmitate (Fig. 5C), which is in line with mitochondrial ROS accumulation in HepG2 cells treated with PA in vitro. Such mitochondrial ROS accumulation in vivo was ameliorated by dantrolene administration (Fig. 5C). We also studied whether blockade of mPT could inhibit lipotoxicity in vivo based on our in vitro results showing the role of mPT in PA-induced lipotoxicity. When we pretreated mice with DUB, an inhibitor of mPT, hepatocyte death detected by TUNEL staining or elevated serum ALT/AST levels was significantly reduced (Fig. 5A, B), indicating the role of mPT in lipotoxicity in vivo. Mitochondrial ROS in the liver tissue of mice treated with LPS + ethyl palmitate was also reduced by DUB (Fig. 5C). These results showing amelioration of PA-induced hepatocyte death in vivo by dantrolene or DUB, suggest that lipotoxicity of hepatocytes in vivo can be mediated by Ca2+-mediated mPT and indicate the role of lysosomal Ca2+ release supported by ER → lysosome Ca2+ refilling and SOCE in mPT-induced lipotoxicity observed in vitro. Fig. 5In vivo lipotoxicity. A After injection of ethyl palmitate and LPS to C57BL/6 mice with or without pretreatment with dantrolene or DUB, cell death was evaluated by TUNEL staining of hepatic sections (right). Representative TUNEL staining is presented (left) ($$n = 8$$–9/group). Insets were magnified. Scale bar, 200 μm. B In serum* from mice of (A), ALT and AST levels were determined using a blood chemistry analyzer ($$n = 8$$–9/group). C In frozen hepatic sections from the mice of (A), mitochondrial ROS accumulation was determined by MitoSOX staining ($$n = 5$$–6/group) (right). Representative fluorescence images are shown (left). Scale bar, 20 μm. Data are presented as the means ± SD. All statistical comparisons were performed using one-way ANOVA with Tukey’s multiple comparison test. (* $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001$; ns not significant). ## Discussion It is well established that PA, as an effector of metabolic stress in vitro, can induce stress in diverse organelles such as ER and mitochondria [2, 4, 8, 9]. However, recent investigation showed that PA can induce stress or dysfunction of lysosome as well [10–13], such as elevated pH or reduced Ca2+ content due to release of Ca2+ from lysosome. Increased cytosolic Ca2+ can mediate diverse beneficial or harmful effects on cells. Since Ca2+ is a well-known inducer of mPT [14, 15], we hypothesized that lysosomal Ca2+ release might contribute to cell death after treatment with PA or lipotoxicity. Indeed, we observed that Ca2+ could be released from lysosome after PA treatment, which is likely due to activation of lysosomal Ca2+ exit channel by mitochondrial ROS. After release of lysosomal Ca2+, [Ca2+]i was increased, which imposed mPT and subsequent death. Cell death due to mPT caused by PA-induced lysosomal Ca2+ release was inhibited by mPT inhibitors such as olesoxime or DUB, showing the role of lysosomal Ca2+ release and consequent mPT in lipotoxicity, which is consistent with previous papers reporting mPT induction by PA [2, 38]. Identity of lysosomal Ca2+ exit channel responsible for PA-induced lysosomal Ca2+ release is not clear. Previous papers have reported the role of TRPML1 or TRPM2 channel in Ca2+- or Zn2+-mediated cell death [39–41]. We have also studied the effect of inhibitors of TRPML1 or TRPM2. However, ML-SI1, ML-SI3 or N-(p-amylcinnamoyl)anthranilic acid inhibiting TRPML1, TRPML$\frac{1}{2}$/3 and TRPM2, respectively [42–44], did not inhibit PA-induced lipotoxic death of HepG2 cells (Oh S-J et al., unpublished results). Furthermore, Ned-19, an inhibitor of TPC$\frac{1}{2}$ which has been reported to be associated with ischemia-reperfusion injury [45], was also without effect (Oh S-J et al., unpublished results). Besides such lysosomal Ca2+ exit channels, other members of the TRPM family or those belonging to different families might play a role in Ca2+-mediated lipotoxic cell death, which could be a subject for further studies. It remains to be clarified which lysosomal Ca2+ channel is involved in lipotoxicity due to lysosomal Ca2+-mediated mPT. It has been demonstrated that ER → lysosome Ca2+ flux occurs when lysosomal Ca2+ content is lowered due to lysosomal Ca2+ release by mitochondrial stressors [12, 31]. Such refilling of lysosomal Ca2+ pool from ER Ca2+ pool occurring when lysosomal Ca2+ content is lowered, is likely due to small lysosomal volume accommodating Ca2+ which is $10\%$ of ER Ca2+ volume [30]. Thus, while [Ca2+]Lys is* comparable to [Ca2+]ER, lysosomal Ca2+ content might not be sufficient for progression of events requiring Ca2+ flux. Besides ER Ca2+ pool, another source of lysosomal Ca2+ could be endocytic pathway. However, most of endocytic Ca2+ has been reported to be dissipated before reaching lysosome [46], supporting the importance of ER Ca2+ as a dominant source of lysosomal Ca2+ pool. The role of ER → lysosome Ca2+ flux in diverse pathological and physiological conditions could be an intriguing topic to be explored in future studies. When we studied whether similar phenomenon occurs after PA treatment of HepG2 cells, ER → lysosome Ca2+ refilling could be clearly demonstrated by simultaneous monitoring of [Ca2+]ER and [Ca2+]Lys during incubation in a Ca2+-free medium ensuring recovery of [Ca2+]Lys after removal of PA. Furthermore, blockade of ER → lysosome Ca2+ refilling with dantrolene, an inhibitor of RyR Ca2+ exit channel on ER or chelation of ER Ca2+ by TPEN could inhibit lipotoxicity, which indicates that intracellular Ca2+ flux from ER occurs to sustain lysosomal Ca2+ release, resulting in cell death. Ca2+ flux from ER to lysosome, in turn, induced ER Ca2+ emptying which activated SOCE. Occurrence of SOCE after PA treatment of HepG2 cells was evidenced by no decrease of [Ca2+]ER in cells treated with PA without removal of extracellular Ca2+, reappearance of the decrease of [Ca2+]ER by BTP2 even in the presence of extracellular Ca2+ and STIM1 aggregation co-localized with ORAI1. Functional role of SOCE after treatment with PA was demonstrated by inhibition of lipotoxicity by BTP2 or extracellular Ca2+ chelation with EGTA. Furthermore, occurrence of SOCE and no decrease of [Ca2+]ER in cells treated with PA without removal of extracellular Ca2+ supports that increase of [Ca2+]i after PA treatment is not due to direct release of Ca2+ from ER through SERCA inhibition, since increase [Ca2+]i due to SERCA inhibition would not be affected by extracellular Ca2+ [32]. While SERCA inhibition is an important component of ER stress associated with lipid overload or obesity in vivo inducing altered membrane phospholipid composition [47], SERCA might not be important in acute lipotoxicity of HepG2 cells in vitro. However, we do not eliminate the possibility that ER stress might contribute to lipotoxicity in vitro. In fact, we have observed that CHOP, an important player in ER stress-induced cell death, can be activated by mitochondrial ROS produced after PA treatment [48]. In our experiments to investigate the role of increased Ca2+ and mPT in lipotoxicity in vivo, we observed that DUB inhibiting mPT and dantrolene abrogating [Ca2+]i increase and cell death in vitro by PA could inhibit lipotoxic death of hepatocytes in vivo. Reduction of mitochondrial ROS by dantrolene or DUB on mitochondrial ROS could be due to a feed-forward response leading to further ROS release following Ca2+-mediated mPT [49, 50] which was inhibited by dantrolene or DUB. Dantrolene is a candidate for therapeutic drug against malignant hyperthermia or Alzheimer’s disease [51, 52]. DUB has also been studied as a potential therapeutic agent against Friedreich’s ataxia [53] or tumor-induced angiogenesis [54]. Our results suggest the possibility that dantrolene or DUB could be considered candidates for drug agents against diseases characterized by lipotoxicity such as nonalcoholic steatohepatitis or cardiomyopathy. While we have shown the contribution of mPT induced by lysosomal Ca2+ release as a mechanism of lipotoxicity in vitro and in vivo, other previously reported mechanisms of lipotoxicity such as ER or mitochondrial stress and generation of ceramide or lysophosphatidylcholine [1–4] could also play an important role depending on the cellular context or environmental condition, and optimal strategy against lipotoxicity could be different accordingly. ## Reagents Reagents in this study were purchased from following sources: Oregon Green-488 BAPTA-1 dextran (OGBD, O6789), Fluo-3 AM (F23915), MitoSOXTM Red (M36008), SYTOX Green (S7020), BAPTA-AM (O6807), calcein-AM (C1430), Fura-2 (F1221) from Thermo Fisher Scientific; palmitate (PA, P0500), decylubiquinone (DUB, D7911), bovine serum albumin (BSA, A9418), cobalt chloride (CoCl2, 232696), ethylene glycol-bis (2-aminoethylether)-N,N,N′,N′-tetraacetic acid (EGTA, 03777), MitoTEMPO (SML0737), CA-074Me [205531] from Sigma-Aldrich; Gly-Phe β-naphthylamide (GPN, ab145914) from Abcam; BTP2 (S8380) from Selleckchem; CytoTox 96® Non-Radioactive Cytotoxicity Assay (G1780) from PromegaTM Corporation. ## Cell culture and treatment HepG2 cells obtained from Korean Cell Line Bank (KCLB) were grown in DMEM medium (Welgene, LM-001-05)-$1\%$ penicillin–streptomycin–amphotericin B mixture (Lonza, 17-745E) supplemented with $10\%$ fetal bovine serum (Corning, 35-010-CV). Cells were tested for mycoplasma contamination using a Mycoplasma PCR Detection Kit (e-MycoTM, 25236, iNtRON Biotechnology). PA stock solution was prepared by dissolving palmitate in $70\%$ ethanol and heating at 56 °C. PA stock solution was diluted in $2\%$ fatty acid-free BSA-DMEM before treatment. For in vitro treatment, following concentrations were employed: OGBD, 100 μg/ml; Fluo-3 AM, 5 μM; MitoSOXTM Red, 5 μM; SYTOX Green Nucleic Acid Stain, 1 μM; BAPTA-AM, 30 μM; calcein-AM, 1 μM; palmitic acid (PA), 500 μM; decylubiquinone (DUB), 200 μM; cobalt chloride (CoCl2), 2 mM; Gly-Phe β-naphthylamide (GPN), 200 μM; BTP2, 10 μM; MitoTEMPO, 10 μM; EGTA, 2 mM; CA-074Me, 10 μM. ## SYTOX Green nucleic acid staining Cell death was evaluated using SYTOXTM Green Nucleic Acid Stain kit (Thermo Fisher Scientific, S7020). Briefly, HepG2 cells were seeded in a 24-well plate. After 24 h, cells were treated with PA with or without indicated compound for 24 h. Cells were then incubated with SYTOX Green (1 μM) for 30 min at 37 °C. SYTOX Green fluorescence was measured by flow cytometry using BD FACSVerse and FACSCanto II (BD Biosciences, San Jose, CA, USA). Data analysis was performed using FlowJo software (10.8, FlowJo, LLC, BD Biosciences). ## LDH release assay Cell death was assessed using an LDH release assay kit (PromegaTM Corporation, G1780). Briefly, HepG2 cells seeded 4 × 104 well in a 96-well plate were treated with PA with or without indicated compound for 24 h. Culture supernatant was collected and analyzed according to the manufacturer’s protocol. ## Transfection and plasmids Cells were transfected with plasmids such as GCaMP3-ML1, GEM-CEPIA1er, YFP-STIM1, 3xFLAG-mCherry Red-Orai1/P3XFLAG7.1 using PolyJetTM In Vitro DNA Transfection Reagent (SigmaGen® Laboratories, SL100688), according to the manufacturer’s protocol. ## Measurement of cytosolic, ER and lysosome Ca2+ contents To measure [Ca2+]Lys, HepG2 cells grown on a chambered coverglass (Thermo Fisher Scientific, 155383) were loaded with 100 μg/ml OGBD, an indicator of lysosomal luminal Ca2+ for 16 h. After incubation in a fresh media for 4 h, cells were treated with BSA or 500 μM PA for 6 h. After washing with Ca2+-free HEPES-buffered saline (HBS), fluorescence was measured using an LSM780 confocal microscope (Zeiss, Oberkochen, Germany) and quantified using ImageJ software. To determine [Ca2+]i by confocal microscopy, HepG2 cells grown on chambered coverglass were loaded with 5 μM Fluo-3 AM for 30 min, an indicator of cytosolic Ca2+. After treatment with 500 μM PA for 6 h, cells were washed with Ca2+-free PBS twice, and fluorescence was recorded using an LSM780 confocal microscope. Fluorescence intensity was quantified using ImageJ software. For ratiometric [Ca2+]i measurement, cells were loaded with 2 μM of the acetoxymethyl ester form of Fura-2 in DMEM at 37 °C for 30 min and then washed with a basal Ca2+ solution (145 mM NaCl, 5 mM KCl, 3 mM MgCl2, 10 mM glucose, 1 mM EGTA, 20 mM HEPES, pH 7.4). Measurements were conducted using MetaFluor on an Axio Observer A1 (Zeiss) equipped with a 150 W xenon lamp Polychrome V (Till Photonics, Bloaaom Drive Victor, NY, USA), a CoolSNAP-Hq2 digital camera (Photometrics, Tucson, AZ, USA), and a Fura-2 filter set. Fluorescence at $\frac{340}{380}$ nm was measured in phenol red-free medium, and converted to [Ca2+]i using the following equation [23].\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left[{{{{\mathrm{Ca}}}}^{2 + }} \right]_{{{\mathrm{i}}}} = {{{\mathrm{K}}}}_d \times \left[{\left({R - R_{\min }} \right)/\left({R_{\max } - R} \right) \times } \right.\left[{F_{\min [380]}/F_{\max [380]}} \right]$$\end{document}Ca2+i=Kd×R−Rmin/Rmax−R×Fmin[380]/Fmax[380]where Kd = Fura-2 dissociation constant (224 nM at 37 °C), Fmin[380] = the 380 nm fluorescence in the absence of Ca2+, Fmax[380] = 380 nm fluorescence with saturating Ca2+, $R = 340$/380 nm fluorescence ratio, Rmax = $\frac{340}{380}$ nm ratio with saturating Ca2+, and Rmin = $\frac{340}{380}$ nm ratio in the absence of Ca2+. To determine ER Ca2+ contents ([Ca2+]ER), HepG2 cells were grown on chambered coverglass and transfected with a GEM-CEPIA1er plasmid, a ratiometric fluorescent indicator of ER Ca2+. After 24 h, cells were treated with BSA or 500 μM PA for 6 h, and then washed with Ca2+-free KRBB (Sigma-Aldrich, K4002). GEM-CEPIA1er fluorescence was measured using an LSM780 confocal microscope at an excitation wavelength of 405 nm and an emission wavelength of 466 or 520 nm. Fluorescence ratio F466/F520 was calculated as an index of [Ca2+]ER [32]. ## GCaMP3-ML1 Ca2+ imaging To detect perilysosomal Ca2+ release, HepG2 cells were transfected with a GCaMP3-ML1 Ca2+ probe, a lysosome-targeted genetically-encoded Ca2+ sensor. Twenty-four h after transfection, cells were treated with BSA or 500 μM PA for 6 h. Perilysosomal Ca2+ release was recorded by monitoring GCaMP3 fluorescence intensity at 470 nm in a basal Ca2+ solution (145 mM NaCl, 5 mM KCl, 3 mM MgCl2, 10 mM glucose, 1 mM EGTA, 20 mM HEPES, pH 7.4) using a Zeiss LSM780 confocal microscope. GPN, a lysosomotropic agent, of 200 μM was added to evoke lysosomal Ca2+ release. GCaMP3-ML1 fluorescence was calculated as a change GCaMP3 fluorescence (ΔF) over baseline fluorescence (F0). ## STIM1 and ORAI1 co-localization After transfection with YFP-STIM1 and 3xFLAG-mCherry Red-Orai1/P3XFLAG7.1 (provided by Yuan J through Cha S-G) plasmids for 48 h, HepG2 cells were treated with BSA or 500 μM PA for 6 h. Cells were then fixed with $4\%$ paraformaldehyde (PFA) at room temperature for 10 min. Fluorescence images were acquired with an LSM780 confocal microscope, and the co-localization between STIM1 and ORAI1 was examined by calculating Pearson’s correlation coefficient using ZEN software (Carl Zeiss Microscopy GmbH, Jena, Germany). ## Measurement of mitochondrial ROS Mitochondrial ROS was measured using MitoSOXTM Red. Briefly, HepG2 cells were pretreated with 10 μM MitoTEMPO for 1 h, and then treated with 500 μM PA for 24 h. After incubation with 5 μM MitoSOXTM Red at 37 °C for 15 min, fluorescence was measured by flow cytometry using BD FACSVerse or FACSCanto II. Data were analyzed using FlowJo software. To determine mitochondrial ROS accumulation in vivo, frozen sections of the liver tissue were incubated with MitoSOXTM Red at 37 °C for 15 min, and then washed with PBS. Fluorescence images were observed with an LSM710 confocal microscope and fluorescence intensity was quantified using ImageJ software (NIH, Bethesda, MD, USA). ## mPTP opening mPTP opening was assessed by Co2+ quenching of calcein-AM fluorescence. Cells were treated with PA (500 μM) with or without DUB (200 μM) or olesoxime (100 μM) for 24 h. Cells were then loaded with calcein-AM (1 μM) and CoCl2 (2 mM) at 37 °C for 15 min. Calcein fluorescence was measured by flow cytometry employing BD FACSVerse and FACSCanto II and analyzed using FlowJo software. ## Mitochondrial membrane potential Mitochondrial membrane potential was determined using tetramethylrhodamine ethyl ester perchlorate (TMRE). After treatment of HepG2 cells with PA for 24 h, cells were incubated with 100 nM TMRE at 37 °C for 30 min, and fluorescence was measured using LSM710 confocal microscope. Fluorescence intensity was quantified using ImageJ software. ## Animals Eight-week-old C57BL/6N male mice were purchased from Orient Bio (Seongnam, Korea). All experiments using mice were performed in accordance with the guidelines of the Public Health Service Policy in Humane Care and Use of Laboratory Animals. The protocol was approved by the Institutional Animal Care and Use Committee (IACUC) of the Department of Laboratory Animal Resources of Soonchunhyang Institute of Medi-bio Science. Ethyl palmitate (Tokyo Chemical Industry, P0003) was dissolved in water with $4.8\%$ lecithin (FUJIFILM Wako Pure Chemical Corporation, 120-00832) and $10\%$ glycerol (Sigma-Aldrich, G2025) to make a mixture containing ethyl palmitate at a concentration of 300 mM. LPS (Sigma-Aldrich, E. coli O55:B5) was dissolved in PBS, and 0.025 mg/kg LPS was injected intraperitoneally into mice. Dantrolene and DUB were dissolved in DMSO and diluted with PBS. For in vivo administration, dantrolene (10 mg/kg), DUB (5 mg/kg) or DMSO solution diluted in PBS was injected into mice that were fasted for 24 h. After 1 h, 300 mM ethyl palmitate or vehicle was administered, followed by LPS injection 1 h later. Blood and liver tissue samples were obtained 24 h later. ## Blood chemistry Serum ALT and AST levels were measured using a Fuji Dri-Chem NX500i chemistry Analyzer (Fujifilm, Tokyo, Japan). ## TUNEL staining Paraffin-embedded liver tissue blocks were prepared by fixing in $10\%$ neutral buffered formalin (Sigma, HT501128). TUNEL staining was conducted using In Situ Cell Death Detection kit (Roche, 11684817910) according to the manufacturer’s instructions. Cell death index was expressed as the number of TUNEL-positive cells per field counted from more than 20 fields randomly selected (×200). ## Statistical analysis Data are presented as means ± SD of three independent experiments. 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--- title: 'Association between lifestyle behaviors and health-related quality of life among primary health care physicians in China: A cross-sectional study' authors: - Yisha Lin - Yuankai Huang - Xiaoyu Xi journal: Frontiers in Public Health year: 2023 pmcid: PMC10030863 doi: 10.3389/fpubh.2023.1131031 license: CC BY 4.0 --- # Association between lifestyle behaviors and health-related quality of life among primary health care physicians in China: A cross-sectional study ## Abstract ### Background Primary health care (PHC) serves as the gatekeeper of health system and PHC physicians take on significant obligations to provide health care services in the pursuit of Universal Health Coverage (UHC). PHC physicians' health-related quality of life (HRQoL) can have a strong impact on patients, physicians and the health care system. Lifestyle interventions are found to be effective to improve HRQoL. The purpose of this study was to evaluate the association between lifestyle behaviors and HRQoL among PHC physicians, so that lifestyle intervention can be tailored by policy makers for health promotion. ### Methods A survey covering 31 provinces and administrative regions in China was conducted in 2020 using a stratified sampling strategy. Data on sociodemographic characteristics lifestyle behaviors and HRQoL were collected by a self-administered questionnaire. HRQoL was measured through EuroQol-five dimension-five level (EQ-5D-5L) instrument. A Tobit regression model was performed to evaluate the association between sociodemographic characteristics, lifestyle behaviors and HRQoL. ### Results Among 894 PHC physicians who completed the survey, Anxiety/Depression (AD) was the dimension with the most problems reported ($18.1\%$). Regular daily routine (β = 0.025, $95\%$CI 0.004 to 0.045) and good sleep quality (β = 0.049, $95\%$ CI = 0.029 to 0.069) were protective factors for HRQoL, while smoking (β = −0.027, $95\%$ CI = −0.079 to −0.003) and frequency of eating breakfast (β = −0.041, $95\%$CI = −0.079 to −0.003) were negatively associated with HRQoL. Physical activity and alcohol drinking were not significantly associated with HRQoL. ### Conclusion These findings suggest that tailored interventions on daily routine, improving sleep quality, and tobacco control among PHC physicians may be effective strategies to improve their HRQoL. ## 1. Introduction Primary health care (PHC) is defined as a whole-of-society approach to effectively organizing and strengthening national health systems to bring services for health and well-being closer to communities, from health promotion to disease prevention, treatment, rehabilitation, palliative care and more [1]. PHC serves as the gatekeeper of health system [2] and provides the foundation for the strengthening of the essential public health functions to confront public health crises such as COVID-19. Physicians, the backbone of the primary health care workforce [3], play a vital role in coordinating a person's care needs, from prevention to disease management to curative care [4]. With the goal of Universal Health Coverage (UHC) and strengthening PHC system [5], primary health care physicians are expected to take on a heavier burden to provide health care services, particularly in times of crisis such as the COVID-19 pandemic. A number of studies have demonstrated a high prevalence of physical and mental illness, job burnout [6, 7], sleep disturbance [8] and even suicide [9] among physicians, concerning unsatisfactory health-related quality of life (HRQoL). HRQoL is a multidimensional concept representing both positive and negative aspects of physical and psychological health, social functioning, and overall well-being [10]. Poor HRQoL of physicians could have a negative impact on work performance and patient outcomes in addition to their individual health [11, 12]. As a result, addressing HRQoL of physicians benefits patients, physicians and the health care system. In China, the total number of PHC physicians was 1.246 million and PHC institutions provided $50.2\%$ of outpatient care (4.3 billion visits) and $14.5\%$ of inpatient care (35.9 million hospital admissions) in 2021 [13]. Given the great amount of health care service provided by PHC physicians, it is an urgent public health issue that HRQoL of PHC physicians should be improved in the light of enhancing PHC services and physicians' performance. It is evidently identified that lifestyle behaviors affect people's health, HRQoL and life expectancy (14–17). Moreover, lifestyle interventions were found to be effective to improve HRQoL [18, 19] as well as the implementation of lifestyle interventions in the workplace has been proven to be cost-effective for both employers and society [20, 21]. The occupational category should be considered when designing workplace health promotion programs in the belief that the occupational category produces significant differences in lifestyle behaviors (22–24). In addition, the association between lifestyle behaviors and HRQoL varies between occupations. For instance, smoking was not found to be associated with HRQoL among government employees [25] while current smoker had lower HRQoL in professional drivers [26]. The result of a study suggested that breakfast, exercise, smoking and drinking should be given priority to health promotion at work for doctors [23]. But it is unknown whether these lifestyle behaviors are risky or not for HRQoL of PHC physicians. Physicians are considered as a group with a higher level of health literacy [27], and their better perceived capacities in finding, understanding and applying health information could directly lead to better physical health and subjective well-being [28]. For example, healthcare professionals are more likely to attend medical programs due to their work environment. A research found that smoking and alcohol consumption were not related to quality of life as anticipated among residents participating in the medical checkups [29]. Considering the above, the influence of lifestyle behaviors on HRQoL of physicians may not be the same as that in the general population or other occupations. Moreover, despite knowledge of the significance of healthy lifestyle behaviors, healthcare workers do not adopt healthy lifestyle behaviors for various reasons (30–33), such as a false feeling of “protection” due to their medical knowledge, excessive workload, lack of time or motivation, and the tendency to prioritize their patients' health over their own. Knowledge of the relationship between lifestyle behaviors and HRQoL among physicians would help tailor more effective health-promoting interventions. Prior work has documented the relationship between lifestyle behaviors and HRQoL in different population. However, these studies have primarily focused on patients with different diseases (14, 18, 34–36) and general populations including adolescents, adults and the elderly from different countries or regions [17, 20, 21]. In research to date, few studies have examined such relationships in healthcare professional groups [37], especially little is known about that in primary health care physicians. A study investigated the relationship between lifestyle factors (smoking, BMI, cooking oil, meals out per week, total fruit and vegetable intake per day, physical activity level, and hours of TV, laptop, or internet use per day) and quality of life among 72 PHC physicians in Saudi Arabia [38] with limitation of small sample size. However, other important lifestyle behaviors' influence [e.g., alcohol consumption, sleep quality (16, 39–41)] on HRQoL of PHC physicians has not been properly studied yet. Combined consideration of both occupational characteristics of healthcare professionals [23, 30, 42] [e.g. atypical work schedules [43], heavy workload and stress [44]] and lifestyle behaviors which have been identified as potential factors affecting HRQoL in the Chinese population (39, 45–47), lifestyle behaviors including daily routine, breakfast, sleep quality, smoking, drinking and physical activity should be taken into account. The absence of relative research on the relationship between such lifestyle behaviors and HRQoL among PHC physicians, however, is an impediment to health policy consideration for improving their HRQoL. The aim of this study was to evaluate the association between lifestyle behaviors and HRQoL among Chinese primary health care physicians. Such important information could be informative for local health care policy makers and researchers to consider at which levels effective lifestyle interventions should be implemented to improve the HRQoL of primary health care physicians. ## 2.1. Setting and study design This cross-sectional study covering 31 provinces and administrative regions in China in 2020 was designed to investigate the associations between some lifestyle behaviors and HRQoL. PHC physicians were recruited and data were collected by a self-administered questionnaire. The inclusion criteria of respondents were: 18 years or older, able to fill in the questionnaire independently and employed in primary health care sectors as physicians in China. A stratified sampling strategy was adopted, and the detailed steps were as follows: [1] The sample of the study included 31 provinces and administrative regions in China. The cities or districts of each region were categorized into three groups (high, medium, and low) according to GDP per capita in 2018, forming a total of 93 groups. [ 2] At least two local PHC institutions including community health centers, township health centers, and community health stations were selected respectively for convenience sampling in each group, forming a total of 558 samples of PHC institution. [ 3] In each PHC institution, at least two physicians selected by convenience sampling were willing to participate in and complete the investigation. ## 2.2.1. Sociodemographic information Sociodemographic information was collected including age, sex, height, weight, marital status, number of children, caregiving status, annual household income, educational level, job title, type of residence and chronic diseases. The sociodemographic characteristics mentioned above were included in the study as covariate. Age was divided into three groups: young adults (age 18–44 years), middle-aged adults (age 45–59 years), and older adults (age over 60 years) [48]. Body mass index (BMI) was computed from self-reported weight and height and was divided into three groups: BMI < 18.5, 18.5 ≤ BMI ≤ 24.9, BMI ≥ 25 [49]. Annual household income was categorized into three levels (RMB): < ¥80,000, ¥80,000 ~ ¥1,50,000 and >¥1,50,000. ## 2.2.2. Lifestyle behaviors profile The following health-related lifestyle behaviors were included in the study as independent variables: daily routine, sleep quality, breakfast, smoking, drinking and physical activity. The item “daily routine” was referred to as the established patterns of waking, eating, sleeping, and organizing one's time daily [50], which was evaluated into two statuses: irregular and regular. Sleep quality was answered by “not good” and “good” subjectively. Frequency of eating breakfast weekly means how many times individuals had breakfast in 1 week in the last 6 months, which was divided into two groups: < 4 times and at least 4 times. Both smoking and drinking status were categorized by yes or no. Smoking was defined as at least one cigarette per day in the last 6 months and drinking was defined as at least once a week in the last 6 months. Physical activity was classified as to whether the respondent practice any sports, exercise or other physical activity for at least 30 min during a usual week. ## 2.2.3. Measurement of HRQoL HRQoL was measured by the Chinese version of the EuroQol-five dimension-five level (EQ-5D-5L), consisting of a descriptive system and EQ- 5D visual analog scale (EQ-VAS) [51]. EQ-5D-5L is one of the major self-reported instruments to evaluate HRQoL due to its simplicity, low respondent burden and high universal acceptance [52]. It includes five dimensions: Mobility (MO), Self-Care (SC), Usual Activities (UA), Pain/Discomfort (PD), and Anxiety/Depression (AD). Each dimension has five levels. The responses for the five dimensions can be combined in a 5-digit number describing the respondent's health state (from 11111 meaning no problems at all to 55555 meaning extreme problems in all five dimensions). Then the responses obtained were converted to the EQ-5D utility index based on Chinese value sets [53] to represent HRQoL. ## 2.2.4. Questionnaire validation The validity, rationality, comprehensibility, and readability of the questionnaire had been verified by experts and the results of a pilot survey in community health centers in Nanjing, Jiangsu province, China. Based on the feedback from the pilot survey, the research team revised the questionnaire and formulate the final version. ## 2.3. Data collection A total of 500 undergraduate students majoring in clinical pharmacy or pharmacy were recruited as investigators. In order to ensure the survey quality, each investigator was strictly trained before the investigation, including understanding the principles and methods of survey design, and standardizing the definition. In the process of collecting data, investigators provided the participants with an electronic device—Interview Master, a survey app in WeChat, and gave them instructions about how to complete the questionnaire on the app. Then the responses were automatically converted into electronic data for analysis software. A total of 5 master students were recruited and trained as auditors. If auditors found obvious errors in the data, the data would be returned to the data collectors and then they would verify it with the respondents. ## 2.4. Data analysis Data were analyzed by STATA 17 for Windows and IBM SPSS Statistics 26 for Windows. Means and standard deviations (SD) were used to describe continuous variable data as well as frequencies and percentages are used to describe categorical variable data. Differences in HRQoL based on categories of sociodemographic characteristics and lifestyle behaviors were explored using Mann–Whitney U (two groups) and Kruskal–Wallis one-way analysis of variance (multiple groups) due to the abnormal distribution of the EQ-5D utility index. The EQ-5D utility index was skewed and it was censored at 1 [54], so a Tobit regression model was chosen to explore the potential effects of sociodemographic characteristics and lifestyle behaviors on HRQoL. The level of statistical significance was set at $p \leq 0.05$ in all analyses. Variance inflation factor (VIF) was used to test the multicollinearity. Robust test was conducted by changing conversion formula to calculate HRQoL. The formula was as follows: 1 subtract the sum of the five dimensions scores divided by 25. The value represented HRQoL by a single index ranging from 0 (for 55555) to 0.8 (for 11111). ## 3.1. Sociodemographic and lifestyle behaviors A total of 1,227 participants answered the questionnaire, of which 894 ($73\%$) provided valid information on all included variables (e.g., the measures of weight and height both were valid, current health status was consistent with chronic disease prevalence). Table 1 summarizes the sociodemographic characteristics and lifestyle behaviors of the participants. A total of 894 participants were included and had an average HRQoL of 0.978 (SD = 0.045). The mean age was 41.19 (SD = 9.15), among which the ratio of male to female respondents was around1: 1.05. Annual household income of respondents was ¥1,62,600 (SD = 1,15,500) on average. Majority of them were married ($89.60\%$) and had at least one child ($87.47\%$). Approximately $90.00\%$ of participants reported not having any chronic diseases diagnosed in the hospital at the time of completing the survey. For their lifestyle behaviors, over half of them had regular daily routine ($67.67\%$) and good sleep quality ($58.17\%$). Almost all of them had breakfast at least 4 times a week ($92.28\%$). Most of them did not smoke ($78.97\%$) or consume alcohol ($55.26\%$). The percentage of respondents who never participated in physical activity was $9.06\%$. Table 1 \| Characteristics \| n (%) \| Mean (SD) \| p-value \| \| --- \| --- \| --- \| --- \| \| All \| 894 (100.00) \| 0.978 ± 0.045 \| \| \| Sex \| \| \| 0.795 \| \| Male \| 458 (51.23) \| 0.976 ± 0.047 \| \| \| Female \| 436 (48.77) \| 0.979 ± 0.043 \| \| \| Age \| \| \| 0.023* \| \| < 45 \| 558 (62.42) \| 0.982 ± 0.036 \| \| \| 45 ~ 59 \| 312 (34.9) \| 0.971 ± 0.056 \| \| \| ≥60 \| 24 (2.68) \| 0.953 ± 0.061 \| \| \| BMI \| \| \| 0.014* \| \| < 18.5 \| 53 (5.93) \| 0.988 ± 0.028 \| \| \| 18.5 ~ 24.9 \| 686 (76.73) \| 0.979 ± 0.044 \| \| \| ≥25 \| 155 (17.34) \| 0.966 ± 0.052 \| \| \| Education \| \| \| 0.992 \| \| Below undergraduate \| 330 (36.91) \| 0.976 ± 0.049 \| \| \| Undergraduate \| 458 (51.23) \| 0.978 ± 0.043 \| \| \| Above undergraduate \| 106 (11.86) \| 0.978 ± 0.045 \| \| \| Type of residence \| \| \| 0.076 \| \| Urban \| 579 (64.77) \| 0.979 ± 0.045 \| \| \| Rural \| 315 (35.23) \| 0.975 ± 0.045 \| \| \| Marital status \| \| \| 0.085 \| \| Single \| 84 (9.4) \| 0.986 ± 0.035 \| \| \| Married \| 801 (89.6) \| 0.977 ± 0.046 \| \| \| Others (e.g., Divorced) \| 9 (1.01) \| 0.952 ± 0.060 \| \| \| Annual household income \| \| \| 0.016* \| \| <¥80,000 \| 151 (16.89) \| 0.989 ± 0.030 \| \| \| ¥80,000 ~¥1,50,000 \| 313 (35.01) \| 0.976 ± 0.044 \| \| \| >¥1,50,000 \| 430 (48.1) \| 0.975 ± 0.050 \| \| \| Number of children \| \| \| 0.301 \| \| 0 \| 112 (12.53) \| 0.984 ± 0.036 \| \| \| 1 ~ 2 \| 516 (57.72) \| 0.976 ± 0.049 \| \| \| >2 \| 266 (29.75) \| 0.978 ± 0.041 \| \| \| Caregiving status \| \| \| 0.300 \| \| Not caring for the elderly \| 642 (71.81) \| 0.978 ± 0.046 \| \| \| Care for the elderly \| 252 (28.19) \| 0.976 ± 0.042 \| \| \| Title \| \| \| 0.288 \| \| Below middle \| 393 (43.96) \| 0.981 ± 0.039 \| \| \| Middle \| 383 (42.84) \| 0.976 ± 0.050 \| \| \| Above middle \| 118 (13.2) \| 0.972 ± 0.049 \| \| \| Enrolment \| \| \| 0.153 \| \| Contract \| 331 (37.02) \| 0.977 ± 0.041 \| \| \| Permanent \| 563 (62.98) \| 0.978 ± 0.048 \| \| \| Commercial insurance \| \| \| 0.921 \| \| No \| 561 (62.75) \| 0.978 ± 0.045 \| \| \| Yes \| 333 (37.25) \| 0.977 ± 0.045 \| \| \| Chronic diseases \| \| \| < 0.001 \| \| No \| 804 (89.93) \| 0.983 ± 0.036 \| \| \| Yes \| 90 (10.07) \| 0.925 ± 0.078 \| \| \| Daily routine \| \| \| < 0.001 \| \| Irregular \| 289 (32.33) \| 0.971 ± 0.046 \| \| \| Regular \| 605 (67.67) \| 0.981 ± 0.045 \| \| \| Sleep quality \| \| \| < 0.001** \| \| Not good \| 374 (41.83) \| 0.968 ± 0.003 \| \| \| Good \| 520 (58.17) \| 0.985 ± 0.002 \| \| \| Breakfast \| \| \| 0.290 \| \| < 4 times/week \| 69 (7.72) \| 0.982 ± 0.041 \| \| \| ≥4 times/week \| 825 (92.28) \| 0.977 ± 0.046 \| \| \| Smoking \| \| \| 0.054 \| \| No \| 706 (78.97) \| 0.979 ± 0.042 \| \| \| Yes \| 188 (21.03) \| 0.971 ± 0.054 \| \| \| Drinking \| \| \| 0.095 \| \| No \| 494 (55.26) \| 0.981 ± 0.041 \| \| \| Yes \| 400 (44.74) \| 0.974 ± 0.050 \| \| \| Physical activity \| \| \| 0.013* \| \| No \| 81 (9.06) \| 0.964 ± 0.057 \| \| \| Yes \| 813 (90.94) \| 0.979 ± 0.044 \| \| ## 3.2. EQ-5D-5L dimensional profile and HRQoL Five levels on the EQ-5D dimension included no problem, slight, moderate, severe, and extreme problems. Frequency of item response in each EQ-5D-5L dimension as follows: MO ($97.8\%$, $2.0\%$, $0.2\%$, $0.0\%$, $0.0\%$), SC ($99.6\%$, $0.4\%$, $0.0\%$, $0.0\%$, $0.0\%$), UA ($98.2\%$, $1.8\%$, $0.0\%$, $0.0\%$, $0.0\%$), PD ($83.2\%$, $16.1\%$, $0.7\%$, $0.0\%$, $0.0\%$) and AD ($81.9\%$, $17.2\%$, $0.9\%$, $0.0\%$, $0.0\%$). In this survey, the largest number of participants reported problems in the AD dimension ($18.1\%$), followed by the PD dimension ($16.8\%$). Roughly $99\%$ of participants had no problem in the other three dimensions. The least problematic dimension was the SC dimension accounting for only $0.4\%$. The results of HRQoL for each factor were also shown in Table 1. Significant differences were found in the subgroups of age, BMI, and annual household income. Younger group (age < 45) had better HRQoL (0.982 ± 0.036) than the older group (0.971 ± 0.056, 0.953 ± 0.061 respectively). Highest BMI group (BMI ≥ 25) had the lowest HRQoL (0.966 ± 0.052), whereas lowest BMI group (BMI < 18.5) had the highest HRQoL (0.988 ± 0.028). HRQoL was progressively lower with an increase in income categories ($$p \leq 0.016$$), and lowest in those with an annual income over ¥1,50,000 (0.975 ± 0.050). Participants suffering from chronic diseases had lower HRQoL (0.925 ± 0.078) than those without chronic disease (0.978 ± 0.045). Regarding lifestyle behaviors, significant differences were found in some factors: daily routine ($p \leq 0.001$), sleep quality ($p \leq 0.001$) and physical activity ($$p \leq 0.013$$). In the case of daily routine, the average HRQoL of participants who answered “not regular” was 0.971 ± 0.046, while that of participants who answered “regular” was 0.981 ± 0.045. Participants who had good sleep quality and participated in physical activity had better HRQoL. Differences in HRQoL based on other lifestyle behaviors (e.g., breakfast, smoking and drinking) did not reach a statistically significant level. ## 3.3. Regression analysis The Tobit regression analysis revealed that several demographic characteristics and lifestyle behaviors were significantly associated with HRQoL in the adjusted model (Pseudo R2 = 0.6038) (Table 2). Female (β = −0.029, $$p \leq 0.014$$, $95\%$ CI = −0.052 to −0.006) and higher annual household income (β = −0.052, $$p \leq 0.001$$, $95\%$ CI −0.083 to −0.021 and β = −0.050, $$p \leq 0.002$$, $95\%$ CI = −0.082 to −0.019) were a risk factor for HRQoL among participants. Suffering from chronic diseases (β = −0.118, $p \leq 0.001$, $95\%$ CI = −0.146 to −0.090) and rural residence (β = −0.028, $$p \leq 0.011$$, $95\%$ CI = −0.049 to −0.006) were negatively associated with HRQoL of respondents. In addition, a negative correlation was identified between HRQoL and the frequency of eating breakfast (β = −0.041, $$p \leq 0.036$$, $95\%$ CI = −0.079 to −0.003). A positive association was observed between HRQoL and regular daily routine (β = 0.025, $$p \leq 0.018$$, $95\%$ CI = 0.004 to 0.045) as well as good sleep quality (β = 0.049, $p \leq 0.001$, $95\%$ CI = 0.029 to 0.069). Smoking status was significantly associated with the HRQoL. Compared with non-smokers, smokers report significantly lower HRQoL (β = −0.027, $$p \leq 0.043$$, $95\%$ CI = −0.079 to −0.003). Associations between other lifestyle behaviors (drinking and physical activity) and HRQoL did not reach a statistically significant level. Variance inflation factor (VIF) for each independent variable was well below the recommended threshold of 10 [55], suggesting that the models did not have a multicollinearity issue. Changing conversion formula to calculate HRQoL did not materially alter the conclusions of the original tobit model (Appendix Table 1). Associations between sleep quality (β = 0.059, $p \leq 0.001$, $95\%$ CI = 0.037 to 0.081), breakfast (β = −0.046, $$p \leq 0.029$$, $95\%$ CI = −0.087 to −0.005) and physical activity (β = 0.035, $$p \leq 0.039$$, $95\%$ CI = 0.002 to 0.068) with HRQoL were significantly observed in the unadjusted model (Pseudo R2 = 0.1879). Table 2 \| Variables \| HRQoL \| HRQoL.1 \| HRQoL.2 \| HRQoL.3 \| HRQoL.4 \| HRQoL.5 \| HRQoL.6 \| HRQoL.7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Adjusted \| Adjusted \| Adjusted \| Adjusted \| Unadjusted \| Unadjusted \| Unadjusted \| Unadjusted \| \| \| β \| p \| 95% CI \| 95% CI \| β \| p \| 95% CI \| 95% CI \| \| Intercept \| 1.143 \| < 0.001 \| 1.067 \| 1.219 \| 1.047 \| < 0.001 \| 0.998 \| 1.096 \| \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| Daily routine (ref. = Irregular) \| \| Regular \| 0.025 \| 0.018* \| 0.004 \| 0.045 \| 0.019 \| 0.101 \| −0.004 \| 0.041 \| \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| Sleep quality (ref. = Not good) \| \| Good \| 0.049 \| < 0.001 \| 0.029 \| 0.069 \| 0.059 \| < 0.001 \| 0.037 \| 0.081 \| \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| Breakfast (ref. ≤4 times/week) \| \| ≥4 times/week \| −0.041 \| 0.036* \| −0.079 \| −0.003 \| −0.046 \| 0.029* \| −0.087 \| −0.005 \| \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| Smoking (ref. = No) \| \| Yes \| −0.027 \| 0.043* \| −0.053 \| −0.001 \| −0.016 \| 0.238 \| −0.042 \| 0.010 \| \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| Drinking (ref. = No) \| \| Yes \| −0.009 \| 0.378 \| −0.031 \| 0.012 \| −0.011 \| 0.329 \| −0.033 \| 0.011 \| \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| Physical activity (ref. = No) \| \| Yes \| 0.025 \| 0.094 \| −0.004 \| 0.055 \| 0.035 \| 0.039* \| 0.002 \| 0.068 \| \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| Sex (ref. = Male) \| \| Female \| −0.029 \| 0.014* \| −0.052 \| −0.006 \| \| \| \| \| \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| Age (ref. ≤45) \| \| 45 ~ 59 \| −0.004 \| 0.726 \| −0.026 \| 0.018 \| \| \| \| \| \| ≥60 \| −0.009 \| 0.759 \| −0.065 \| 0.047 \| \| \| \| \| \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| Marital status (ref. = Single) \| \| Married \| −0.021 \| 0.504 \| −0.082 \| 0.040 \| \| \| \| \| \| Others (e.g., Divorced) \| −0.030 \| 0.566 \| −0.133 \| 0.073 \| \| \| \| \| \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| Number of children (ref. = 0) \| \| 1 ~ 2 \| 0.012 \| 0.671 \| −0.043 \| 0.066 \| \| \| \| \| \| >2 \| 0.028 \| 0.331 \| −0.028 \| 0.084 \| \| \| \| \| \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| Annual household income (ref. ≤ ¥80,000) \| \| ¥80,000 ~¥1,50,000 \| −0.052 \| 0.001 \| −0.083 \| −0.021 \| \| \| \| \| \| >¥1,50,000 \| −0.050 \| 0.002 \| −0.082 \| −0.019 \| \| \| \| \| \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| Education (ref. = Below undergraduate) \| \| Undergraduate \| 0.002 \| 0.845 \| −0.020 \| 0.025 \| \| \| \| \| \| Above undergraduate \| 0.004 \| 0.820 \| −0.031 \| 0.039 \| \| \| \| \| \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| Title (ref. = Below middle) \| \| Middle \| −0.004 \| 0.722 \| −0.027 \| 0.018 \| \| \| \| \| \| Above middle \| −0.021 \| 0.232 \| −0.054 \| 0.013 \| \| \| \| \| \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| Enrolment (ref. = Contract) \| \| Permanent \| 0.019 \| 0.057 \| −0.001 \| 0.040 \| \| \| \| \| \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| Chronic diseases (ref. = No) \| \| Yes \| −0.118 \| < 0.001** \| −0.146 \| −0.090 \| \| \| \| \| \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| BMI (ref. ≤18.5) \| \| 18.5 ~ 25 \| −0.016 \| 0.464 \| −0.058 \| 0.027 \| \| \| \| \| \| ≥25 \| −0.042 \| 0.081 \| −0.090 \| 0.005 \| \| \| \| \| \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| Caregiving status (ref. = No need) \| \| Care for the elderly \| 0.006 \| 0.565 \| −0.015 \| 0.027 \| \| \| \| \| \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| Type of residence (ref. = Urban) \| \| Rural \| −0.028 \| 0.011* \| −0.049 \| −0.006 \| \| \| \| \| \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| Commercial insurance (ref. = No) \| \| Yes \| −0.004 \| 0.687 \| −0.024 \| 0.016 \| \| \| \| \| ## 4. Discussion The purpose of this study was to explore the relationship between lifestyle behaviors and HRQoL among Chinese primary health care physicians. Our results confirmed the significant association between daily routine, sleep quality, breakfast, smoking and HRQoL, while drinking and physical activity were not found to influence HRQoL of Chinese primary health care physicians. We found that regular daily routine and good sleep quality were positively associated with HRQoL among Chinese primary health care physicians. Given the 24-h nature of medical care, shift work is common in the healthcare sectors, including evening, night, and early morning shifts, as well as fixed or rotating schedules [56]. As a main cause to irregular daily routine, shift work appears to be a risk factor for overweight, obesity, type 2 diabetes, elevated blood pressure, sleep deprivation and the metabolic syndrome, all of which have a poor impact on HRQoL [57, 58]. Workers during irregular daily routine will experience the negative influence of sleeping, waking, and eating at an inappropriate circadian phase [59]. There is evidence that the circadian rhythms of individuals during an irregular daily routine may deteriorate mood and performance in the healthcare sectors [60, 61]. This situation highlights the necessity for examination and intervention programs on negative health outcomes connected with shift work. According to a meta-analysis [8], the pooled prevalence of sleep disturbances among Chinese healthcare professionals was much higher than the general population in China, owing to the changes of lifestyle, increased work pressure, and deteriorating doctor-patient relationships. In addition, physicians are reported more caffeine consumption than the general population [62], which have several negative effects on sleep quality and quantity [63]. Poor sleep quality, however, may have a negative impact on immune system and be related to depression and anxiety, which can affect both physical and mental health in HRQoL [64, 65] and result in high levels of burnout causing more medical errors [62, 66, 67]. It is suggested that health care policy makers should implement measure to improve sleep quality of primary health care physicians, such as promoting physical activity, strategic naps, work hour reductions and environmental modifications in the workplace [56, 62]. Maybe health care organizations can follow an example of some companies which provide rooms with nap pods or beds for the purpose of napping [68]. Our finding that smoking was a risky factor for HRQoL was in accord with earlier researches [45, 46]. Physicians may be particularly at risk of smoking, due to heavy workload, work conditions, or nightshifts disrupting the circadian rhythm [69]. The overall smoking prevalence among Chinese physicians ranged from 14 to $64\%$ across studies and smoking rates of PHC physicians was $42\%$ [70]. On the one hand, smokers are more likely than non-smokers to develop cardio vascular disease, stroke, lung cancer and have higher risks of heart failure [71, 72]. On the other hand, physicians who smoke have less knowledge and less favorable attitude toward tobacco control compared to non-smokers [73, 74]. Consequently, they would provide less smoking cessation counseling for their patients [75]. Furthermore, smoking may cause diminished overall health and increased absenteeism from work [76], which not only have negative impact on the delivery of the healthcare services to their patients but also cause substantial economic burden of sickness absenteeism [77]. As a result, it is recommended to develop tailored smoking cessation training programs for primary health care physicians. Interestingly, we discovered that the frequency of breakfast was inversely connected to HRQoL among primary health care physicians in the current study, whereas a study conducted in Taiwan [78] showed that breakfast skippers had significantly worse HRQoL than breakfast eaters. One possible explanation for this might be that participants are more inclined to skip breakfast for longer sleep durations, since sleep quality (β = 0.049) appears to have greater impact than breakfast (β = −0.042) on HRQoL. Although there are some evidence showed that skipping breakfast is negatively associated with obesity [78], diabetes mellitus [79] and dyslipidemia, the importance of breakfast remains controversial [80, 81]. Breakfast skippers seemed to have lower risk for of chronic disease and skipping breakfast as a way for calorie restriction may have potential metabolic benefits including neuroprotective, anti-aging, and anti-inflammatory [81]. In addition, there is a study indicating that breakfast skippers showed better HRQOL and lower levels of stress and depression than breakfast eaters who ate a poor or very poor quality of breakfast [40]. According to the study, a good quality breakfast comprises of bread/toast/cereal and/or dairy products rather than commercially prepared goods. Moreover, compared with a nutrition-inadequate breakfast and no breakfast, a nutrition-adequate breakfast will significantly improve short-term cognitive function [82], emphasizing the importance of breakfast quality. Physicians may have limited access to healthy breakfast due to work commitments and lack of time in the workplace [83]. Hence, the association between breakfast quality and HRQoL should be further studied in future. Furthermore, an increasing number of studies have been conducted to examine the relationship between physical activity and HRQoL [47, 84, 85]. It was evidently demonstrated that physical activity improved HRQoL and well-being when compared with minimal or no-treatment controls for adults aged 18–65 years [86]. The typical primary health care physician works long hours and leads a sedentary lifestyle, thus they are encouraged to shift from being sedentary to doing some physical activity which has the greatest potential health gains [87]. However, contrary to expectations, physical activity was not significantly associated with HRQoL based on our regression result and this finding was in line with a research among PHC physician in Saudi Arabia [38]. Nonetheless, Mann–Whitney U analysis showed that participants who had a habit of physical activity ($90.94\%$) got significantly better HRQoL. The insignificant regression results may be due to the fact that most of the participants had a habit of physical activity, which requires further research on a larger scale. Also, a recent study discovered that shift work seemed to hinder the beneficial effects of physical activity [88]. Similarly to physical activity, drinking was not found significantly associated with HRQoL in line with some researches [39, 89] while some studies have demonstrated that drinking is a risk factor for HRQoL [46, 47, 90]. By contrast, some studies have reported moderate drinkers tending to have better HRQoL than non-drinkers and heavy drinkers [16, 91, 92]. Besides, drinking was found to effectively alleviate stress and be linked to improved mental health in healthcare professionals [93, 94]. Such the existence of a causal relationship between alcohol consumption patterns and HRQoL may be resulted from differences in consumption by sex, nationality, and individual characteristics [92]. Moreover, a review indicated that alcohol consumption was an independent predictor of chronic conditions [95], which was a significantly negative factor of HRQoL in our study. Our result also could be partly explained by the inclusion of participants who refrain from alcohol consumption permanently or temporarily due to the presence of chronic disease, which consequently would decrease their HRQoL. Therefore, further studies including more specific drinking groups should be conducted in the future to determine the precise correlations between alcohol consumption and HRQoL in primary health care physicians. Approximately $18.1\%$ of the study participants reported problems in the Anxiety/Depression dimension which was higher than the other four dimensions. Many studies indicated that physicians can be affected by the full spectrum of mental disorders and the most common mental disorders reported among physicians are depression and anxiety (96–98). Workplace risk factors, such as high job demands, a work-family life imbalance and long working hours, are often important in explaining much of the variation in mental ill health among physicians [99]. Working as a primary health care physician involves a heavy workload, and consequently they have little leisure time to spend with their family and friends. Furthermore, healthcare workers are always at the frontline of public health responses to major critical incidents and emergencies like COVID-19 [100, 101], causing increases in mental ill health among them. In this regard, it is high time that public health care policy makers should establish ways to mitigate mental health risks and tailor interventions, such as modifications to work processes, shortening of shifts [98] and mental health training, which effectively make an enhancement in some mental health outcomes among primary health care physicians. Another potential solution is eHealth interventions including Cognitive Behavioral Therapy, Stress Management, Mindfulness approaches and Cognitive training through Apps, which appears to be an effective and more feasible way than face-to-face sessions to deliver these types of interventions among PHC physicians [102, 103]. Several study limitations are worth mentioning. First, our results should be generalized with caution to other countries with different cultural backgrounds because this was a cross-sectional study in China. Second, physicians also might have reported underestimation of unfavorable lifestyle behaviors or overestimation of HRQoL in this study because of social acceptability bias. Third, dietary behaviors were not included. It should be fully studied in future. Lastly, we used a simple questionnaire of lifestyle behaviors for greater response by participants, which might result in missing important information. We suggest that future studies use standardized questionnaires to comprehensively measure daily routine, sleep quality, breakfast, smoking, drinking and physical activity. For example, physical activity can be assessed by International Physical Activity Questionnaire (IPAQ) [104]. ## 5. Conclusions This study evaluated association of lifestyle behaviors including daily routine, sleep quality, frequency of eating breakfast, physical activity, drinking, and smoking with HRQoL in a group of 894 Chinese primary health care physicians. Our study found that lifestyle behaviors including daily routine, sleep quality, frequency of eating breakfast, and smoking affected the HRQoL of Chinese primary health care physicians. However, this association was not observed for other lifestyle behaviors including drinking and physical activity. These findings may guide health policy makers to tailor interventions, such as adequate sleep, strategic naps, and smoking cessation training programs, to improve HRQoL of primary health care physicians effectively. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The research design was reviewed and approved by the Ethics Committee of China Pharmaceutical University, Nanjing city, Jiangsu Province, China (Project Number: CPU2014006). All methods were carried out in accordance with the relevant guidelines and regulations. Based on the principle of informed consent, all data were collected anonymously after obtaining the permission and informed consent signed by respondents. ## Author contributions YH and XX contributed to the conception and design of the study. YL and YH contributed to the data analysis. YL, YH, and XX contributed to the interpretation of data. YL contributed to writing manuscript. All authors had read and approved the final version of the manuscript for submission. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1131031/full#supplementary-material ## References 1. 1.World Health Organization. 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--- title: Automation of flow analysis in scleral vessels based on descriptive-associative algorithms authors: - Bekzhan Kerimkhan - Alexander Nedzved - Ainur Zhumadillayeva - Kanagat Dyussekeyev - Gulzhan Uskenbayeva - Bakhyt Sultanova - Leila Rzayeva journal: Scientific Reports year: 2023 pmcid: PMC10030867 doi: 10.1038/s41598-023-31866-4 license: CC BY 4.0 --- # Automation of flow analysis in scleral vessels based on descriptive-associative algorithms ## Abstract Blood flow reflects the eye's health and is disrupted in many diseases. Many pathological processes take place at the cellular level like as microcirculation of blood in vessels, and the processing of medical images is a difficult recognition task. Existing techniques for measuring blood flow are limited due to the complex assumptions, equipment and calculations requirements. In this paper, we propose a method for determining the blood flow characteristics in eye conjunctiva vessels, such as linear and volumetric blood speed and topological characteristics of the vascular net. The method preprocesses the video to improve the conditions of analysis and then builds an integral optical flow for definition of flow dynamical characteristic of eye vessels. These characteristics make it possible to determine changes in blood flow in eye vessels. We show the efficiency of our method in natural eye vessel scenes. The research provides valuable insights to novices with limited experience in the diagnosis and can serve as a valuable tool for experienced medical professionals. ## Introduction The change in the blood flow rate in vessels reflects the health state and level of disease. When microcirculation is disturbed in the retina due to diseases or conjunctiva of the eye, microhemodynamic disturbances in the brain, myocardium, and other organs happen. That connected with changes of vascular net and characteristics of blood. The change in the blood flow rate in the blood vessels of the eye reflects the change in physiological state. Thus, one can indirectly judge the state of blood flow in other organs and tissues and reveal its disturbances at the early stages of the vascular pathology development by checking the size of the vessels and the parameters of hemodynamics in the eye's tissues. In this regard, the search for non-invasive methods for studying the state of blood vessels and hemodynamics can improve the early diagnosis of cardiovascular diseases. The detection of non-invasive methods for determining of the dynamical state of blood vessels is a very important task. Using a non-invasive method for determining the state of blood vessels and blood flow is important in developing modern, effective diagnostic methods. Non-invasive observation allows to research of optical characteristics of the retina and other fundus structures' vascular system. Changes in blood vessels and blood flow allow estimating an integral characteristic of the body's state. Changes in blood vessels are visible by fundus lens. They are connected with integral characteristic of the state of the body as a whole and the state of the visual system, in particular. Changes in the ratio of the diameter of arteries, veins, and blood vessels, increased curvature of blood vessels, and other changes are early signs of retinal vascular damage caused by diseases. Changes in blood flow correlate with the changes in blood flow in the microvasculature of the brain, heart, and kidneys. Therefore, one can obtain important information about microcirculatory blood flow and its disorders in atherosclerosis, arterial hypertension, diabetes mellitus, and other diseases by measuring the linear blood flow velocity. Blood flow monitoring is very famous problem. There are many methods of such flow researching, for example Doppler ultrasonography and velocimetry, laser Doppler flowmetry, and dedicated handheld devices. Blood flow parameters are determined only for straight sections of the microvascular net by such methods. Changes in the nodes, branches, and complex fragments of the vascular network have not been studied in academic research, to the best of our knowledge. It is connected with complexity of the interpretation of the results of motion measurement. Almost all modern methods of this direction include interactive methods and carry out the analysis of point structures. The blood flow rate is one of the most critical parameters which characterize the body's functionality of the circulatory system. Velocity characteristics of blood flow directly connected with many significant characteristics such as blood pressure, human age, frequency of contractions of the heart muscle, quality characteristics of blood and etc. The blood flow rate plays a considerable role in diagnosing heart and vascular diseases and monitoring the state of the whole body. Today there are many portable devices (fitness bracelets, holders) for blood flow monitoring. However, the interpretation of the results is not always adequate. This is because most analyses do not consider the features of the changes in processes that occur in the nodes and complex fragments of the vasculature. Methods of analyzing and processing images allow for tracing such changes. In this case it is very important to take into account the description of dynamical processes in the nodes and complex fragments of the vasculature. The descriptive methods allow defining computer vision algorithm that is based dynamical process analysis in all vessels network. There are many algorithms for processing and analysis of vascular medical image. Several reviews are described them. In 2003, in report for the NHS (Health Technology Assessment), Sharp published an overview about digital analysis of vessel. The basic task of such research was a digital analysis of different technologies for processing and analyzing diagnostic images. It failed because digital technologies were only at an early stage of their development in this field1. The first reviews of algorithms of vascular image analysis for automation of medical diagnostics can be described in2,3. Then a comparative overview of algorithms was published by Kirbas and Quek4 where isolating vessels and elongated objects was detected on two-dimensional and three-dimensional space for different medical problems. The vessels segmentation algorithms have many ways of realization from time of fist fundus camera but a quality review of segmentation and registration of the retina vessels was presented by Mabrouk et al.5. In this paper only tasks of boundaries and detection central lines (skeleton) of the vessels was described. There are complex solutions for vessels analysis, for example in paper6,7 the automatic diagnosis of diabetic retinopathy was analyzed for retinal images by algorithms of computer vision. But the review7 is unique because it collected papers with different algorithms for automation analysis of vessels on medical images for diagnostics in diabetic retinopathy published between 1998 and 2008. Most interest have algorithms of vessels segmentation on color images received for fundus cameras. For segmentation, it includes algorithm that based on methods of thresholding and region growing. The next review8 have description of algorithms only for isolation of vessels that try to use topological description for algorithms. Recently, machine learning methods9,10 and neural networks11,12 have been actively used to study retina vessels. Almost all papers describe vessel segmentation problems on a static image. Analysis of this topic allows us to determine trends and problems in digital vascular processing. All articles described above spend researching of static retina images in two-dimensional space. It is very complex to determine the optimal algorithm for each stage of vessel image processing. In many cases, some methods have some defects and troubles. Some articles develop and improve approaches based on the existing ones. Abramoff et al.13 's application of an automated system is not recommended for clinical use to detect vascular pathology based on existing algorithms. On the other side, modern equipment makes it possible to study dynamic changes in blood vessels, and this direction is beginning to develop actively14. This work aims to automate the research of eye diseases on base analysis of the blood flow velocity in the vessels for diagnosing diseases of the respiratory and cardiovascular systems. ## Acquisition of sclera vessels video sequence Vascular examination is usually carried out using a fundus lens. Fundus lenses are designed for wide-field stereoscopic examination of the fundus by biomicro-ophthalmoscopy, as well as for laser interventions on the inner membranes and structures of the eye. Due to the large field of view of fundus lenses, they allow obtaining a panoramic image of a significant part of the fundus without requiring additional manipulations with the lens itself. Fundus lenses in combination with a slit lamp binocular microscope make it possible to carry out a consistent view of the fundus by scanning the light slit, as well as to examine the retina in detail. Due to the high resolution, stereoscopicity and image quality, fundus lenses make it possible not only to get an approximate idea of the pathology, but also morphological detailing of the changes found, their exact spatial-depth localization. A clear image makes it possible to diagnose even minor abnormalities in the vitreous humor and in the fundus. In our case for measuring the morphometric parameters of the vessels of the bulbar conjunctiva of the eye, we use ophthalmology complex which consists of a personal computer, a monochrome camera Imper × 1023 Bobcat IGV-B1410M, a laser device for aiming and focusing the camera on the vessels of the bulbar conjunctiva and a device for synchronizing pulsed illumination with the construction of video frames15, having an adjustable frequency and brightness of light. On the basis of this equipment, a sequence of images of the scleral vessels is formed as video where it is possible to monitor changes in the vessels and diagnose pathological processes. We use gray scale video. However, this video sequence has a number of disadvantages that make automatic analysis difficult. During acquisition, it is impossible to fix the eye, as a result, the field of view is always moving and the brightness is randomly changing. for example, a region with vessels can move to other place of headlights and the sharpness of its fragments can change as Fig. 1. There are cases when the image completely jumps to another position or image contrast disappears. As a result, the object on which the observation is carried out disappears. This leads to difficulties during the analysis and requires additional processing of video-sequence. Figure 1Two adjacent images in a video sequence: (a) The first frame; (b) The first frame. In image vessels shifted, a right fragment of image has a less contrast, a bright spot in the center illuminates other objects. ## Problems of processing and analysis video The video turns out to be unstable and requires division into fragments that can be stabilized. Such way procedure of vessels analysis has many troubles. More important troubles look like this:the heterogeneity of contrast in the image requires additional clarification of the region of interest for processing by estimation of the information content in area of objects;loss of sharpness leads to the need to cut out a fragment of a bad image;strong jumps when preparing a video requires splitting a sequence of images into fragments in which the same object is present;movement object lead to additional stabilization of every fragment of video. In result, analysis of dynamical characteristics of blood flow require to definition solution of next problems:extracting quality fragment of video by definition regions of interest and the cropping of regions with maximum information content;stabilization of such video-fragment;vessels masking;calculation of blood flow characteristics. This article solves these troubles and problems by organization of cascade of computer vision methods and algorithms. ## Definition regions of interest The video with a fundus lens is not stable, images constantly change the sharpness and position of the object. Therefore, one of the most important tasks is to determine the region of interest (ROI) where the object has good contrast. Contrast measures the relative decrease in the luminance in an image. It can be defined locally or globally. The local contrast can be estimated depending on the local differences in gray levels of image fragment. It is possible to define a contrast as a function of image edges. There is task of definition of region with high level of information content. The informativeness of images should be understood as the total amount of information obtained by spatial characteristics during their perception or analysis. A more accurate assessment of the information content of an image appears to be complex and dependent on several indicators, including local contrasts and detail. The simplest method from this group is based on the extraction of low and high frequencies of images with their subsequent summation. However, the images of blood vessels are a set of narrow extended objects, therefore, to determine the information content, high frequencies are sufficient. The most striking indicator of information content in the image is high gradient levels. A gradient is a vector quantity that shows the direction of the steepest increase in a certain quantity:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{g} = grad\,g=\left(\frac{\partial I}{\partial x},\frac{\partial I}{\partial y}\right),$$\end{document}g¯=gradg=∂I∂x,∂I∂y,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I$$\end{document}I is image, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y$$\end{document}y are horizontal and vertical coordinates. The quick and convenient way to obtain high-frequency components is to use Sobel filters. The Sobel operator is a discrete differential operator that calculates the approximate value of the image brightness gradient. There are two type of Sobel operator for vertical and horizontal gradient. Maximum from it allow to collect results from every direction. The next trouble is definition of gradient level where image has good contrast. For solution of such problem in common scheme of processing we create addition block for definition of threshold for gradient like as Fig. 2.Figure 2The process of ROI definition for region with good contrast. The procedure od threshold detection consist of procedure of brightness histogram estimation. Maximum from every component of gradient. The informativity are calculated as contrast estimation16:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C = \frac{{I}_{max}-{I}_{min}}{{I}_{max}+{I}_{min}},$$\end{document}C=Imax-IminImax+Imin,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{max}$$\end{document}Imax and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{min}$$\end{document}Imin is maximum and minimum value of brightness on gradient image. This estimation allows to define case with very low informativity if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C<0.3$$\end{document}C<0.3. Then it is necessary detect threshold for vessel binarization. For such operation we use cumulative histogram that demonstrate on Fig. 3.Figure 3Characteristics of gradient image: (a) gradient image as maximum from vertical and horizontal Sobel component; (b) set of histograms of gradient: classic, cumulative with line between border points, rotated cumulative histogram. The most informative fragment of the gradient image is characterized as very bright. Nevertheless, it is very difficult to determine the position of the threshold for brightness on the histogram in the classical representation. In order to facilitate the interpretation of the histogram, It is translated into a cumulative form where it correspond to the integral representation of the frequencies at each brightness level as on Fig. 3b. The optimal position of the threshold for brightness will correspond to the point of maximum bend of this histogram. if a curve of cumulative histogram turns along the line between the border points, threshold of brightness is detected as the point with maximum level. After binarization by this threshold, a lot of small debris remains at the image. they are removed by size. In addition, an additional operation of dilation is used to linking of the separated objects. For coordinates detection, contours are created for all objects in the image by which the maximum and minimum positions are determined. Coordinates of this positions correspond to bounding box of ROI with maximum information content. After processing all video-sequence the set of bounding box coordinates are created. ## The cropping of regions with maximum information content The information-rich region of the image contains a qualitative fragment of the vessels, which can be tracked on the other frames of the video sequence. These fragments of the image can be watched video only during a certain period of time. The presence of a vessel area on the frames of the video sequence can be tracked based on the movement of the ROI and the center of mass of the contrast, which is calculated as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{c} = \frac{\sum_{i}{x}_{i}\cdot {I}_{i}p}{n},$$\end{document}xc=∑ixi·Iipn,3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{c} = \frac{\sum_{i}{y}_{i}\cdot{I}_{i}p}{n}$$\end{document}yc=∑iyi·Iipnwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{c}$$\end{document}xc and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{c}$$\end{document}yc is coordinate of center of mass, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i is sets of border information points, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{i}$$\end{document}Ii is intensity pixel (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}$$\end{document}xi, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}$$\end{document}yi) with high informatively, p is normalization coefficient that aligns maxim intensity of image fragment inside ROI to $100\%$ weight. Control of center of mass position allow to detect time fragment on video that include ROI with vessels. These coordinates are controlled for every frame. If shifting of center of mass more than constant value the frame marked like as last in sequence of image with contrast vessels. This constant value is defined by u (in our case this constant is equal 100). In parallel ROI of every frame is corrected by comparison with common coordinates for all frames of contrast vessels fragments:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(x}_{c1}{y}_{c1}) =(\mathrm{min}(\underset{f,j}{\bigvee }{x}_{f,j}),\mathrm{min}(\underset{f,j}{\bigvee }{y}_{f,j}))),$$\end{document}(xc1yc1)=(min(⋁f,jxf,j),min(⋁f,jyf,j))),\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(x}_{c2}{y}_{c2}) =(\mathrm{max}(\underset{f,j}{\bigvee }{x}_{f,j}),\mathrm{max}(\underset{f,j}{\bigvee }{y}_{f,j}))),$$\end{document}(xc2yc2)=(max(⋁f,jxf,j),max(⋁f,jyf,j))),\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{b} =\mathrm{min}(\mathrm{max}(\underset{f,j}{\bigvee }{x}_{f,j})-\mathrm{min}(\underset{f,j}{\bigvee }{x}_{f,j})),$$\end{document}wb=min(max(⋁f,jxf,j)-min(⋁f,jxf,j)),4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{b} =\mathrm{min}(\mathrm{max}(\underset{f,j}{\bigvee }{y}_{f,j})-\mathrm{min}(\underset{f,j}{\bigvee }{y}_{f,j})),$$\end{document}hb=min(max(⋁f,jyf,j)-min(⋁f,jyf,j)),where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(x}_{c1}{y}_{c1})$$\end{document}(xc1yc1) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(x}_{c2}{y}_{c2})$$\end{document}(xc2yc2) is corner points of bounding box for all frame, (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{b}$$\end{document}wb, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{b}$$\end{document}hb) is width and height of common small box for all frame, j is index in ROI of frame image, f is index of frame. As result after missing of center every image is cropped by bounding box and we have sequence of cropped image with common contrast fragment of vessels, coordinates of centers of contrast ROI for every frame and common box of contrast regions around those centers. ## Creation and stabilization fragments of vessels video After the previous actions, a set of videos has been created. Now, it is necessary to build a fixed coordinate system for each video from set for possibility of motion estimation into vessels. The current video contains a change in the coordinates of the object, which includes the movement of the camera and the object itself. As the camera moves, different objects can get the same coordinates, even if the objects were static. It is necessary:determine the origin of coordinates;compare two consecutive frames with each other;find a transformation that will translate object coordinates on the current frame into coordinates relative to the origin, taking into account all camera movements. The using of frame stabilization leads to a reduction of the images size, because It is necessary to cut a common fragment from every image of video-sequence. Usually, the upper left corner of the first frame is taken as the origin. But the video of the vessels is characterized by the random movement and the position of the first frame does not always correspond to a good position. In previous part of this article we define ROI of contrast fragment and its position. It is possible to use such information and try to stabilize video by fragment into ROI. We use only $50\%$ of this ROI with the same position of center. For origin of coordinates we define such center from ROI of first frame as Fig. 4.Figure 4Definitions of ROI and matching region. The next step of frame comparation allow to take different result for different algorithms of image comparation. We try at least tree types of such algorithms:by methods of machine learning17;by classical correlation18;by comparation of key points positions. All algorithms of machine learning including algorithms on base Kalman filter show bad results. They led to displacement of the vessels from frame to frame. For images of the vasculature, the comparison method based on classical correlation gives the best results, since it relies directly on the images of the vessels, and not on their features. Image correlation is performed for each frame. Correlation of images is comparing all possible pixel pairs and generation of map of likelihood that both pixels will have the close value as a function of the distance and direction of separation. In a more mathematical definition, correlation is the convolution of a function of two image. For a digital image I of size M × N and image J of size K × L correlation can be calculated by5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{IJ}\left(k,l\right)= \frac{{\sum }_{m}{\sum }_{n}\left(I\left(m+k,n+l\right)-\overline{I }\right)\left(J\left(m,n\right)-\overline{J }\right)}{\sqrt{{\sum }_{m}{\sum }_{n}{\left(I\left(m,n\right)-\overline{I }\right)}^{2}{\sum }_{m}{\sum }_{n}{\left(J\left(m,n\right)-\overline{J }\right)}^{2}}},$$\end{document}GIJk,l=∑m∑nIm+k,n+l-I¯Jm,n-J¯∑m∑nIm,n-I¯2∑m∑nJm,n-J¯2,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I(m,n)$$\end{document}I(m,n) is the pixel intensity value at coordinates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(m,n)$$\end{document}(m,n) in the first frame, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{J }(m,n)$$\end{document}J¯(m,n) is the intensity value at a coordinates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(m,n)$$\end{document}(m,n) in the second frame, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{I }$$\end{document}I¯ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{J }$$\end{document}J¯ are mean values of the intensity in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I$$\end{document}I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$J$$\end{document}J images respectively. As result the map of correlation is constructed where maximum correspond of point of best crossing. The coordinate shift is calculated as difference of coordinates of the maximum on the correlation image and center of ROI at first frame. Unfortunately, this way is too long. In practical applications, the correlation array is usually computed using Fourier-transform methods, since the fast Fourier transform19 is a much faster method than directly computing the correlation. But it allows to using additional libraries and increase code of software. Fast and robust image matching is a going by key points comparation. We compare the performance of three different image matching techniques as SIFT20, SURF21, and Shi-Tomasi Corner Detector22 for different image with vessels. For this purpose, the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time are estimated for every algorithm (Table 1).Table 1Sample of comparation fragments from two frames by different algorithms. Base of algorithmTime (s)Key points of frame 1Key points of frame 2MatchesMatch rate (%)Correlation0.89––1100SURF0.131281199677,7SIFT0.462665078,15Shi-Tomasi0.3222222100 The matching rate was calculated as:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Mr = \frac{M200}{\left(Kp1+Kp2\right)},$$\end{document}Mr=M∗200Kp1+Kp2,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Mr$$\end{document}Mr is* Matching rate characteristics, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Kp1$$\end{document}Kp1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Kp2$$\end{document}Kp2 are counts of detected key points for first and second frames, M is count of matched points. As result the algorithm on base Shi-Tomasi Corner Detector is the fastest algorithm while correlation shows the best quality. SIFT and SURF performs with errors because detect points where vessels change direction. In this time algorithm on base Shi-Tomasi Corner Detector use only points in nodes of vessels branch. This property allows to take best results for algorithm on base Shi-Tomasi Corner Detector on vessels images. In this way sets key points that characterized by maximum of matching in sequence of images. Sequential analysis of moving points is not efficient. For control of shifting of key points we use optical flow. The best solution of it is Lucas–Kanade algorithm23. It is a widely used differential method for points shifting. This algorithm based on the essentially constant flow for a local neighborhood of every pixel. It defines the optical flow equations by the least square’s criterion. All the neighborhood points have the same motion. Shifting characteristics (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{x}$$\end{document}fx,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{y}$$\end{document}fy,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{t}$$\end{document}ft)—vertical, horizontal and time24 are calculated for these points. The final solution includes two equation and two unknown parameters [7].7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left[\begin{array}{c}{\varvec{u}}\\ {\varvec{v}}\end{array}\right] = {\left[\begin{array}{cc}{\sum }_{i}{f}_{x\left(i\right)}^{2}& {\sum }_{i}{f}_{x(i)}{f}_{y(i)}\\ {\sum }_{i}{f}_{x(i)}{f}_{y(i)}& {\sum }_{i}{f}_{y\left(i\right)}^{2}\end{array}\right]}^{-1}\left[\begin{array}{c}-{\sum }_{i}{f}_{x(i)}{f}_{t(i)}\\ -{\sum }_{i}{f}_{y(i)}{f}_{t(i)}\end{array}\right],$$\end{document}uv=∑ifxi2∑ifx(i)fy(i)∑ifx(i)fy(i)∑ifyi2-1-∑ifx(i)ft(i)-∑ify(i)ft(i),where u and v of horizontal and vertical component of motion vectors for key points, i is index of pixel in image. The set of such vectors is really good for small motions, but it is not good for a large motion. To deal with this we use pyramids. From bottom to top characteristics of motion are change, small offset vectors are removed and large offset vectors become to small ones. The application of Lucas-Kanade algorithm allow to get optical flow by considering scaling. The algorithm use scale pyramids for solve problem of motion scale. However, there are distance limits for offset points. The vectors u and v have good corresponding of shifting only for defined range of distance. The step of image cropping guaranteed to such conditions. The optical flow allows to construct set of offset key points between two frames in video-sequence. It is possible to use this set for definition offset of frame as median. It will to correspond of most strong shifting. Now we have ROI of contrast region, offset and coordinate for every frame. On base such information video constructed with stabilized position. ## Vessels masking Even though the video is stabilized, it still contains a lot of useless information that interferes with motion analysis. Masking of vessels allow to extract important motion in them. The vessel mask is constructed by segmentation. The solution of the problem of segmentation of the vascular net is performed as a classification problem. The learning the convolutional neuron network (CNN) with a sliding window allow to solve it and define vessel class as region on image. The prediction of the label of a class for each pixel is going by analysis pattern in neighbourhood region as in article25,26. This small region is used as a source data (Fig. 5).Figure 5The common scheme of pipeline of vessel segmentation. The fully connected convolutional neural network on base U-net model was used for vascular image segmentation. Such model is supplemented with additin layers. In them Union operators are changed to operators of discretization. In this way, the resolution of the output layer is increased. It allow to combine features of higher resolution from a narrowing region to fragment with an expanding laers. The training of convolutional neural network with such modification is going to more accurate result at the output. A set of 130 Gy-scale images of eye sclera was collected in Belarussian State Medical University by fundus camera of GigE type Full HD (1920 × 1080) video resolution. each image was divided into fragments with size 352 × 352 pixels. Also we use open source image sets: DRIVE, STARE, CHASE DB1, HRF It was used to train the neural network27. In addition, we use pretraining part of the U-net like-SNA architecture28 that was proposed in isbi 2012 EM Segmentation Challenge (Segment Neural Membranes). For improving result of training of CNN a geometric augmentation was used. It use simple geometric transformation: 1) flips, 2) turns, 3) reflections, 4) elastic deformations, and 5) scaling. As result dataset is increased to 650 synthetic images. The NVIDIA GeForce GTX 1080 Ti was used for training process. The training lasted for 700 epochs and batch equal 8. For classification is used two classes. One class include regions with vessel in the center and second class collect regions without vessel in the center. Every region has the same size and is selected from the input sample images as Fig. 6. A stochastic gradient descent was used as optimization method. The result of image processing by CNN is the probability for each vessel. It is change from 0 to 1, where 0 stands for vessel, 1 stand for non-vessel and look like image segmentation. Figure 6Training patterns: (a) with vessel in the center (class 0); (b) without vessel in the center (class 1). This neural network includes 23 layers of convolution. After it constructed 64-component vector. But we use only two classes. Therefore, convolutions of 1 × 1 size was used on the last layer. The size of the input image is determined by subsampling (2 × 2 max pooling). It allows to guaranty even values of height and width of layer. A concatenation with the corresponding set of features from the narrowing region, and two 3 × 3 convolutions was used in this network. After every layer is going a transformation through the ReLU activation function. This model is going to good results for the segmentation of blood vessels like as Fig. 7.Figure 7The results of segmentation of the vessels branch and result of segmentation by CNN for different type of image: (a) on the endoscopic image; (b) image of the fundus. The standard deviation and accuracy mark were used for estimation of effectiveness of this algorithm5. Standard deviation is calculated as:8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma = \sqrt{\frac{1}{n}{\sum }_{i}\left({x}_{i}-\overline{x }\right)},$$\end{document}σ=1n∑ixi-x¯,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}n is pixel number, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}$$\end{document}xi is result label (0 or 1), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }$$\end{document}x¯—probability result. Accuracy is normalized number of true answers. Estimation of segmentation effectiveness is shown in Table 2.Table 2Estimation of segmentation quality for vascular net. DataAccuracySDVascular net detection0.95170.1897Clear regions0.93210.2135Common mean estimation0.94180.2016 This segmentation algorithm extract regions of vascular net from gray-scale image with high quality and allow to use this result as mask for vessel analysis. ## Calculation of blood flow characteristics Before measurement brightness stabilization are spend after masking of stabilized video. It is allowed to normalize densitometric characteristics of images and remove brightness distortion. For realization of it we use specific technics of masking and Anbarjafari algorithm2. Every frame in video is change to image where background under mask is reduced to a constant value of the corresponding average brightness around the vessel. Brightness values inside the vessel are stored. In result we have permanent background. Then Anbarjafari algorithm aligns brightness in video. It use an iterative n th root and n th power color equalization for single generic images. The intensity value of an image is passed through a non-linear transfer function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(x)=\mathrm{ln}(0.5)/\mathrm{ln}(x)$$\end{document}(x)=ln(0.5)/ln(x), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}x is the image’s mean intensity. The operation is repeated until the final image achieves a mean intensity equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ, set typically to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma =0.5$$\end{document}γ=0.5. The evolution of this method is described in3. As rule boundary effects influence to the analysis of a blood flow. For removing this effect, it is necessary to accurate define region for analysis. Middle line of a vessel corresponds to such region. It can detect by thinning operation. The stabilization video and brightness can be masking such middle line for analysis. Inside this region point of vessels are defined for analysis and are used for calculation optical flow. It allows to estimate dynamical characteristics of blood flow speed that is determinate directly through vessel in vascular net. This operation allows to decrease extern influence for calculation of velocity characteristics and calculate the instantaneous linear speed in the center of the vessel. For analysis velocity characteristics in every point of image the map of dense of optical flow is generated from two frame from video. For this solution the algorithm of Gunnar FarneBack technique24 show best result. In frame of this algorithm vectors in map of optical flow correspond to the pattern of apparent motion of objects as the motion of objects between every two consecutive frames from video. It is defined by the movement of the object being captured. The FarneBack algorithm destined for dense vector field of optical flow. This algorithm check at all of the points on the image. The changes of pixel intensity are registrated between the two frames, unlike Lucas Kanade which works only on corner points detected by Shi-Tomasi algorithm22. For best interpretation horizontal and vertical components of optical flow transform to polar coordinate system as magnitude and phase of vector and represented in HSV color coordinate system where magnitude correspond to brightness and phase is hue, like as Fig. 8.Figure 8Maps of optical flow for vascular network: (a) vector field representation in horizontal/vertical coordinate system, that is traditional for image; (b) vector field representation in polar coordinate system. The magnitudes of optical flow vectors are using for calculation of value of relative velocity of blood flow. But most strong adequacy of it collected on along the midline of the skeleton. Constriction of profile changing of optical flow along this line define distribution relative values of velocity for vessel branch (Fig. 9).Figure 9Vascular net skeleton with branch node points and extraction blood way with velocity estimation in branch. This profile represents a change in blood instantaneous linear speed for any point on the middle line of the vessel (Fig. 10).Figure 10Optical flow in vessel branch and chart of relative velocity in vessel branch. ## Institutional review board statement The study did not require ethical approval. ## Informed consent Informed consent was obtained from all subjects involved in the study. ## Results Thus, as a result of calculating the optical flow, the motion vector for each pixel is determined. Knowing the time during which the position of the pixel has changed, we can calculate the instantaneous linear velocity of blood flow. Normally, in adults, the speed of propagation of a pulse wave in the vessels of the elastic type is 5–8 m/s, in the vessels of the muscular type—6–10 m/s. this is consistent with the normalized values of the optical flow magnitude. The velocity of blood flow in the vessel and its diameter are used to record the instantaneous changes that occur in the vessel. Velocity determination based on optical flow and vessel width can be performed in parallel. The subsequent analysis of the results makes it possible to quantify changes in the linear velocity of blood flow in the vessels of healthy people when modeling the processes of hypercapnia and hyperoxia. Problems associated with the discretization of time and space make it difficult to apply absolute values. The optical flow values were used to determine the instantaneous speed, which was measured in relative units29. The volumetric velocity of blood flow in the capillary depends on its width, it can be calculated by the next formula9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q = v\cdot S,$$\end{document}Q=v·S,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v$$\end{document}v is the linear velocity of blood flow, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q$$\end{document}Q is the volumetric velocity, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S$$\end{document}S is the cross-sectional area of the vessel. Basic characteristics of segment of vascular net are width, length and blood flow speed. The length is detected for whole network or separate segments. But width and speed are detected only in one point. Therefore, for segment of vascular network such characteristics are calculated by averaging results for all selected points. Also, there is possibilities to construct of multiple adjacent blood vessel speed profiles. Other hemodynamic parameters can be calculated by topological transformation and description. Thinning and segmentation of vessel reconstruct image to skeleton of pattern of vascular net that include branch points. These points are important topological elements where description of blood flow is changed. In result for vascular net it is possible to calculate common length, length of every segment of vascular branch, branchiness, compactness, and tortuosity, based on topological characteristics and such dynamical characteristics as velocity. ## Discussion The proposed algorithm for measuring and monitoring the blood flow characteristics in the vessels allows the estimation of linear and volumetric blood speed and topological characteristics of the vascular net. The algorithm analyses the image sequentially and then builds optical flow maps for the video sequence. Dynamic characteristics of vessels are introduced and calculated. These characteristics determine changes in blood flow in natural eye vessel scenes. The method was tested on the video sequence of blood vessels of the conjunctiva. The change in blood flow speed in vessels reflects the change in blood flow in the microcirculatory bed and other organs for normal and pathological conditions. The research and testing were realized by a high-resolution monochrome digital video camera Imperx Bobcat IGV-B1410M with a microscope lens with a focal length of 40 mm. The linear speed of blood flow in a vessel with a diameter of 1.91 µm is 0.50379 relative units, corresponding to 5∙10–5 m/s. This result corresponds to the data obtained by the Doppler method. ## Conclusions The proposed method allows spending research of the dynamic blood characteristics of the vascular net. The definition of instantaneous linear and volumetric speed for each vessel point describes eye diseases' properties at an early stage. The quantitative assessment of the cross-section area and linear and volumetric speed in vessels creates new properties of different pathologies, including the topology of vessel net. As AI can extrapolate the patterns in video data, further study on the blood flow pattern might allow us to identify some diseases in the future. Another future direction of research might throw light on studying specific types of blood vessels like retinal, improving existing study like30 that only study retinal images. The developed methods of analyzing sclera video sequences and calculating the magnitude of the optical flow make it possible to quantify the change in the linear velocity of blood flow in them in healthy people when modelling hypercapnia and hyperoxia. The proposed method is a non-invasive method for the diagnosis of microcirculation disorders. It is used in medical institutions to study microcirculation in the vessels of the bulbar conjunctiva. In addition, according to several studies, the microcirculatory bed in the vessels of the bulbar conjunctiva can be used to judge the state of the microcirculatory bed of other organs and systems, for example, the brain, kidneys, and heart. Thus, having information about the state of the bulbar conjunctiva vessels makes it possible to assess the risk of developing hemodynamic disorders in other vessels. ## References 1. Bühler K, Felkel P, La Cruz A. Geometric methods for vessel visualization and quantification—a survey 399–419 (2004.0) 2. 2.Anbarjafari, G. HSI based colour image equalization using iterative nth root and nth power. arXiv preprint arXiv:1501.00108. (2014). 3. 3.van Vliet, S., Sobiecki, A., & Telea, A. C. Joint brightness and tone stabilization of capsule endoscopy videos. In VISIGRAPP (4: VISAPP) 101–112 (2018). 4. Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Comput. Surv. (CSUR) (2004.0) 36 81-121. DOI: 10.1145/1031120.1031121 5. 5.Mabrouk, M. S., Solouma, N. H., & Kadah, Y. M. Survey of retinal image segmentation and registration. (2006). 6. Faust O, Acharya UR. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: A review. J. Med. 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Girard F, Kavalec C, Cheriet F. Joint segmentation and classification of retinal arteries/veins from fundus images. Artif. Intell. Med. (2019.0) 94 96-109. DOI: 10.1016/j.artmed.2019.02.004 12. 12.Yi, Y., Guo, C., Hu, Y., Zhou, W., & Wang, W. BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation. Front. Public Health, 10, (2022). 13. Fraz MM, Remagnino P. Blood vessel segmentation methodologies in retinal images–a survey. Comput. Methods Programs Biomed. (2012.0) 108 407-433. DOI: 10.1016/j.cmpb.2012.03.009 14. Das S, De Ghosh I, Chattopadhyay A. An efficient deep sclera recognition framework with novel sclera segmentation, vessel extraction and gaze detection. Signal Process. Image Commun. (2021.0) 97 116349. DOI: 10.1016/j.image.2021.116349 15. Firago VA, Kubarko AI, Hotra O, Volkova I, Kozbakova A, Musabekov N. Determination of blood flow velocity in vessels of the bulbar conjunctiva. Przegląd Elektrotechniczny (2016.0) 8 105-108 16. Al-Bayati AN. 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--- title: 'Factors associated with the retention of secukinumab in patients with axial spondyloarthritis in real-world practice: results from a retrospective study (FORSYA)' authors: - Maxime Dougados - Julien Lucas - Emilie Desfleurs - Pascal Claudepierre - Philippe Goupille - Adeline Ruyssen-Witrand - Alain Saraux - Anne Tournadre - Daniel Wendling - Cédric Lukas journal: RMD Open year: 2023 pmcid: PMC10030893 doi: 10.1136/rmdopen-2022-002802 license: CC BY 4.0 --- # Factors associated with the retention of secukinumab in patients with axial spondyloarthritis in real-world practice: results from a retrospective study (FORSYA) ## Abstract ### Background Secukinumab efficacy and retention data are emerging in patients with axial spondyloarthritis (axSpA) in real-world settings. However, limited data are available on the predictive factors that affect the retention rate. The key objective was to determine whether objective signs of inflammation (OSI) were predictive of secukinumab retention at 1 year. ### Methods FORSYA is a French, multicentric, non-interventional, retrospective study in adult axSpA patients who received secukinumab treatment between its launch (11 August 2016) and 31 August 2018. The time to secukinumab discontinuation and retention were analysed using a Kaplan-Meier (KM) analysis. OSI was predefined by at least one of the criteria: C reactive protein ≥5 mg/L or erythrocyte sedimentation rate ≥28 mm/hour at secukinumab initiation or MRI inflammation at the sacroiliac or spine level. ### Results In total, 906 patients from 48 centres were included in the analysis, $42.2\%$ of whom were men, with a mean age of 46.2±11.7 years and a mean disease duration of 9.3±9.1 years. The 1-year KM retention rate ($95\%$ CI) for secukinumab was $59\%$ ($55\%$–$62\%$), whereas for patients with and without OSI, it was $58\%$ ($54\%$–$62\%$) and $63\%$ ($53\%$–$73\%$), respectively. In multivariate analysis, lack of prior exposure to tumour necrosis factor inhibitor (TNFi), absence of OSI and inflammatory bowel disease (IBD) were associated with a better retention of secukinumab at 1 year. ### Conclusion Following its approval in France, ~$59\%$ of axSpA patients retained secukinumab in daily practice, at 1 year. Prior exposure to TNFi, OSI and IBD were identified as risk factors for secukinumab discontinuation. ## Introduction Axial spondyloarthritis (axSpA) is an inflammatory rheumatic disease that affects the axial skeleton (spine and/or sacroiliac joints), leading to severe pain, stiffness and fatigue.1 Various types of lymphoid and non-lymphoid cells (producing proinflammatory cytokines such as tumour necrosis factor (TNF)-α, interleukin (IL)−17, IL-23, etc) have been shown to play a combined role in the pathogenesis of axSpA.2 The proinflammatory cytokine, IL-17A, has been identified as a relevant therapeutic target for some chronic inflammatory disorders, including axSpA.3 4 Secukinumab is the first-in-class human monoclonal IgG1κ antibody that directly inhibits IL-17A and is approved for the treatment of patients with axSpA (radiographic (r-axSpA) and non-radiographic (nr-axSpA)).5 Secukinumab has demonstrated significant long-term efficacy and safety versus placebo in patients with axSpA across various randomised clinical trials (RCTs).6–10 RCTs evaluate sophisticated outcome measures including MRI, health-related quality of life with a stringent prospective, randomised and controlled design.11 12 However, RCTs typically have a smaller and well-defined study population. Data from RCTs may not fully mimic secukinumab treatment in a real-world setting because clinical trials are highly regulated and do not inevitably represent everyday practice. Moreover, close monitoring of patients with a predefined time for different visits at the centres is far from the daily practice. Therefore, conventional RCT outcomes may be inappropriate in the specific scenario of real-world data collection. Despite some limitations associated with real-world evidence (RWE) studies, such as analysis design and incomplete or missing data, these observational studies (prospective or retrospective) complement the evidence generated by RCTs and depend on everyday therapeutic use in the real-world setting.13–15 RWE studies can inform the application of RCTs to healthcare decision-making and provide insights beyond what RCT covers.16 Under real-world conditions, drug retention provides critical information on efficacy, safety, compliance and convenience of use. The retention of secukinumab has been evaluated in retrospective observational series and registries17–22 including axSpA.20 23 24 Although the factors influencing anti-TNF efficacy and retention in axSpA—such as smoking status, young age, gender, human leucocyte antigen (HLA)-B27 positivity, radiographic status, objective signs of inflammation (OSI) and rank of drug administration—have been identified in various RCTs and RWE studies,25–27 data on anti-IL17 agents are sparse.20 22 23 The impact that patient characteristics, diagnosis or drug use (line of treatment or dosage) might have on secukinumab retention under real-life conditions in patients with axSpA is only partially understood. This non-interventional retrospective study was designed to evaluate if the presence of OSI at the initiation of secukinumab is predictive of secukinumab retention at 1 year. The study also evaluated other predictive factors of retention in patients treated for active axSpA in France. ## Study design and patients FORSYA is an ongoing, multicentric, non-interventional, retrospective and descriptive study in adult axSpA and psoriatic arthritis (PsA) patients who were initiated on commercial secukinumab treatment between its launch (11 August 2016) and 31 August 2018. In this manuscript, we report the results obtained in the axSpA subgroup. Patients who received secukinumab for indications other than axSpA or PsA, patients who received secukinumab as an investigational medical product during an interventional trial, patients for whom no follow-up by the centre was available after secukinumab initiation, and patients who objected to the collection and use of data for this study were excluded. The primary objective of this study was to evaluate if the presence of OSI at secukinumab initiation was associated with secukinumab retention at 1 year. In order to avoid a bias due to the non-evaluation of patients who had to stop their treatment very soon after its initiation, only the centres that were able to provide an exhaustive list issued from their electronic health record system or from their own specific databases of patients fulfilling the inclusion/exclusion criteria were involved in the study. The data were retrospectively collected from patients’ files (patient chart review study) between October 2019 and September 2020 by either a physician or research nurse at each centre or by an independent clinical research assistant from the Contract Research Organization. The study was registered with Health Data Hub28 and conducted in accordance with the Guidelines for Good Pharmacoepidemiology Practices of the International Society for Pharmacoepidemiology [2015].29 All patients were individually informed of this study and had the opportunity to refuse the extraction of the data contained in their medical files. ## Assessments The presence of OSI was predefined for axSpA patients by at least one of the criteria: a C reactive protein (CRP) level ≥5 mg/L within the 3 months before initiation of secukinumab or erythrocyte sedimentation rate (ESR) ≥28 mm/hour at secukinumab initiation or MRI inflammation at the sacroiliac or spine level (as defined by the local radiologist or rheumatologist) at any time before secukinumab initiation. Moreover, for the patients with a positive MRI, we have collected the date of the last MRI prior to secukinumab initiation. Presence of structural damage at the sacroiliac joints (SIJ) level on pelvic X-Rays was recorded in two different ways (a) by asking the physician whether she/he was initiating the drug because of an nr-axSpA or a r-axSpA (b) the fulfilment or not of the modified New York (mNY) criteria. Presence of MRI abnormality was only considered for the inflammation domain and was also recorded in two different ways (a) the fulfilment or not of the Assessment of SpondyloArthritis International Society (ASAS) 2009 criteria (b) the presence of inflammation based on the local investigator/radiologist opinion at MRI SIJ or Spine level before initiating the drug. The retention period of treatment was analysed as a function of time. This period was defined as the time interval between the start of secukinumab treatment and the final discontinuation of treatment. The candidate baseline predictive factors for secukinumab retention included sociodemographic data (age, gender, body mass index (BMI) and tobacco status), axSpA anamnesis (disease duration, radiographic structural damage on pelvic X-rays or inflammation on MRI, HLA-B27, history of synovitis, enthesitis, uveitis, inflammatory bowel disease (IBD) and psoriasis) and comorbidities (eg, cardiovascular disease, hypertension, metabolic syndrome, renal insufficiency, severe infections, gastroduodenal ulcers, osteoporosis, depression, fibromyalgia, cancer) and OSI. The treatment regimen of secukinumab, the loading and maintenance doses at initiation, treatment modifications and the reasons for treatment modifications and concomitant treatment at initiation were also collected and assessed. ## Statistical analysis The mean and SD were used to describe quantitative variables and were reported in terms of absolute frequency and percentage by modality for $95\%$ CI of the percentages. Cox proportional hazard regression models (univariate and multivariate) were applied to investigate the predictive factors at secukinumab initiation affecting the retention at 1 year of secukinumab treatment. The dependent variable was the time to secukinumab definitive discontinuation within 1 year, meaning that in case of temporarily discontinuation with a reinitiation of the drug later on the status of the patient at the end of the second period was considered. Any data that were ‘not available’ were considered as ‘missing’; hence, these data were not considered when computing the proportion of patients per modality in the qualitative variable analysis. For predictive factors with less than $20\%$ of missing data, a multiple imputation approach was applied using the method developed by Rubin30 and data were entered in a multivariate model using a stepwise selection (significance level for entering variables=$20\%$; significance level for removing variables=$10\%$). OSI was forced into the model regardless of its significance level or rate of missing data. The time to definitive secukinumab discontinuation and retention were analysed using a Kaplan-Meier (KM) analysis. Survival probability estimates at 6 months and 1 year were calculated with $95\%$ CIs using the log–log transformation. Moreover, as a post hoc analysis, we have evaluated the percentage of patients still on treatment at 1 year with regards to the time of the MRI assessment (within 3, 6, 12 months or more than 12 months prior the baseline visit). The percentage values of discontinuation due to intolerance were calculated against total discontinued patients ($$n = 476$$). The analyses were performed using SAS software V.9.4 or higher. ## Patient and study course Among the 48 active centres that included patients in this study, 47 fulfilled the criteria related to exhaustivity of information about their centre’s patients receiving secukinumab. A total of 2098 patients were identified, $59.7\%$ through electronic health records and $40.3\%$ through each centre’s personal database. Among the patients identified, $34.2\%$ did not meet eligibility criteria and were excluded (primarily because of other diagnosis or the absence of follow-up) and 1381 patients were eligible. This study reports the results observed in the groups of patients where secukinumab was initiated based on a diagnosis of axSpA (685 with r-axSpA and 221 with nr-axSpA) (figure 1). Figure 1: Patient and study course. N, size of the population; PsA, psoriatic arthritis; SpA, spondyloarthritis. ## Baseline demographics and disease characteristics Demographics and disease characteristics of the analysed patients are summarised in table 1. Overall, the mean (SD) age was 46.2 (11.7) years, the mean BMI was 27.0 kg/m2, and approximately $42\%$ of the patients were men. At secukinumab initiation, $67.7\%$ of patients were non-smokers. The mean (SD) disease duration was 9.3 (9.1) years; the shortest and longest durations were observed in the first line (4.6 years) and in the ≥third line (10.2 years) treatment groups, respectively. More than $60\%$ of patients were HLA-B27 positive, $25\%$ had concomitant psoriasis and $78\%$ had evidence of radiologic (MRI or X-rays) signs of axSpA on the sacroiliac joint. Based on the opinion of the investigator, 685 and 221 patients were suffering from a radiographic and a non-radiographic axSpA, respectively. Among the 685 r-axSpA patients, an information related to the mNY criteria was available in 405 and a fulfilment of the mNY criteria for presence of SIJ structural damage was noticed in 323 ($80\%$). The presence of inflammation at MRI was observed in (a) 401 out of 491 ($82\%$) patients with information on the fulfilment of the ASAS criteria for the presence of inflammation at SIJ-MRI b) 488 out of 703 ($69\%$) patients when considering the presence of inflammation at MRI of either the SIJ or the spine before the initiation of secukinumab. At least one OSI was reported in $86.3\%$ of patients ($41.3\%$ had a CRP level ≥5 mg/L (or an ESR ≥28 mm/hour) and $69.4\%$ had an MRI sign of inflammation on the sacroiliac joint or the spine). It has to be mentioned that objective sign of inflammation was absent not only in $14\%$ of the patients with a radiographic status of their disease but also in $24\%$ of the patients with a non-radiographic status of their disease, condition which is in contradiction with the current recommendation of use of biotherapy in nr-axSpA. Secukinumab was the first line of treatment in 72 patients ($8\%$), second line in 134 patients ($14.9\%$) and third or subsequent lines in the majority of patients, that is, in 693 patients ($77.1\%$) (data not available for 7 patients). In the third-line treatment group, the mean (SD) number of biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) previously received was 3.3 (1.4). Other than anti-TNF, 40 patients had been treated with ustekinumab; 14 with tocilizumab; 11 with abatacept and<5 with rituximab, ixekizumab and apremilast. Table 1 \| Parameters \| TotalN=906 \| OSI+N=617 \| OSI-N=98 \| nr-axSpAN=221 \| r-axSpAN=685 \| First lineN=72 \| Second lineN=134 \| ≥Third lineN=693 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age (years), mean (SD); m \| 46.2 (11.7); m=906 \| 45.6 (11.6); m=617 \| 47.6 (12.5); m=98 \| 45.9 (11.2); m=221 \| 46.3 (11.9); m=685 \| 41.7 (12.6); m=72 \| 46.5 (12.2); m=134 \| 46.6 (11.4); m=693 \| \| Male, n/m (%) \| 382/906 (42.2) \| 267/617 (43.3) \| 37/98 (37.8) \| 58/221 (26.2) \| 324/685 (47.3) \| 30/72 (41.7) \| 69/134 (51.5) \| 279/693 (40.3) \| \| BMI (kg/m2), mean (SD); m \| 27.0 (5.5); m=533 \| 27.2 (5.7); m=387 \| 25.9 (5.2); m=52 \| 26.5 (5.2); m=128 \| 27.1 (5.6); m=405 \| 25.5 (4.4); m=45 \| 26.5 (5.2); m=81 \| 27.2 (5.7); m=405 \| \| Current smoker, n/m(%) \| 231/715 (32.3) \| 169/501 (33.7) \| 21/71 (29.6) \| 55/173 (31.8) \| 176/542 (32.5) \| 29/61 (47.5) \| 28/110 (25.5) \| 174/539 (32.3) \| \| Disease duration, years, mean (SD); m \| 9.3 (9.1); m=807 \| 8.4 (8.7); m=556 \| 11.3 (9.7); m=85 \| 6.5 (6.9); m=197 \| 10.3 (9.6); m=610 \| 4.6 (7.9); m=66 \| 7.6 (8.7); m=120 \| 10.2 (9.1); m=615 \| \| HLA-B27 +, n/m (%) \| 527/825 (63.9) \| 363/571 (63.6) \| 49/91 (53.8) \| 98/197 (49.7) \| 429/628 (68.3) \| 38/66 (57.6) \| 77/124 (62.1) \| 406/629 (64.5) \| \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| Sacroiliac joint radiographic status \| \| nr-axSpA, n/m (%) \| 221/906 (24.4) \| 139/617 (22.5) \| 43/98 (43.9) \| 221/221 (100.0) \| \| 15/72 (20.8) \| 33/134 (24.6) \| 171/693 (24.7) \| \| r-axSpA, n/m (%) \| 685/906 (75.6) \| 478/617 (77.5) \| 55/98 (56.1) \| \| 685/685 (100.0) \| 57/72 (79.2) \| 101/134 (75.4) \| 522/693 (75.3) \| \| MRI sacroiliitis according to the ASAS definition; n/m (%) \| 401/491 (81.7) \| 397/441 (90.0) \| 0/14 (0.0) \| 83/95 (87.4) \| 318/396 (80.3) \| 50/53 (94.3) \| 61/74 (82.4) \| 289/363 (79.6) \| \| Past or present arthritis/synovitis, n/m (%) \| 258/803 (32.1) \| 163/544 (30.0) \| 33/91 (36.3) \| 64/196 (32.7) \| 194/607 (32.0) \| 13/69 (18.8) \| 38/119 (31.9) \| 206/611 (33.7) \| \| Past or present enthesitis, n/m (%) \| 314/756 (41.5) \| 221/520 (42.5) \| 29/92 (31.5) \| 67/178 (37.6) \| 247/578 (42.7) \| 14/64 (21.9) \| 46/110 (41.8) \| 254/579 (43.9) \| \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| Past or present extra-rheumatological manifestations \| \| IBD, n/m (%) \| 22/878 (2.5) \| 17/599 (2.8) \| 1/96 (1.0) \| 3/212 (1.4) \| 19/666 (2.9) \| 0/70 (0.0) \| 1/130 (0.8) \| 21/672 (3.1) \| \| Psoriasis, n/m (%) \| 199/791 (25.2) \| 140/549 (25.5) \| 20/88 (22.7) \| 56/196 (28.6) \| 143/595 (24.0) \| 8/63 (12.7) \| 27/124 (21.8) \| 163/599 (27.2) \| \| Uveitis, n/m (%) \| 131/859 (15.3) \| 86/583 (14.8) \| 9/94 (9.6) \| 22/207 (10.6) \| 109/652 (16.7) \| 7/69 (10.1) \| 22/129 (17.1) \| 100/655 (15.3) \| \| At least one OSI, n/m (%) \| 617/715 (86.3) \| – \| – \| 139/182 (76.4) \| 478/533 (89.7) \| 63/68 (92.6) \| 86/100 (86.0) \| 463/542 (85.4) \| \| CRP≥5 mg/L or ESR≥28 mm/h at secukinumab initiation, n/m (%) \| 282/683 (41.3) \| – \| – \| 67/171 (39.2) \| 215/512 (42.0) \| 30/64 (46.9) \| 35/99 (35.4) \| 214/514 (41.6) \| \| MRI OSI at the sacroiliac joint or spine, n/m (%) \| 488/703 (69.4) \| – \| – \| 108/186 (58.1) \| 380/517 (73.5) \| 58/65 (89.2) \| 73/104 (70.2) \| 353/530 (66.6) \| ## Secukinumab treatment At initiation, $95\%$ of patients received loading dose of secukinumab, of which $86.4\%$ received 150 mg dose and $9.7\%$ received 300 mg dose every week for the first 4 weeks. After 4 weeks, $82.8\%$ and $9.1\%$ of the patients received maintenance dose of secukinumab 150 mg and 300 mg every 4 weeks, respectively. After secukinumab initiation, an increase in the dosage was required in 164 patients ($18.1\%$) due to insufficient efficacy and in 60 patients ($6.6\%$) due to end-dose effect. At the time of secukinumab initiation, $48.6\%$ of patients were on non-steroidal anti-inflammatory drugs (NSAIDs) (since the onset of the disease $92.2\%$ ($\frac{819}{888}$) of the patients received at least once a NSAID for their axSpA (mean number of NSAIDs used: 3.3±18)), $11.6\%$ on conventional synthetic disease-modifying antirheumatic drugs and $7.3\%$ on corticosteroids as concomitant treatment. ## Secukinumab retention rate After starting secukinumab treatment, the mean (SD) follow-up duration was 844.7 (294.4) days. The mean (SD) time to definitive discontinuation of secukinumab was 274.7 (200.9) days and the number of patients who discontinued treatment was 476. The majority of treatment discontinuations were due to inefficacy ($$n = 360$$; $75.6\%$), intolerance ($$n = 88$$; $18.5\%$) and other reasons ($$n = 28$$; $5.9\%$) (figure 2). The main reasons for discontinuation due to intolerance included infection ($$n = 22$$; $4.6\%$), allergy ($$n = 8$$; $1.68\%$), IBD ($$n = 3$$; $0.63\%$) and uveitis ($$n = 2$$; $0.42\%$). The percentage values of discontinuation were calculated against the total number of patients that discontinued treatment ($$n = 476$$). The KM retention rate for secukinumab was $76\%$ ($95\%$ CI 74 to $79\%$) at month 6 and $59\%$ ($95\%$ CI 55 to $62\%$) at month 12, with no differences observed between r-axSpA and nr-axSpA patients. Figure 2:* Secukinumab retention rate with event (discontinuation) defined by inefficacy or intolerance or other reason. ## Univariate Cox regression The hazard of definitive discontinuation of secukinumab at 1 year was 1.20 times higher ($95\%$ CI 0.84 to 1.72) in patients with at least one OSI versus patients without OSI. The difference was not statistically significant ($$p \leq 0.316$$) (table 2). Past or present history of IBD (Crohn’s disease or haemorrhagic rectocolitis) (HR: 1.96 ($95\%$ CI 1.13 to 3.41); $$p \leq 0.017$$) and ≥third line of secukinumab treatment (HR: 1.69 ($95\%$ CI 1.08 to 2.66); $$p \leq 0.023$$) were the only significant factors associated with secukinumab treatment discontinuation at 1 year (table 2). Table 2 \| N=903† \| N=903†.1 \| % patients stillon treatmentat 1 year \| Univariate Cox regressions \| Univariate Cox regressions.1 \| Univariate Cox regressions.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Predictive factor \| Modality* (N) \| % patients stillon treatmentat 1 year \| HR (95% CI) \| P value‡ \| P type III \| \| At least one objective sign of inflammation \| No = (N=97)* \| 65.3% \| 1.20 (0.84 to 1.72) \| 0.316 \| \| \| At least one objective sign of inflammation \| Yes (N=616) \| 58.8% \| \| \| \| \| Age (years) \| ≤40 (N=288)* \| \| \| \| 0.231 \| \| Age (years) \| >60 (N=108) \| 59.3% \| 0.90 (0.64 to 1.27) \| 0.550 \| 0.231 \| \| Age (years) \| 40–60 (N=507) \| 61.3% \| 0.82 (0.66 to 1.03) \| 0.088 \| 0.231 \| \| Gender \| Male (N=380)* \| 50.9% \| \| \| \| \| Gender \| Female (N=523) \| 59.2% \| 1.03 (0.84 to 1.27) \| 0.776 \| \| \| BMI (kg/m2) \| Normal weight (≥18.5 and <25) (N=200)* \| 64.2% \| \| \| \| \| BMI (kg/m2) \| Obesity (≥30) (N=133) \| 63.4% \| 1.03 (0.72 to 1.48) \| 0.870 \| 0.944 \| \| BMI (kg/m2) \| Pre-obesity (≥25 and <30) (N=186) \| 60.8% \| 1.09 (0.79 to 1.51) \| 0.590 \| \| \| BMI (kg/m2) \| Underweight (<18.5) (N=12) \| 66.7% \| 0.90 (0.33 to 2.47) \| 0.840 \| \| \| Smoking status (at secukinumab initiation) \| Never (N324)* \| 61.0% \| \| \| 0.595 \| \| Smoking status (at secukinumab initiation) \| Former smoker (N=158) \| 60.8% \| 1.02 (0.75 to 1.38) \| 0.912 \| 0.595 \| \| Smoking status (at secukinumab initiation) \| Current smoker (N=231) \| 57.4% \| 1.14 (0.88 to 1.48) \| 0.328 \| 0.595 \| \| Diagnosis delay (years) \| ≤2.5 (N=289)* \| 59.3% \| \| \| 0.106 \| \| Diagnosis delay (years) \| 2.5 to ≤5 (N=89) \| 71.1% \| 0.63 (0.41 to 0.97) \| 0.034 \| 0.106 \| \| Diagnosis delay (years) \| <5 (N=179) \| 59.8% \| 0.91 (0.68 to 1.22) \| 0.537 \| 0.106 \| \| Disease duration (years) \| ≤5 (N=345)* \| 57.7% \| \| \| 0.573 \| \| Disease duration (years) \| 5 to ≤(N=192) \| 61.9% \| 0.88 (0.67 to 1.17) \| 0.385 \| 0.573 \| \| Disease duration (years) \| >10 (N=268) \| 60.4% \| 0.89 (0.70 to 1.15) \| 0.378 \| 0.573 \| \| HLA-B27 positivity \| No (N=297)* \| 60.1% \| \| \| \| \| HLA-B27 positivity \| Yes (N=525) \| 58.8% \| 1.05 (0.84 to 1.32) \| 0.652 \| \| \| Radiological structural damage according to mNY criteria \| No (N=155)* \| 55.8% \| \| \| \| \| Radiological structural damage according to mNY criteria \| Yes (N=325) \| 58.9% \| 0.94 (0.70 to 1.25) \| 0.666 \| \| \| CRP≥5 mg/L or ESR≥28 mm if CRP not available \| No (N=398) \| 59.1% \| \| \| 0.42 \| \| CRP≥5 mg/L or ESR≥28 mm if CRP not available \| Yes (N=282) \| 62.4% \| 0.90 (0.71 to 1.16) \| 0.422 \| 0.42 \| \| Sign of inflammation in the MRI of the sacroiliitis or spine regardless of the date prior to the initial prescription of secukinumab \| No (N=214) \| 66.0% \| \| \| 0.06 \| \| Sign of inflammation in the MRI of the sacroiliitis or spine regardless of the date prior to the initial prescription of secukinumab \| Yes (N=487) \| 57.4% \| 1.29 (0.99 to 1.68) \| 0.063 \| 0.06 \| \| Past or present history of active arthritis/synovitis diagnosed by a doctor \| No (N=543) \| 59.3% \| \| \| \| \| Past or present history of active arthritis/synovitis diagnosed by a doctor \| Yes (N=258) \| 62.8% \| 0.88 (0.69 to 1.11) \| 0.278 \| \| \| Past or present history of psoriasis \| No (N=589)* \| 60.6% \| \| \| 0.660 \| \| Past or present history of psoriasis \| Yes (N=199) \| 58.3% \| 1.06 (0.82 to 1.36) \| 0.063 \| 0.660 \| \| Past or present history of uveitis \| No (N=725)* \| 59.6% \| \| \| \| \| Past or present history of uveitis \| Yes (N=131) \| 61.8% \| 0.92 (0.68 to 1.24) \| 0.576 \| \| \| Past or present history of IBD (Crohn’s disease or haemorrhagic rectocolitis) \| No (N=853)* \| 59.8% \| \| \| \| \| Past or present history of IBD (Crohn’s disease or haemorrhagic rectocolitis) \| Yes (N=22) \| 40.9% \| 1.96 (1.13 to 3.41) \| 0.017 \| \| \| Secukinumab maintenance dose at initiation (per month) \| 150 mg (N=747)* \| 61.1% \| \| \| 0.940 \| \| Secukinumab maintenance dose at initiation (per month) \| 300 mg (N=88) \| 62.5% \| 0.94 (0.65 to 1.34) \| 0.727 \| 0.940 \| \| Secukinumab maintenance dose at initiation (per month) \| Other (N=5) \| 100% \| <0.01 [0.01 to >999.99) \| 0.963 \| 0.940 \| \| Secukinumab treatment line \| First line (N71)* \| 72.2% \| \| \| 0.062 \| \| Secukinumab treatment line \| Second line (N=133) \| 62.7% \| 1.49 (0.89 to 2.51) \| 0.129 \| 0.062 \| \| Secukinumab treatment line \| ≥Third line (N=692) \| 57.6% \| 1.69 (1.08 to 2.66) \| 0.023 \| 0.062 \| \| Concomitant treatment with csDMARDs at initiation \| No (N=758)* \| 59.4% \| \| \| \| \| Concomitant treatment with csDMARDs at initiation \| Yes (N=145) \| 60% \| 0.93 (0.70 to 1.23) \| 0.604 \| \| \| Oral corticosteroids intake at initiation of secukinumab \| No (N=724)* \| 60.5% \| \| \| \| \| Oral corticosteroids intake at initiation of secukinumab \| Yes (N=57) \| 63.2% \| 0.90 (0.58 to 1.40) \| 0.633 \| \| \| History of depression or anti-depressive concomitant treatment \| No (N=703)* \| 60.8% \| \| \| \| \| History of depression or anti-depressive concomitant treatment \| Yes (N=165) \| 54.5% \| 1.25 (0.97 to 1.61) \| 0.089 \| \| \| History or suspicion of fibromyalgia \| No (N=787)* \| 60.4% \| \| \| \| \| History or suspicion of fibromyalgia \| Yes (N=89) \| 52.8% \| 1.25 (0.90 to 1.72) \| 0.181 \| \| \| History of depression or anti-depressive concomitant treatment or history or suspicion of fibromyalgia \| No (N=641)* \| 61.0% \| \| \| \| \| History of depression or anti-depressive concomitant treatment or history or suspicion of fibromyalgia \| Yes (N=226) \| 55.3% \| 1.22 (0.97 to 1.54) \| 0.090 \| \| \| Concomitant treatment with a PPI \| No (N=609)* \| 59.4% \| \| \| \| \| Concomitant treatment with a PPI \| Yes (N=246) \| 59.5% \| 0.99 (0.78 to 1.25) \| 0.931 \| \| A post hoc analysis evaluating specifically the impact of the line of therapy on secukinumab retention rate with regards to the presence (vs absence) of OSI showed the following (values given are number and (%) of patients still on treatment at 1 year in the group of patients with versus without OSI, respectively). ## Multivariate Cox regression After multiple imputation, at least one OSI (HR: 1.44 ($95\%$ CI 1.08 to 1.93); $$p \leq 0.014$$), past or present history of IBD (HR: 1.76 ($95\%$ CI 1.01 to 3.07); $$p \leq 0.047$$) and ≥third line of secukinumab treatment (HR: 1.67 ($95\%$ CI 1.06 to 2.62); $$p \leq 0.028$$) and depression (HR:1.25 (0.97 to 1.60); p=non-significant) were predictors of secukinumab treatment discontinuation at 1 year (table 3). Table 3 \| Predictive factor \| Modality* (N) \| Multivariate Cox regression \| Multivariate Cox regression.1 \| Multivariate Cox regression.2 \| \| --- \| --- \| --- \| --- \| --- \| \| Predictive factor \| Modality* (N) \| HR (95% CI) \| P value† \| P type III \| \| At least one objective sign of inflammation \| No (N=165)* \| \| \| \| \| At least one objective sign of inflammation \| Yes (N=711) \| 1.44 (1.08 to 1.93) \| 0.014 \| \| \| Secukinumab treatment line \| First line (N=68)* \| \| \| 0.084 \| \| Secukinumab treatment line \| Second line (N=132) \| 1.53 (0.91 to 2.57) \| 0.107 \| 0.084 \| \| Secukinumab treatment line \| ≥Third line (N=676) \| 1.67 (1.06 to 2.62) \| 0.028 \| 0.084 \| \| Past or present history of IBD \| No (N=854)* \| \| \| \| \| Past or present history of IBD \| Yes (N=22) \| 1.76 (1.01 to 3.07) \| 0.047 \| \| \| History of depression or anti-depressive concomitant treatment \| No (N=716)* \| \| \| \| \| History of depression or anti-depressive concomitant treatment \| Yes (N=160) \| 1.25 (0.97 to 1.60) \| 0.090 \| \| ## KM curve The 1-year KM retention of secukinumab according to predictive factors identified in the Cox multivariate analysis is presented in figure 3. The 1-year KM retention rate for secukinumab was $58\%$ ($95\%$ CI 54 to $62\%$) and $64\%$ ($95\%$ CI 54 to $73\%$) ($$p \leq 0.315$$) for patients with or without OSI, respectively; $41\%$ ($95\%$ CI 21 to $60\%$) and $59\%$ ($95\%$ CI 56 to $62\%$) ($$p \leq 0.015$$) for patients with or without IBD. The 1-year KM retention rate for secukinumab was numerically greater in first line versus second and ≥third line ($70\%$ ($95\%$ CI $59\%$ to $81\%$), $62\%$ ($95\%$ CI $54\%$ to $70\%$) and $57\%$ ($95\%$ CI 53 to $61\%$); ($$p \leq 0.059$$), respectively) treatment groups. Figure 3: Kaplan-Meier analysis of time to definitive secukinumab discontinuation defined by (A) OSI. (B) Past or present History of IBD. (C) Line of treatment. axSpA, axial spondyloarthritis; IBD, inflammatory bowel disease; OSI, objective sign of inflammation. ## Retention rate with regards to the time of MRI assessment In the subgroup of patients with presence of inflammation at MRI and for whom this information was available ($$n = 402$$ patients), the pourcentage of patients still on treatment was $65\%$, $66\%$, $59\%$ and $56\%$ in case this MRI had been performed within 3, 6, 12 months and more than 12 months prior baseline visit. ## Safety During the treatment period, 186 patients ($20.5\%$) had at least one adverse event related to secukinumab, which led to treatment adaptation for 79 ($8.7\%$) patients, hospitalisation for 22 ($2.4\%$) patients and to treatment discontinuation for 121 ($13.4\%$) patients. As expected, infections and infestations were the most common adverse events ($10.4\%$), followed by gastrointestinal disorders ($4.6\%$). Of these gastrointestinal disorders, there were three cases of Crohn’s disease, and all three of these required treatment discontinuation; hospitalisation was also required in one case and there was also one case of haemorrhagic rectocolitis. In addition, uveitis was reported in seven cases and led to therapy discontinuation in four of these cases. ## Discussion This study evaluated the rate of retention of secukinumab in real-world practice at the time it was approved in France and made it possible to highlight certain factors associated with this drug retention. Treatment with secukinumab showed a $59\%$ retention rate after 1 year of treatment in patients mostly refractory to biological therapy under real-world conditions. The main cause of discontinuation in our study was lack of efficacy. Prior exposure to b/tsDMARD, OSI and past or present history of IBD were identified as predictive factors of secukinumab discontinuation. The previously reported 1-year retention rate of secukinumab in real-world settings was in the range of $61\%$–$79\%$ in both axSpA and PsA patients.17 19 20 31 In comparison to these findings, retention rate in the current study was slightly lower, presumably because large proportion of patients received secukinumab as ≥third line of therapy. Furthermore, based on literature, the retention rate of anti-TNF agents in the French population is lower than in other countries.32 This could be explained by the less restrictive access to treatment in France, facilitating faster treatment switches. A disparity of secukinumab retention rates between countries has also been observed in the European Spondyloarthritis Research collaboration Network study.20 33 In various RCTs and RWE studies of axSpA patients, the factors reported to influence anti-TNF therapy efficacy and retention rate are smoking status, young age, gender, HLA-B27 positivity, radiographic status, OSI and rank of drug administration.24 25 This study aimed to evaluate whether OSI is associated with secukinumab retention and to determine if other predictive factors could be identified. The main predictive factors associated with secukinumab discontinuation were the presence of OSI, line of treatment and history of IBD. Based on the univariate Cox regression analysis, at least one OSI at 1 year was not a significant predictor of secukinumab discontinuation; however, after multiple imputation, the multivariate analysis revealed that OSI was a significant predictor of secukinumab discontinuation. This result was unexpected as it was observed that patients with active inflammatory disease were more likely to benefit from anti-TNF.25 Although >$20\%$ of the data for OSI were missing, the variable was forced into the multivariate model irrespective of its significance level or rate of missing data. In this study, the majority (>$85\%$) of patients presented with OSI at treatment initiation, which might also impact the analysis. Moreover, in the MEASURE trials that assessed efficacy of secukinumab in patients with r-axSpA, even if a good response was observed in patients with a CRP level <5 mg/L, the magnitude of response to secukinumab was higher in patients with an elevated CRP level ≥5 mg/L.34 Another reason to explain this unexpected result was the fact that in this analysis, we have defined a « positive » MRI by any MRI with presence of inflammation whenever this MRI had been performed. The post hoc analysis performed in the subgroup of patients with positive MRI and with the availability of the date of the MRI with regards to the initiation of secukinumab suggested a better retention rate at 1 year in case of a recent positive MRI ($65\%$ vs $56\%$ in case an MRI performed within 6 months vs more than 12 months prior secukinumab initiation, respectively. However, one could also explain these results (absence of OSI as a predisposing factor of a better drug retention rate) by the fact that secukinumab at the time of its launch in France has been considered as the last opportunity for a lot of patients and had been continued in a longer run in patients without objective sign of inflammation than in the patients with presence of OSI. In this latter group a re-initiation of a previous biotherapy and/or another anti-TNF could have been proposed. Finally, the divergence between the results of the uni- versus multi-variate analyses and also the narrow difference between these two groups (see figure 3C) might also suggest that this statistically significant difference is without any clinical relevance. The additional analyses conducted in different sub-groups of patients with regard to the line of therapy and the presence vs absence of OSI are questionable because of 1) the low number of patients in some categories and 2) the high percentage of patients with OSI. It is well established that the retention rate of secukinumab is the highest when this therapy is used as the first biological agent in patients with axSpA or PsA compared with when it is used as second or third line of treatment,20 22 35 likewise with any b/tsDMARD. In alignment with other real-world studies, better retention of secukinumab in first-line treatment compared with ≥third-line treatment ($70\%$ vs $57\%$) was observed in this study and ≥third line of secukinumab was a significant predictor of secukinumab discontinuation at 1 year.17 35 Past or present history of IBD was found to be a significant predictor of secukinumab discontinuation. Contrary to anti-TNF therapies, secukinumab is known to be ineffective in IBD. It is established that there is a low incidence rate of developing new-onset IBD or exacerbating preexisting IBD with anti-IL-17 therapy.36 In generally, patients with IBD who discontinue anti-TNF therapy tend to experience a clinical relapse.36–39 As this study included patients with history of IBD who were previously on anti-TNF therapy and then switched to secukinumab, it is possible that they experienced a relapse of IBD after discontinuation of anti-TNF therapy, which then led to discontinuation of secukinumab. A history of depression was also identified as a predictor of secukinumab discontinuation although the effect was not significant, consistent with the results reported in the Cantabria and ASTURias study.17 In contrast to anti-TNF therapies, gender, young age, HLA-B27 positivity and radiographic status were not predictors of secukinumab retention in this study. The findings on gender were consistent with the MEASURE studies, where efficacy and safety outcomes were comparable between male and female axSpA patients treated with secukinumab over 52 weeks.40 In the real-word German AQUILA study in r-axSpA, the secukinumab retention rate between male and female patients was not significantly different.41 In pooled analyses of the MEASURE studies, secukinumab was effective in patients with r-axSpA regardless of their HLA-B27 status; however, patients who were HLA-B27 positive seemed to derive increased therapeutic benefit than those who were HLA-B27 negative.42 Other than the aforementioned factors, baseline patient characteristics did not have a major impact on the overall secukinumab retention in this study. Overall, secukinumab treatment was well tolerated in patients with axSpA. Throughout the treatment period, no new or unexpected safety signals were observed. The safety profile was consistent with the established safety profile across approved indications.43 The strength of this study lies in the fact that the findings complement clinical trial results. RWE studies provide valuable data on the predictive factors for retention, safety and survival of secukinumab in a heterogeneous French patient population with comorbidities, which is not commonly reported in the RCT. Furthermore, the study enrolled a larger patient population than previously published studies.17 19 31 This study has some limitations; due to the retrospective collection of patient data exclusively from that available in the source record, the number of data gaps inherent in this type of study was inevitable. The anticipated high frequency of missing data concerning several parameters/items was the main reason of the choice of drug retention rate as the primary outcome in this study since we anticipated that this information (date of initiation and date of discontinuation of the drug) will be available in the majority of patients in contrast to other parameters/items. Therefore, despite we can be quite confident concerning this primary analysis (eg, % patients still on treatment overtime), the other analyses and in particular the evaluation of the predisposing factors of this retention rate can be more questionable. A small proportion of patients received secukinumab as the first or second-line treatment, whereas a larger number of patients received it as ≥third-line treatment. Also, a higher proportion of patients with r-axSpA received secukinumab compared with those with nr-axSpA. Several factors indicated as predictors of anti-TNF therapy retention, such as BMI and smoking status, could not be included in the multivariate Cox regression analysis because >$20\%$ of the data were missing. The high percentage of missing data concerning obesity and smoking could be also considered as a weakness of the study since these two patients’ characteristics (and in particular obesity) have been previously reported as predisposing factors of IL-17 retention rate in axSpA with conflicting results: obesity associated with a longer44 or a shorter retention rate. It is obviously important to check whether there is a difference in the predisposing factors of drug retention rate based on their mechanism of action to potentially guide the choice of the drug (IL17 vs TNF inhibitors) to use as the first biotherapy in daily practice. For example, the fact that obesity has been reported with a better retention rate of IL-17-inhibitors might be explained by the IL-17-pathway since obesity has been shown to promote Th17 differentiation and IL-17 production45 However, the conflicting results observed in the different reported clinical studies regarding the impact of obesity on IL-17 inhibitors retention rate preclude any specific recommendation for the use of IL-17 inhibitors in this group of patients. In summary, the overall retention of secukinumab in daily practice in the period following its approval in France was approximately $59\%$ at 1 year in axSpA patients. Prior exposure to b/tsDMARDS, OSI and IBD was identified as predictive factors of secukinumab discontinuation. It might be of interest to replicate this study by evaluating this retention rate remotely from the launch of the molecule. ## Data availability statement Data are available upon reasonable request. ## Patient consent for publication Not applicable. ## References 1. Poddubnyy D. Axial spondyloarthritis: is there a treatment of choice?. Ther Adv Musculoskelet Dis (2013.0) 5 45-54. DOI: 10.1177/1759720X12468658 2. Ranganathan V, Gracey E, Brown MA. Pathogenesis of ankylosing spondylitis-recent advances and future directions. Nat Rev Rheumatol (2017.0) 13 359-67. DOI: 10.1038/nrrheum.2017.56 3. Taams LS, Steel KJA, Srenathan U. Il-17 in the immunopathogenesis of spondyloarthritis. Nat Rev Rheumatol (2018.0) 14 453-66. DOI: 10.1038/s41584-018-0044-2 4. Miossec P, Kolls JK. Targeting IL-17 and Th17 cells in chronic inflammation. Nat Rev Drug Discov (2012.0) 11 763-76. DOI: 10.1038/nrd3794 5. Braun J, Kiltz U, Bühring B. Secukinumab in axial spondyloarthritis: a narrative review of clinical evidence. Ther Adv Musculoskelet Dis (2021.0) 13. DOI: 10.1177/1759720X211041854 6. Braun J, Baraliakos X, Deodhar A. Effect of secukinumab on clinical and radiographic outcomes in ankylosing spondylitis: 2-year results from the randomised phase III measure 1 study. Ann Rheum Dis (2017.0) 76 1070-7. DOI: 10.1136/annrheumdis-2016-209730 7. Deodhar A, Blanco R, Dokoupilová E. Improvement of signs and symptoms of nonradiographic axial spondyloarthritis in patients treated with secukinumab: primary results of a randomized, placebo-controlled phase III study. Arthritis Rheumatol (2021.0) 73 110-20. DOI: 10.1002/art.41477 8. Marzo-Ortega H, Sieper J, Kivitz AJ. 5-Year efficacy and safety of secukinumab in patients with ankylosing spondylitis: end-of-study results from the phase 3 measure 2 trial. The Lancet Rheumatology (2020.0) 2 e339-46. DOI: 10.1016/S2665-9913(20)30066-7 9. Pavelka K, Kivitz A, Dokoupilova E. Efficacy, safety, and tolerability of secukinumab in patients with active ankylosing spondylitis: a randomized, double-blind phase 3 study, measure 3. Arthritis Res Ther (2017.0) 19. DOI: 10.1186/s13075-017-1490-y 10. Deodhar A, Conaghan PG, Kvien TK. Secukinumab provides rapid and persistent relief in pain and fatigue symptoms in patients with ankylosing spondylitis irrespective of baseline C-reactive protein levels or prior tumour necrosis factor inhibitor therapy: 2-year data from the measure 2 study. Clin Exp Rheumatol (2019.0) 37 260-9. PMID: 30148436 11. Baraliakos X, Gossec L, Pournara E. Secukinumab in patients with psoriatic arthritis and axial manifestations: results from the double-blind, randomised, phase 3 maximise trial. Ann Rheum Dis (2021.0) 80 582-90. DOI: 10.1136/annrheumdis-2020-218808 12. Baraliakos X, Østergaard M, Gensler LS. Comparison of the effects of secukinumab and adalimumab biosimilar on radiographic progression in patients with ankylosing spondylitis: design of a randomized, phase IIIB study (surpass). Clin Drug Investig (2020.0) 40 269-78. DOI: 10.1007/s40261-020-00886-7 13. Mahajan R. Real world data: additional source for making clinical decisions. Int J Appl Basic Med Res (2015.0) 5 82. DOI: 10.4103/2229-516X.157148 14. Sherman RE, Anderson SA, Dal Pan GJ. Real-World evidence-what is it and what can it tell us?. N Engl J Med (2016.0) 375 2293-7. DOI: 10.1056/NEJMsb1609216 15. Villines TC, Cziraky MJ, Amin AN. Awareness, knowledge, and utility of RCT data vs RWE: results from a survey of US cardiologists: real-world evidence in clinical decision making. Clin Med Insights Cardiol (2020.0) 14. DOI: 10.1177/1179546820953410 16. Berger ML, Sox H, Willke RJ. Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE special Task force on real-world evidence in health care decision making. Pharmacoepidemiol Drug Saf (2017.0) 26 1033-9. DOI: 10.1002/pds.4297 17. Alonso S, Villa I, Fernández S. Multicenter study of secukinumab survival and safety in spondyloarthritis and psoriatic arthritis: secukinumab in Cantabria and asturias study. Front Med (Lausanne) (2021.0) 8. DOI: 10.3389/fmed.2021.679009 18. Chimenti MS, Fonti GL, Conigliaro P. One-Year effectiveness, retention rate, and safety of secukinumab in ankylosing spondylitis and psoriatic arthritis: a real-life multicenter study. Expert Opin Biol Ther (2020.0) 20 813-21. DOI: 10.1080/14712598.2020.1761957 19. García-Dorta A, León-Suarez P, Peña S. Association of gender, diagnosis, and obesity with retention rate of secukinumab in spondyloarthropathies: results form a multicenter real-world study. Front Med (Lausanne) (2021.0) 8. DOI: 10.3389/fmed.2021.815881 20. Michelsen B, Lindström U, Codreanu C. Drug retention, inactive disease and response rates in 1860 patients with axial spondyloarthritis initiating secukinumab treatment: routine care data from 13 registries in the eurospa collaboration. RMD Open (2020.0) 6. DOI: 10.1136/rmdopen-2020-001280 21. Min HK, Kim H-R, Lee S-H. Retention rate and effectiveness of secukinumab vs TNF inhibitor in ankylosing spondylitis patients with prior TNF inhibitor exposure. Rheumatology (Oxford) (2021.0) 60 5743-52. DOI: 10.1093/rheumatology/keab245 22. Moreno-Ramos MJ, Sanchez-Piedra C, Martínez-González O. Real-world effectiveness and treatment retention of secukinumab in patients with psoriatic arthritis and axial spondyloarthritis: A descriptive observational analysis of the spanish BIOBADASER registry. Rheumatol Ther (2022.0) 9 1031-47. DOI: 10.1007/s40744-022-00446-9 23. Gentileschi S, Rigante D, Sota J. Long-term effectiveness of secukinumab in patients with axial spondyloarthritis. Mediators Inflamm (2020.0) 2020. DOI: 10.1155/2020/6983272 24. Williams T, Wadeley A, Bond D. Real-World experience of secukinumab treatment for ankylosing spondylitis at the Royal national Hospital for rheumatic diseases, Bath. Clin Rheumatol (2020.0) 39 1501-4. DOI: 10.1007/s10067-020-04944-5 25. Maksymowych WP, Kumke T, Auteri SE. Predictors of long-term clinical response in patients with non-radiographic axial spondyloarthritis receiving certolizumab pegol. Arthritis Res Ther (2021.0) 23. DOI: 10.1186/s13075-021-02650-4 26. Ngo MD, Zummer M, Andersen KM. First biologic drug persistence in patients with ankylosing spondylitis and nonradiographic axial spondyloarthritis: a real-world Canadian physicians’ experience. J Clin Rheumatol (2022.0) 28 e210-6. DOI: 10.1097/RHU.0000000000001693 27. Arends S, van der Veer E, Kallenberg CGM. Baseline predictors of response to TNF-α blocking therapy in ankylosing spondylitis. Curr Opin Rheumatol (2012.0) 24 290-8. DOI: 10.1097/BOR.0b013e32835257c5 28. Retrospective observational french study evaluating whether objective signs of inflammation are associated with retention under secukinumab treatment at 1 year in patients treated for spondyloarthritis 29. Guidelines for good pharmacoepidemiology practice (GPP). Pharmacoepidemiol Drug Saf (2016.0) 25 2-10. DOI: 10.1002/pds.3891 30. Rubin DB. Multiple Imputation for Nonresponse in Surveys (1987.0). DOI: 10.1002/9780470316696 31. Favalli EG, Marchesoni A, Balduzzi S. FRI0273 EFFECTIVENESS and retention rate of secukinumab for psoriatic arthritis and axial spondyloarthritis: real-life data from the italian LORHEN registry. Ann Rheum Dis (2020.0) 79 722. DOI: 10.1136/annrheumdis-2020-eular.3197 32. Frazier-Mironer A, Dougados M, Mariette X. Retention rates of adalimumab, etanercept and infliximab as first and second-line biotherapy in patients with rheumatoid arthritis in daily practice. Joint Bone Spine (2014.0) 81 352-9. DOI: 10.1016/j.jbspin.2014.02.014 33. Lindström U, Di Giuseppe D, Delcoigne B. Effectiveness and treatment retention of TNF inhibitors when used as monotherapy versus comedication with csdmards in 15 332 patients with psoriatic arthritis. data from the eurospa collaboration. Ann Rheum Dis (2021.0) 80 1410-8. DOI: 10.1136/annrheumdis-2021-220097 34. Braun J, Deodhar A, Landewé R. Impact of baseline C-reactive protein levels on the response to secukinumab in ankylosing spondylitis: 3-year pooled data from two phase III studies. RMD Open (2018.0) 4. DOI: 10.1136/rmdopen-2018-000749 35. Glintborg B, Lindstrom U, Di Giuseppe D. One-year treatment outcomes of secukinumab versus tumor necrosis factor inhibitors in spondyloarthritis. Arthritis Care Res (Hoboken) (2020.0). DOI: 10.1093/rheumatology/keaa825 36. Caron B, Jouzeau J-Y, Miossec P. Gastroenterological safety of IL-17 inhibitors: a systematic literature review. Expert Opin Drug Saf (2022.0) 21 223-39. DOI: 10.1080/14740338.2021.1960981 37. Gisbert JP, Marín AC, Chaparro M. Systematic review: factors associated with relapse of inflammatory bowel disease after discontinuation of anti-TNF therapy. Aliment Pharmacol Ther (2015.0) 42 391-405. DOI: 10.1111/apt.13276 38. Gisbert JP, Marín AC, Chaparro M. The risk of relapse after anti-TNF discontinuation in inflammatory bowel disease: systematic review and meta-analysis. Am J Gastroenterol (2016.0) 111 632-47. DOI: 10.1038/ajg.2016.54 39. Kennedy NA, Warner B, Johnston EL. Relapse after withdrawal from anti-TNF therapy for inflammatory bowel disease: an observational study, plus systematic review and meta-analysis. Aliment Pharmacol Ther (2016.0) 43 910-23. DOI: 10.1111/apt.13547 40. van der Horst-Bruinsma I, Miceli-Richard C, Braun J. A pooled analysis reporting the efficacy and safety of secukinumab in male and female patients with ankylosing spondylitis. Rheumatol Ther (2021.0) 8 1775-87. DOI: 10.1007/s40744-021-00380-2 41. Kiltz U, Brandt-Juergens J, Kästner P. POS0899 how does gender affect secukinumab treatment outcomes and retention rates in patients with ankylosing spondylitis?-real world data from the German AQUILA study. Ann Rheum Dis (2021.0) 80 706-7. DOI: 10.1136/annrheumdis-2021-eular.141 42. Deodhar A, Mease PJ, Poddubnyy D. FRI0271 IMPACT of HLA-B27 status on clinical outcomes among patients with ankylosing spondylitis treated with secukinumab. Ann Rheum Dis (2020.0) 79 721. DOI: 10.1136/annrheumdis-2020-eular.1448 43. Deodhar A, Mease PJ, McInnes IB. Long-Term safety of secukinumab in patients with moderate-to-severe plaque psoriasis, psoriatic arthritis, and ankylosing spondylitis: integrated pooled clinical trial and post-marketing surveillance data. Arthritis Res Ther (2019.0) 21. DOI: 10.1186/s13075-019-1882-2 44. Armağan B, Kılıç L, Farisoğulları B. Drug retention rate and predictive factors of drug survival for secukinumab in radiographic axial spondyloarthritis. Rheumatol Int (2023.0) 43 147-56. DOI: 10.1007/s00296-022-05213-1 45. Ahmed M, Gaffen SL. IL-17 in obesity and adipogenesis. Cytokine Growth Factor Rev (2010.0) 21 449-53. DOI: 10.1016/j.cytogfr.2010.10.005
--- title: 'Cohort profile: InfCareHIV, a prospective registry-based cohort study of people with diagnosed HIV in Sweden' authors: - Christina Carlander - Johanna Brännström - Fredrik Månsson - Olof Elvstam - Pernilla Albinsson - Simon Blom - Lena Mattsson - Sanne Hovmöller - Hans Norrgren - Åsa Mellgren - Veronica Svedhem - Magnus Gisslén - Anders Sönnerborg journal: BMJ Open year: 2023 pmcid: PMC10030896 doi: 10.1136/bmjopen-2022-069688 license: CC BY 4.0 --- # Cohort profile: InfCareHIV, a prospective registry-based cohort study of people with diagnosed HIV in Sweden ## Abstract ### Purpose The Swedish InfCareHIV cohort was established in 2003 to ensure equal and effective care of people living with HIV (PLHIV) and enable long-term follow-up. InfCareHIV functions equally as a decision support system as a quality registry, ensuring up-to-date data reported in real time. ### Participants InfCareHIV includes data on >$99\%$ of all people with diagnosed HIV in Sweden and up to now 13 029 have been included in the cohort. InfCareHIV includes data on HIV-related biomarkers and antiretroviral therapies (ART) and also on demographics, patient-reported outcome measures and patient-reported experience measures. ### Findings to date Sweden was in 2015 the first country to reach the UNAIDS (United Nations Programme on HIV/AIDS)/WHO’s 90-90-90 goals. Late diagnosis of HIV infection was identified as a key problem in the Swedish HIV-epidemic, and low-level HIV viraemia while on ART associated with all-cause mortality. Increased HIV RNA load in the cerebrospinal fluid (CSF) despite suppression of the plasma viral load was found in $5\%$ of PLHIV, a phenomenon referred to as ‘CSF viral escape’. Dolutegravir-based treatment in PLHIV with pre-existing nucleoside reverse transcriptase inhibitor-mutations was non-inferior to protease inhibitor-based regimens. An increase of transmitted drug resistance was observed in the InfCareHIV cohort. Lower efficacy for protease inhibitors was not due to lower adherence to treatment. Incidence of type 2 diabetes and insulin resistance was high in the ageing HIV population. Despite ART, the risk of infection-related cancer as well as lung cancer was increased in PLHIV compared with HIV-negative. PLHIV were less likely successfully treated for cervical precancer and more likely to have human papillomavirus types not included in current HPV vaccines. Self-reported sexual satisfaction in PLHIV is improving and is higher in women than men. ### Future plans InfCareHIV provides a unique base to study and further improve long-term treatment outcomes, comorbidity management and health-related quality of life in people with HIV in Sweden. ## Introduction With access to antiretroviral therapy (ART) the overall life expectancy of people living with HIV (PLHIV) is close to that of HIV-seronegative people, as shown in a study including European and North American HIV cohorts.1 However, studies from the Swedish InfCareHIV cohort show that after a 15-year follow-up period, successfully treated PLHIV in Sweden were three times more likely to die when compared with HIV-seronegative controls.2 The InfCareHIV cohort is heterogenous, including a large proportion of migrants from 108 countries on all continents, diverse modes of HIV transmission and socioeconomic backgrounds and is gender balanced. The pol sequence of the virus is characterised at diagnosis regarding drug resistance mutations and subtype. All known HIV-1 subtypes, many recombinants as well as unique recombinant forms and HIV-2 are represented. Pretreatment HIV drug resistance is a critical aspect that requires global collaborative studies and to minimise its effect, retention in care and optimal adherence to treatment is essential.3 The Swedish HIV cohort is ageing and faced with increasing risk of age-related comorbidities. Also, chronic HIV-related inflammation, legacy of pretreatment immunodeficiency and injury and past and present ART may add to the development of comorbidity.4 5 Additionally, half of PLHIV in Sweden are still late presenters, defined as CD4 count <350 and/or AIDS at time of diagnosis, which has well-known consequences for prognosis and transmission.6 7 Despite undetectable plasma viral levels, health-related quality of life (HRQoL) is shown to be poorer compared with HIV-seronegative people, in a study from the UK.8 9 This article describes the prospective Swedish HIV cohort, InfCareHIV, including all diagnosed PLHIV in Sweden, with data dating back to the earliest cases in the 1980s. ## Study population The InfCareHIV cohort was established in 2003 in the two largest cities in Sweden, Stockholm and Gothenburg. These two cities care for about half of all PLHIV in Sweden. The other Swedish clinics joined thereafter and since 2008 the cohort has had complete national coverage, including all 29 clinical HIV centres in Sweden. To date (August 2022), 13 029 PLHIV have been included of whom 8436 are currently in active care (table 1, figure 1). In total 42 PLHIV have been diagnosed with HIV-2 of whom 28 are in active care. In the beginning of the HIV epidemic the majority of PLHIV were born in Sweden, most of whom were men who have sex with men ($57.5\%$) or transmitted through intravenous drug use ($26.9\%$) (online supplemental table 1). The number of migrants with HIV, in particular from sub-Saharan Africa (SSA) and Asia, increased gradually, as did the proportion of women and heterosexually transmitted individuals (online supplemental table 1). A study performed between 2009 and 2012 found $58\%$ of newly diagnosed in Sweden to be late presenters (CD4 <350 cells/µL or AIDS) and $38\%$ to have an advanced infection (<200 cells/µL or AIDS). Ten years later the proportion of late diagnosis remains high ($63\%$ and $42\%$ with CD4 <350 and <200, respectively).6 7 Figure 1: Year of HIV diagnosis. People with diagnosis <1983 were diagnosed retrospectively on biobanked blood once HIV-testing was introduced. TABLE_PLACEHOLDER:Table 1 Of those in active care (August 2022) $39\%$ are women and $1.2\%$ are children below 18 years of age. A majority was born outside Sweden ($67\%$), most commonly in SSA ($36\%$). About half ($51\%$) have stated heterosexual mode of transmission, about one-third are men who have sex with men or bisexual men ($31\%$). Only $4\%$ have stated intravenous drug use as mode of transmission while $3\%$ were mother-to-child transmissions, $1.4\%$ through blood products and for $9.6\%$ transmission mode is unknown or information is missing (table 1). Almost all are on ART ($98\%$), and of them almost everybody ($95\%$) reaches the treatment goal of HIV-RNA <50 copies/mL ($98\%$ HIV-RNA <200 copies/mL) in snapshot analysis after at least 6 months of treatment.10 Of the 4483 people no longer on follow-up in InfCareHIV, $56\%$ are deceased, $34\%$ have emigrated and $3\%$ are lost-to follow-up. InCareHIV also includes undocumented migrants who often end-up among the $3\%$ that are lost-to follow-up (table 1). Retrospective data from before the cohort was established as nation-wide in 2008 has been backlogged, including PLHIV deceased before 2008. ## Coverage and validation of the study population The national coverage is nearly $100\%$ as all 29 clinical centres that attend PLHIV are included in InfCareHIV, and more than $99\%$ of all diagnosed PLHIV who are living in Sweden are included. This number has been repeatedly validated on an aggregated level, most recently in 2019, against the number of HIV-diagnoses reported to the Public Health Agency of Sweden, a report that is mandatory by Swedish law. The validations found that data regarding HIV diagnoses were even more accurate in InfCareHIV than in the Public Health Agencies records. When transferring between clinics the person keeps their individual InfCareHIV identity number with no data lost. ## Cohort variables Variables collected and manually registered by a health professional at enrolment include sex at birth, gender identity (added 2021), country of birth, mode of HIV transmission, date of any last negative HIV-test and first positive HIV-test (in Sweden and if relevant abroad), any AIDS diagnoses (eg, pneumocystis pneumonia, oesophageal candidiasis or tuberculosis), confirmed primary HIV infection and suspected country of HIV transmission (online supplemental table 2). Data are collected/updated at each follow-up visit; manually or automatically depending on the type of data and the HIV centre. Data include ART start and stop dates, ART regimen (including doses and mode of administration) and the reason for any change of drug regimens, prophylaxis of opportunistic infections and selected co-medications. Serological and virological data on co-infection with hepatitis C and B virus, weight, date and type of AIDS-defining events, date and cause of death are also included. HIV-RNA (plasma and cerebrospinal fluid (CSF)) and HIV drug resistance (including viral sequences) results, described as both mutations and predicted phenotypic sensitivity, as well as CD4+ and CD8+ T cell counts and CD4/CD8 ratios are either automatically or manually included depending on the HIV centre. HIV-1 subtype and HLA B-5701-allele are registered (online supplemental table 2). Pregnancies and following deliveries are also registered. Socioeconomic data such as civil status, education level or income is not included in InfCareHIV. ## Validation of cohort variables Validation of data is performed regularly on national level using a Data Quality Index, with the possibility to validate also on clinical and individual level. Data Quality Index for InfCareHIV is currently at 4.9 on a 0–5 scale. Data are also validated through the Distributed Data Management tool which enables data extraction for data sharing and automatically conducts quality assurance checks that signal any incorrect values that may then be manually corrected. This tool is derived and modified from a collaboration with EuroCoord, the European Network of HIV/AIDS Cohort studies to coordinate at European and International Level Clinical Research on HIV/AIDS.11 ## Ethics At time of entering HIV care, PLHIV are informed about the registry, after which they can either give verbal consent or opt out. Participants always have the right to exit InfCareHIV, although this has been very uncommon. Participants can request an extract on their data from the registry, free of charge, in accordance with the European General Data Protection Regulation (GDPR $\frac{2016}{679}$) and the Swedish Data Protection Act (2018:218). ## Patient and public involvement A representative from a Swedish organisation for PLHIV (www.hiv-sverige.se) is co-opted to the InfCareHIV steering committee. A web page (www.infcarehiv.se) contains information for both PLHIV, professionals involved in HIV care, researchers and the public. Statistics and treatment results of the individual clinical centres are published openly in an annual report and on the web page. ## Decision support system The mainstay of InfCareHIV is the clinical decision support system (figure 1). Data on HIV-RNA levels (plasma and CSF), CD4 cell counts (absolute and percentage), drug resistance and previous and current ART are presented through a graphical system. In addition, the legend below the graph presents data regarding date of HIV diagnosis, date of first ART and names of the HIV-team consisting of doctor, nurse and counsellor. Periods of pregnancy are also shown in the graph. All other information is easily accessible in the system. Also, since all viral (pol) sequences are stored in the database, reanalysis can easily be performed when new drugs or new information about resistance appear. Thus, data generated in clinical care are automatically ‘reused’ for research and development. The graph gives the clinician a relevant visual summary that is easier to grasp than the medical records, especially in treatment experienced PLHIV with a long history of different ART. Meanwhile the graph is also a pedagogical instrument during patient consultation, facilitating patient-centred care, making it easy to illustrate, for example, the consequences of good or poor treatment adherence. HRQoL is also illustrated in the graph (figure 2). Finally, the graph is used when transferring PLHIV between clinic centres, for expert-consultancy and at multidisciplinary team conferences. Figure 2: Clinical decision support system with results from self-reported health questionnaire below the graph. Red symbolises low ratings that need attention (very unsatisfied/unsatisfied), yellow medium ratings (rather unsatisfied/rather satisfied) and green contentment (satisfied/very satisfied), online supplemental table 3. Antal missade doser, missed doses of antiretroviral therapy; Biverkningar, side effects; Delaktig i planering, patient participation in planning of care; Kroppslig hälsa, physical health; Psykiskt välmående, psychological health; Rökning, smoking; Sexualitet, sexual health. ## Quality registry An important feature of InfCareHIV is to function as a National Quality Registry. The aim of a quality registry is to develop and ensure quality of care systematically and continuously. This allows for comparisons on HIV care at a national, regional and clinical centre level and has led to increased equality of care on national level with diminishing differences in treatment results in Sweden’s HIV centres. We believe it is most likely that this contributed to that Sweden in 2015 was the first country to reach the United Nations Programme on HIV/AIDS (UNAIDS)/WHO’s 90-90-90 goal with $90\%$ diagnosed, $90\%$ of them on ART and $90\%$ of them with viral suppression.10 In concordance with WHO/UNAIDS updated goal, we are now measuring the 95-95-95 goal and have also added 95 goals for HRQoL. ## Health-related quality of life Systematical quantification of patient-reported outcomes (PROs) will assist the improvement of medical care and HRQoL of PLHIV. In 2011, a 9-item self-reported Health Questionnaire (HQ) was integrated to the registry to be answered annually by PLHIV either via a website or using a computerised or paper version at the outpatient clinic (online supplemental table 3).12 The questionnaire was developed together with representatives from PLHIV patient organisations. The HQ assesses patient-reported outcome measures (PROMs) regarding physical, psychological and sexual health, self-reported ART adherence, experience of side effects and patient-reported experience measures regarding involvement and satisfaction with care. A question on smoking habits was added in 2017. The questionnaire is electronically available in Swedish and English and in paper version in the eight most common languages in the InfCareHIV cohort. Illiterate patients are offered help by a nurse/interpreter. The results are presented in the Decision support system (figure 2) and used at the patient’s routine clinical follow-up visit. The HQ enhances patient-centred HIV care by focusing the consultation on the patients current needs. The red signal symbolises low ratings (very unsatisfied/unsatisfied), that need attention, yellow medium ratings (rather unsatisfied/rather satisfied) and green contentment (satisfied/very satisfied), (online supplemental table 3). There is a standardised clinical manual for the health personnel with suggestions on how to manage yellow and red alerts (eg, screening for depression and asking about intimate partner violence if psychological well-being gets a yellow or red alert). The HQ is validated by content validity and test–retest reliability and has also been evaluated as a tool for longitudinal follow-up of trends in PROs.12–14 Our fourth 95 goal is that $95\%$ of all PLHIV should perform the health questionnaire and that $95\%$ of PLHIV should have an ART regimen without experience of side effects. The results from the HQ are also used in research, more on that below. HIV-related stigma and discrimination are major obstacles for reaching good HRQoL. In close relation to InfCareHIV we developed a 12-item HIV stigma scale, a short version of the commonly used 40-item HIV Stigma Scale by Berger et al.15 It is a valid and reliable instrument for the measurement of enacted, anticipated and internalised stigma and crucial for mapping trends in the prevalence of HIV-related stigma and tracking the effectiveness of stigma-reducing interventions.15 16 ## Biobank In the major clinical centres blood plasma/serum/liquor samples are collected, and in selected cases also peripheral blood mononuclear cells, HIV isolates, CSF and CSF cells in biobanks. These biobanks are separate from the cohort but linking between the cohort data and the biobanks can be performed (after ethical permission) for translational studies. ## Studies on late presentation In a national prospective study, using InfCareHIV linked to patient study forms, Brännström et al found late diagnosis of HIV infection to be a key problem in the Swedish HIV-1 epidemic, where more than half of the patients were diagnosed late, but the majority could have been diagnosed earlier with a more efficient healthcare system.6 17 18 Most of the patients experienced barriers to HIV testing, but less so if the HIV-test was offered through screening programmes or by a healthcare professional rather than having to be self-initiated.19 20 Results from the study have been adopted by the Public Health Agency of Sweden and have had implications for the Swedish governmental National strategy against HIV/AIDS and other infectious diseases.21 ## Studies on comorbidity and mortality Elvstam et al reported an association between low-level HIV viraemia while on ART and all-cause mortality,22 and has further explored the effects of detectable viraemia during ART by linking InfCareHIV to National Health registries and analysing stored biobank samples.23–27 Malmström et al studied risk of cancer by HIV status in Sweden for three decades, finding that PLHIV have a remaining increased risk of infection-related cancer despite ART, while lung cancer was the only non-infection-related cancer increased in PLHIV.28 By linking InfCareHIV to the National Cervical Cancer Screening registry, Carlander et al showed that PLHIV are less likely to have successful treatment of cervical precancer and more likely to have human papillomavirus (HPV) types not included in current HPV vaccines.29–32 These results were used when the national cervical-cancer-screening recommendation was updated in 2022.33 Bratt et al showed that the incidence of type 2 diabetes and insulin resistance is high in the ageing HIV population, where comorbidities are common and associated whereas there was no association found for HIV-related factors.34 Möller et al showed that well-treated PLHIV are not at higher odds of severe COVID-19 compared with HIV-negative people after controlling for age and comorbidity.35 ## Studies on HIV infection of the central nervous system Gisslén et al have used InfCareHIV in a substantial number of studies exploring CSF viral load and other biomarkers in different settings of untreated and treated HIV.36–54 Viral load is normally one log lower in CSF than in plasma in untreated HIV,55 but CSF exceeds plasma HIV RNA in approximately $15\%$ of patients, with considerable variations between different disease stages.36 Central nervous system (CNS) infection is generally well controlled by systemic suppressive ART,56 although in approximately $5\%$ the HIV RNA load was increased in the CSF despite suppression of the plasma viral load, a phenomenon referred to as ‘CSF viral escape’.41 This phenomenon is most often transient, not associated with any symptoms and comparable to plasma viral blips57 which occur in about the same frequency.58 Several studies have indicated that a stable, permanent infection of cells in the CNS is established later than in systemic viral reservoirs,40 which has implications when exploring HIV eradication strategies.59 ## Studies on elite controllers Sönnerborg et al have extensively studied, since the 1990s, long-term non-progressors and elite controllers (EC), selected from the InfCareHIV cohort. Specific immunological60 and metabolic features have been described.61 Also, a naturally occurring dipeptide was found to be enhanced among EC and to possess antiretroviral properties, acting as both an entry inhibitor and an RT-inhibitor.62 ## Studies on drug resistance mutations Sönnerborg et al have also studied HIV drug resistance, both transmitted and acquired, and the molecular HIV epidemiology. Among several key findings, an increase of transmitted drug resistance in the InfCareHIV cohort3 was reported. A pronounced HIV-1 subtype heterogeneity, including all known subtypes, many circulating recombinant forms and unique recombinant forms was also described.63 Sörstedt et al studied the effect of dolutegravir-based treatment in PLHIV with pre-existing NRTI-mutations and found a non-inferior effect compared with protease inhibitor-based regimens64 and has also explored viral blips during ART.58 ## Studies on patient-reported outcomes and experiences Svedhem et al validated the Health Questionnaire and demonstrated that self-reported adherence in the HQ was correlated to viral suppression and described determinants of optimal ART-adherence in the cohort.12 Svedhem et al have shown that the assessment of PROMs is an important tool to ensure the long-term adherence to treatment, improvement in quality of life and evaluate side effects on HIV treatment.12 14 PROMs have also successfully been used in virological molecular modelling showing that lower affinity for protease inhibitors to HIV-1C protease do not depend on lower adherence to treatment among people infected with subtype C.13 Tyrberg et al compared plasma drug levels of ART, potential drug–drug interactions and side-effects in PLHIV aged ≥65 years of age, with controls ≤49 years of age, and found differences in drug concentrations and reported side effects between groups.65 Mellgren et al demonstrated that the experience of side effects of ART declined significantly in the cohort during 2011–2017 and that experiences of side effects were diverse and associated with both self-reported physical and psychological health.14 Two studies on self-reported sexuality by Mellgren et al found that self-reported sexual satisfaction in PLHIV improved annually and that women were more satisfied with their sexual life compared with men.66 67 In women living with HIV, satisfaction with sexual life was associated with self-reported psychological health and experiences of side effects. Carlander et al showed that over the past 20 years access to employment has increased in PLHIV although remaining lower compared with HIV-negative, even after controlling for migrant and socioeconomic status.68 ## International collaborations InfCareHIV has collaborated or collaborates with several international HIV cohorts such as EuroCoord, EuroSIDA, RESPOND, PENTA, COHERE, CASCADE, CHAIN, NEAT, EuResist, EuCare, UCSF CSF Cohort and CARE. ## Future plans InfCareHIV provides a unique base to continue study long-term treatment, comorbidities and HRQoL of people with HIV in Sweden. Half of all PLHIV is still diagnosed late and studies that can help improve HIV-testing guidelines and HIV awareness are essential. Attempts are made to develop more precise bioinformatics tools for assessment of time of infection and the undiagnosed population. Pretreatment HIV drug resistance is a critical aspect that requires global collaboration and long-term follow-up. Despite most PLHIV reaching the treatment goal of undetectable viral level, patient reported health is still poorer than for HIV-negative people and studies on how to improve HRQoL in PLHIV and minimise stigma should be prioritised. With our ageing cohort, studies on comorbidity become more important and specially to assess what comorbidities are associated with normal ageing and what comorbidities may be associated with ART or HIV-induced chronic inflammation. Multiomics using samples and clinical information is presently evaluated to identify biomarkers for prediction of comorbidities, to characterise the evolution of CNS inflammation and injury45 and for future selection of patients in HIV cure attempts. ## Strengths and limitations The main strength of the InfCareHIV cohort is that it includes all diagnosed PLHIV in Sweden. The accuracy, completeness, consistency and validity is very high and consequently InfCareHIV has the highest certification level among Swedish Health Quality registries according to the Swedish Association of Local Authorities and Regions.69 The personal identity number given to all Swedish residents enables linkage of data to any Swedish population or health registry after appropriate application, providing opportunities to answer a wide range of research questions. Also, the automatic transfer of key laboratory data, including viral sequences, and the possibility of linking cohort data and biobanks allow easy reuse of information obtained in clinical care for research purposes. Some variables in InfCareHIV (eg, comorbidity, weight and self-reported smoking status that has been added in later years) lacks in coverage which currently limits their use in research but linking to other health registries for the collection of this data is then a possibility. The aim is for all PLHIV to be invited to answer the self-reported health questionnaire annually to facilitate patient centred care and improve HRQOL, although currently we reach only about $34\%$, and it is a prioritised matter to improve this number. ## Data availability statement Data are available upon reasonable request. Data can be made available upon reasonable request and after ethical approval. ## Patient consent for publication Not applicable. ## Ethics approval This cohort study has been approved by the Regional Ethical committees in Sweden (Dnr 532-11, Dnr $\frac{2018}{11}$-$\frac{31}{2}$, Dnr 2022-02897-02). ## References 1. Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. Lancet HIV (2017) 4 e349-56. DOI: 10.1016/S2352-3018(17)30066-8 2. de Coninck Z, Hussain-Alkhateeb L, Bratt G. 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European network of HIV/AIDS cohort studies to coordinate at european and international level clinical research on HIV/AIDS. (2015) 12. Marrone G, Mellgren Å, Eriksson LE. High concordance between self-reported adherence, treatment outcome and satisfaction with care using a nine-item health questionnaire in infcarehiv. PLoS One (2016) 11. DOI: 10.1371/journal.pone.0156916 13. Häggblom A, Svedhem V, Singh K. Virological failure in patients with HIV-1 subtype C receiving antiretroviral therapy: an analysis of a prospective national cohort in Sweden. Lancet HIV (2016) 3 e166-74. DOI: 10.1016/S2352-3018(16)00023-0 14. Mellgren Å, Eriksson LE, Reinius M. Longitudinal trends and determinants of patient-reported side effects on ART-a Swedish national registry study. PLoS One (2020) 15. DOI: 10.1371/journal.pone.0242710 15. Reinius M, Wettergren L, Wiklander M. Development of a 12-item short version of the HIV stigma scale. Health Qual Life Outcomes (2017) 15 115. DOI: 10.1186/s12955-017-0691-z 16. Reinius M, Wiklander M, Wettergren L. The relationship between stigma and health-related quality of life in people living with HIV who have full access to antiretroviral treatment: an assessment of earnshaw and chaudoir’s HIV stigma framework using empirical data. AIDS Behav (2018) 22 3795-806. DOI: 10.1007/s10461-018-2041-5 17. Brännström J, Svedhem V, Marrone G. Symptomatic patients without epidemiological indicators of HIV have a high risk of missed diagnosis: a multi-centre cross sectional study. PLoS One (2016) 11. DOI: 10.1371/journal.pone.0162503 18. Brännström J, Sönnerborg A, Svedhem V. A high rate of HIV-1 acquisition post immigration among migrants in Sweden determined by a CD4 T-cell decline trajectory model. HIV Med (2017) 18 677-84. DOI: 10.1111/hiv.12509 19. Wiklander M, Brännström J, Svedhem V. Development and psychometric testing of a barriers to HIV testing scale among individuals with HIV infection in Sweden; the barriers to HIV testing scale-karolinska version. Health Qual Life Outcomes (2015) 13 185. DOI: 10.1186/s12955-015-0381-7 20. George-Svahn L, Eriksson LE, Wiklander M. Barriers to HIV testing as reported by individuals newly diagnosed with HIV infection in Sweden. AIDS Care (2021) 33 1286-95. DOI: 10.1080/09540121.2020.1844862 21. National strategy against HIV/AIDS and certain other transmittable diseases. (2017) 22. Elvstam O, Marrone G, Medstrand P. All-cause mortality and serious non-AIDS events in adults with low-level human immunodeficiency virus viremia during combination antiretroviral therapy: results from a Swedish nationwide observational study. Clin Infect Dis (2021) 72 2079-86. DOI: 10.1093/cid/ciaa413 23. Elvstam O, Medstrand P, Yilmaz A. Virological failure and all-cause mortality in HIV-positive adults with low-level viremia during antiretroviral treatment. PLoS One (2017) 12. DOI: 10.1371/journal.pone.0180761 24. Elvstam O, Medstrand P, Jansson M. Is low-level HIV-1 viraemia associated with elevated levels of markers of immune activation, coagulation and cardiovascular disease?. HIV Med (2019) 20 571-80. DOI: 10.1111/hiv.12756 25. Elvstam O, Marrone G, Medstrand P. Associations between plasma human immunodeficiency virus (HIV) ribonucleic acid levels and incidence of invasive cancer in people with HIV after initiation of combination antiretroviral therapy. Open Forum Infect Dis (2021) 8. DOI: 10.1093/ofid/ofab131 26. Brattgård H, Björkman P, Nowak P. Factors associated with low-level viraemia in people with HIV starting antiretroviral therapy: a Swedish observational study. PLoS One (2022) 17. DOI: 10.1371/journal.pone.0268540 27. Elvstam O, Marrone G, Engström G. Associations between HIV viremia during antiretroviral therapy and cardiovascular disease. AIDS (2022) 36 1829-34. DOI: 10.1097/QAD.0000000000003240 28. Malmström S, Wagner P, Yilmaz A. Failure to restore CD4+ cell count associated with infection-related and noninfection-related cancer. AIDS (2022) 36 447-57. DOI: 10.1097/QAD.0000000000003117 29. Carlander C, Wagner P, van Beirs A. Suppressive antiretroviral therapy associates with effective treatment of high-grade cervical intraepithelial neoplasia. AIDS (2018) 32 1475-84. DOI: 10.1097/QAD.0000000000001853 30. Carlander C, Wagner P, Svedhem V. Impact of immunosuppression and region of birth on risk of cervical intraepithelial neoplasia among migrants living with HIV in Sweden. Int J Cancer (2016) 139 1471-9. DOI: 10.1002/ijc.30188 31. Carlander C, Lagheden C, Eklund C. HPV types in cervical precancer by HIV status and birth region: a population-based register study. Cancer Epidemiol Biomarkers Prev (2020) 29 2662-8. DOI: 10.1158/1055-9965.EPI-20-0969 32. Carlander C, Lagheden C, Eklund C. Nonvaccine human papillomavirus genotype common in women with HIV failing cervical precancer treatment. AIDS (2021) 35 2367-74. DOI: 10.1097/QAD.0000000000003026 33. Prevention of cervical cancer. (2022) 34. Bratt G, Brännström J, Missalidis C. Development of type 2 diabetes and insulin resistance in people with HIV infection: prevalence, incidence and associated factors. PLoS One (2021) 16. DOI: 10.1371/journal.pone.0254079 35. Möller I, Gisslén M, Wagner P. Clinical outcome in people with COVID-19 by HIV-status: a nationwide register study. CROI (2022) 36. Ulfhammer G, Edén A, Antinori A. Cerebrospinal fluid viral load across the spectrum of untreated human immunodeficiency virus type 1 (HIV-1) infection: a cross-sectional multicenter study. Clin Infect Dis (2022) 75 493-502. DOI: 10.1093/cid/ciab943 37. Hagberg L, Price RW, Zetterberg H. Herpes zoster in HIV-1 infection: the role of CSF pleocytosis in secondary CSF escape and discordance. PLoS One (2020) 15. DOI: 10.1371/journal.pone.0236162 38. Joseph SB, Kincer LP, Bowman NM. Human immunodeficiency virus type 1 RNA detected in the central nervous system (CNS) after years of suppressive antiretroviral therapy can originate from a replicating CNS reservoir or clonally expanded cells. Clin Infect Dis (2019) 69 1345-52. DOI: 10.1093/cid/ciy1066 39. Ulfhammer G, Edén A, Mellgren Å. Persistent central nervous system immune activation following more than 10 years of effective HIV antiretroviral treatment. AIDS (2018) 32 2171-8. DOI: 10.1097/QAD.0000000000001950 40. Burbelo PD, Price RW, Hagberg L. Anti-human immunodeficiency virus antibodies in the cerebrospinal fluid: evidence of early treatment impact on central nervous system reservoir?. J Infect Dis (2018) 217 1024-32. DOI: 10.1093/infdis/jix662 41. Edén A, Nilsson S, Hagberg L. 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--- title: Cocaine induces locomotor sensitization through a dopamine-dependent VTA-mPFC-FrA cortico-cortical pathway in male mice authors: - Lun Wang - Min Gao - Qinglong Wang - Liyuan Sun - Muhammad Younus - Sixing Ma - Can Liu - Li Shi - Yang Lu - Bo Zhou - Suhua Sun - Guoqing Chen - Jie Li - Quanfeng Zhang - Feipeng Zhu - Changhe Wang - Zhuan Zhou journal: Nature Communications year: 2023 pmcid: PMC10030897 doi: 10.1038/s41467-023-37045-3 license: CC BY 4.0 --- # Cocaine induces locomotor sensitization through a dopamine-dependent VTA-mPFC-FrA cortico-cortical pathway in male mice ## Abstract As a central part of the mammalian brain, the prefrontal cortex (PFC) has been implicated in regulating cocaine-induced behaviors including compulsive seeking and reinstatement. Although dysfunction of the PFC has been reported in animal and human users with chronic cocaine abuse, less is known about how the PFC is involved in cocaine-induced behaviors. By using two-photon Ca2+ imaging to simultaneously record tens of intact individual networking neurons in the frontal association cortex (FrA) in awake male mice, here we report that a systematic acute cocaine exposure decreased the FrA neural activity in mice, while the chemogenetic intervention blocked the cocaine-induced locomotor sensitization. The hypoactivity of FrA neurons was critically dependent on both dopamine transporters and dopamine transmission in the ventromedial PFC (vmPFC). Both dopamine D1R and D2R neurons in the vmPFC projected to and innervated FrA neurons, the manipulation of which changed the cocaine-induced hypoactivity of the FrA and locomotor sensitization. Together, this work demonstrates acute cocaine-induced hypoactivity of FrA neurons in awake mice, which defines a cortico-cortical projection bridging dopamine transmission and cocaine sensitization. The prefrontal cortex is involved in cocaine abuse disorders. Here, the authors show that cocaine suppresses frontal association cortex (FrA) in awake mice and induces locomotor sensitization through a dopamine dependent VTA-vmPFC-FrA pathway. ## Introduction As one of the most abused drugs, cocaine acts by blocking the dopamine transporter (DAT) in the brain to become stimulating, reinforcing, and addictive1–5. The altered brain synaptic transmission modulated by the increased mesolimbic dopamine is one of the essential mechanisms underlying cocaine-induced place preference and compulsive seeking behaviors, among which the dopaminergic projection from the ventral tegmental area (VTA) to the nucleus accumbens (NAc) has been well-established in mediating some of the cocaine-induced behaviors1,6,7. However, the knowledge of subcortical mesostriatal dopamine has not yet led to effective treatment of substance use disorders8. Thus, a further investigation of brain circuit mechanisms underlying substance use disorders is required. The prefrontal cortex (PFC), especially the medial PFC (mPFC), has been reported to be abnormal in cocaine abusers, including human beings and rodents9–11. Anatomically, the PFC is subdivided into a number of subregions, including the frontal association cortex (FrA) and the mPFC, while the mPFC can be further dissociated into the anterior cingulate cortex, the prelimbic cortex (PrL), the infralimbic cortex (IL), and the dorsal peduncular cortex (DP). Some of the mPFC subregions, such as the PrL and IL, are functionally involved in the regulation of cocaine-seeking behaviors. Furthermore, optogenetic manipulations of mPFC neurons can enhance or impair the performance of compulsive cocaine-seeking in rodents9,10,12–14. The mPFC has also been reported, at the behavior level, to be related to cocaine-induced reinstatement and locomotor sensitization15,16. Although dopamine suppression of the mPFC has been proposed to mediate cocaine effect17–21, the responses of mPFC neurons to VTA stimulations are manifold22,23 and changes of a specific subtype of mPFC neurons remain largely unknown, especially in the awake mice with intact circuits of various spatial-temporal inputs, modulators, and different levels of network activity24,25. Mechanistically, D1 and D2 dopamine receptors are thought to be excitatory and inhibitory, respectively24–28, however, there are also opposite reports that D1R is inhibitory and D2R is excitatory in a subset of cortical pyramidal neurons29,30. Thus, the specific responses of different subtypes of mPFC neurons depend on the firing pattern of dopaminergic terminals, local interaction with inhibitory interneurons, extracellular dopamine concentration, and D1R/D2R binding affinity24,31. These studies provide profound evidence for the involvement of the mPFC in cocaine-induced behaviors; however, both phenotypes and the underlying mechanisms remain elusive24,25. The FrA is located in the anterior dorsal lateral part of the PFC, receiving inputs from several regions like the agranular insular cortex, the anterior part of the basolateral amygdaloid nucleus, and the perirhinal cortex32–34, and projecting to midbrain dopamine neurons (including the VTA and the substantia nigra pars compacta, SNc) in a greater number and density of neurons than all mPFC subregions35. These anatomical structures suggest that the FrA is a potentially important region involved in dopamine-related functions. Recent studies indeed have reported critical roles of the FrA in anxiety and fear memory;32–34 however, whether and how this region is functionally involved in cocaine-related mechanisms remain unclear. To address how and to what extent the multiple PFC subregions of an awake mouse are affected by acute cocaine, which targets neural signaling via DAT, norepinephrine transporter (NET), or serotonin transporter36, a more thorough investigation of specific PFC subregions in cocaine exposure at the single-cell level is required. Especially, dopamine transmission modulates cortical neurons in a manifold yet mysterious manner according to various studies in slices, organotypic cultures, and anesthetized or freely-moving animals22–24,37–39. Decoding how cocaine affects PFC neurons from the very beginning with single-neuron recordings in the awake brain would shed new light on both the mesocortical signaling pathway and the initiating mechanisms of acute cocaine exposure that lead to subsequent neural adaptations and drug abuse disorders. In the present work, by using two-photon Ca2+ imaging to visualize neuronal activity in the FrA in awake mice at the resolution of single-cell-recordings and (up to) single-action potentials32,40,41, we found that acute cocaine induced substantial hypoactivity of the FrA and this change was necessary for cocaine-induced locomotor sensitization. Importantly, we further identified a vmPFC–FrA pathway that mediates the cocaine-induced hypoactivity and locomotor sensitization in response to the altered dopamine transmission, providing a cortico-cortical circuit mechanism of cocaine in awake mice. ## Acute cocaine induces hypoactivity in the FrA To investigate whether acute cocaine administration affects PFC neurons, we used two-photon Ca2+ imaging to record individual neuronal activity in layers II/III of the FrA in head-fixed awake mice (Fig. 1a). The spontaneous somatic Ca2+ signals (SSCSs) were acquired with the Ca2+ indicator Cal-520 (Fig. 1b) to measure neuronal population activity at single-action-potential-sensitivity32,40. Compared to the control group (intraperitoneal, i.p. saline), the percentage of silent cells increased from 4.8 to $30.5\%$ and the averaged SSCS frequency decreased by ~$53\%$ within 10–20 min after cocaine injection (10 mg/kg, Fig. 1c–f). In some of these mice, we made longer time-elapse recordings and found that the inhibitory effect of cocaine on the FrA was reversible within 40–50 min after cocaine injection (Fig. S1b), and the SSCS frequency in the control group was stable until at least 50 min after saline injection (Fig. S1a). To further determine whether the excitation change mainly occurs in excitatory or inhibitory neurons, we next applied Ca2+ imaging by virally expressing genetically-encoded GCaMP6s. Using adeno-associated virus (AAV) with CaMKIIα-GCaMP6s (Fig. 1g), we confirmed that acute cocaine administration inhibited the SSCS frequency of excitatory neurons by ~$51\%$ (Fig. 1h and S2a). However, when the inhibitory neurons were visualized by injecting the Cre-dependent GCaMP6s-expressing virus into the FrA of vGAT-Cre transgenic mice (Fig. 1i), we did not observe a significant change after cocaine injection (Fig. 1j and S2b). Thus, our results demonstrated that acute cocaine exposure had a pronounced hypoactive effect, specifically on excitatory (but not inhibitory) neurons in the FrA.Fig. 1Acute cocaine-induced hypoactivity of the FrA is necessary for locomotor sensitization.a, b Schematic and representative micrograph showing two-photon Ca2+ imaging of Cal-520-labeled neurons in the FrA of head-fixed awake mice. Scale bars: 1 mm (a) and 50 µm (b). c Representative traces of spontaneous somatic Ca2+ signals (SSCS) before (basal) and after i.p. injection of saline (sal) or cocaine (coc, 10 mg/kg). Scale bars, 20 s, $200\%$ dF/F0. d Percentage of silent versus active cells after saline or cocaine injection. e, f Cumulative distribution and box-and-whisker plot (minimum, maximum, and three quartiles) of SSCS frequency under different conditions. Sal, $$n = 598$$ neurons from seven mice ($$p \leq 0.28$$); coc, $$n = 591$$ neurons from seven mice. g, h Representative micrograph and statistics (minimum, maximum, and three quartiles) of SSCS frequency of excitatory neurons in the FrA before and after i.p. saline or cocaine. Scale bar, 50 µm. Sal, $$n = 185$$ neurons from four mice ($$p \leq 0.63$$); coc, $$n = 227$$ neurons from five mice. i, j Representative micrograph and statistics (minimum, maximum, and three quartiles) of SSCS frequency of GABAergic neurons in the FrA before and after saline or cocaine i.p. injection. Scale bar, 50 µm. Sal, $$n = 35$$ neurons from four mice ($$p \leq 0.16$$); coc, $$n = 37$$ neurons from four mice ($$p \leq 0.21$$). k Schematic of virus injection and representative micrograph showing the expression of hM3Dq-mCherry in excitatory FrA neurons; scale bar, 200 µm. l Timeline of behavioral tests. m Group data (mean ± s.e.m.) for CPP score. $$n = 11$$ (AAV-mCherry, sal), 12, 12, 12 mice. n Statistics (mean ± s.e.m.) of distance traveled for locomotor sensitization measurement. $$n = 11$$ (AAV-mCherry, sal), 12, 12, 12 mice. Two-tailed Wilcoxon test for (e–j), Ordinary two-way ANOVA followed by Bonferroni’s multiple comparisons for (m and between-group comparisons in n), One-way ANOVA followed by Dunnett’s multiple comparisons for (in-group comparisons in n). P values of (m, n) were summarized in Supplementary Table 1. n.s. not significant; $p \leq 0.05$; $p \leq 0.01$; *$p \leq 0.001.$ Source data* are provided as a Source Data file. To test whether this acute cocaine-induced hypoactivity in the FrA is critical for cocaine-induced behaviors, we combined a chemogenetic activation approach with the behavioral tests of conditioned place preference (CPP) and locomotor sensitization, to investigate the rescue effect of FrA manipulation on reward association memory and possibly sensitization of incentive motivation42–44. CaMKIIα-hM3Dq-mCherry expressing virus was bilaterally injected into the FrA to express hM3Dq in excitatory neurons (Fig. 1k). Two-photon imaging (Fig. S3a–c) and whole-cell patch-clamp electrophysiology (Fig. S3d) confirmed that activation of hM3Dq with clozapine-N-oxide (CNO) is capable of activating FrA excitatory neurons in vivo and ex vivo. The results showed that chemogenetic activation of excitatory neurons successfully prevented cocaine-induced hypoactivity in the FrA of awake mice (Fig. S3c). Then, CPP was assessed following daily pairing with cocaine (10 mg/kg, i.p., saline for control groups) on days 1–3, during which locomotor sensitization was recorded (Fig. 1l). Chronic Ca2+ imaging confirmed that cocaine did inhibit FrA excitatory neurons on the 3 consecutive training days (Fig. S4); thus CNO was injected (i.p.) 30 min before each cocaine-pairing session to test the effect of FrA excitation on cocaine-induced sensitization. The CPP score in the chemogenetic activation group (hM3Dq virus-injected mice) was similar to that of control virus-injected mice (Fig. 1m and S5a), suggesting that disruption of the cocaine-induced hypoactivity in the FrA does not prevent cocaine-induced CPP, and implying that FrA excitatory neurons are irrelevant to the cocaine-induced CPP or aversion. Strikingly, control mice showed clear locomotor sensitization to repeated cocaine exposure, and this was completely abolished in AAV-hM3Dq mice (Fig. 1n). As a control, both the basal locomotor activity (saline groups) and the cocaine-induced hyperactivity in day 1 remained unchanged in the hM3Dq group (Fig. 1n). Moreover, we applied the same behavioral assays to intact C57BL/6 J mice and excluded a possible role of CNO per se in regulating locomotor sensitization (Fig. S5b, c). On the other hand, we tested whether inhibitory DREADDs (hM4Di) promote subthreshold cocaine-induced locomotor sensitization. We first examined the cocaine dose-dependent behaviors and selected 2.5 mg/kg as the subthreshold dose (Fig. S6a), which is only able to induce significant locomotor distance sensitization on day 3 but not on day 2. Then we followed the same protocol but used hM4Di instead of hM3Dq to investigate the role of FrA inhibition in the cocaine effect. The data show that locomotor sensitization of hM4Di-coc mice were indeed facilitated compared with the control group mCherry-coc (Fig. S6b2), leaving the CCP score unchanged (Fig. S6b1). The locomotor distance of hM4Di-coc mice was significantly increased on day 1 to a similar level as day 3 and thus didn’t express further sensitization across the 3 days examination (Fig. S6b3). Thus, chemogenetic inhibition of FrA neurons produced enhanced locomotor hyperactivity and thus, no further facilitation was observed. Collectively, these results demonstrated that acute cocaine-induced hypoactivity of the FrA is necessary for locomotor sensitization. ## Cocaine-induced hypoactivity of the FrA is dependent on DAT Although the dopamine system has been proposed to be the initiating target of cocaine to affect locomotion, reward, and compulsive seeking1–5, it is necessary, without bias, to identify the underlying target(s) of cocaine with regard to the FrA, which is a newly-identified area involved in cocaine action. As a nonselective inhibitor of monoamine transporters, cocaine inhibits all three kinds of monoamine transporter to a similar extent, including DAT, SERT, and NET36,45. We first injected (i.p.) relatively selective inhibitors of different transporters to determine which type of monoamine transporter is functionally involved in the cocaine-induced hypoactivity of the FrA. Interestingly, both nomifensine32 and GBR-1278346, specific DAT blockers, showed robust inhibition of FrA excitatory neurons, while desipramine (NET blocker)47 and fluoxetine (SERT blocker)48 did not (Fig. 2a–c), implying functional involvement of the dopamine system in the modulation of FrA activity. Importantly, we tested the acute cocaine effect on DAT knockout (DAT-KO)49 and cocaine-insensitive DAT (DAT-CI)3 transgenic mice and found that cocaine failed to inhibit FrA activity in either transgenic line (Fig. 2d–f), indicating critical roles of DAT in cocaine-induced FrA hypoactivity. In addition, the inhibitory effect of cocaine on FrA neurons disappeared when VTA dopaminergic neurons were depleted by the stereotactic injection of the neurotoxin 6-hydroxydopamine (6-OHDA, Fig. 2g–i). In contrast, mice treated with MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, i.p. daily for 5 days) or 6-OHDA (injected into SNc) showed substantial dopamine loss in the SNc and the caudate-putamen (CPu, dorsal striatum) while the cocaine-induced FrA hypoactivity remained unchanged (Fig. S7). Together, these results suggested that VTA dopaminergic neurons play essential roles in cocaine-induced hypoactivity in the FrA.Fig. 2Dopamine transporter (DAT) mediates cocaine-induced hypoactivity of the FrA.a–c Representative traces and normalized frequency (minimum, maximum, and three quartiles) of SSCS of excitatory neurons in the FrA after i.p. cocaine, nomifensine (nomi, 8 mg/kg, a blocker of DAT and NET), GBR-12783 (GBR, 10 mg/kg, selective DAT blocker), desipramine (desip, 35 mg/kg, selective NET blocker), or fluoxetine (fluox, 10 mg/kg, selective SERT blocker). AAV-CaMKIIα-GCaMP6s was used to label excitatory neurons in FrA layers II/III. coc, $$n = 285$$ neurons from five mice; nomi, $$n = 145$$ neurons from three mice; GBR, $$n = 201$$ neurons from four mice; desip, $$n = 208$$ neurons from four mice ($$p \leq 0.064$$); fluox, $$n = 281$$ neurons from five mice ($$p \leq 0.23$$). d–f Representative traces and normalized frequency (minimum, maximum, and three quartiles) of SSCS in the FrA after i.p. cocaine. The Ca2+ indicator Cal-520 was used for Ca2+ imaging in DAT-KO and DAT-Cre mice. AAV-CaMKIIα-GCaMP6s was used in DAT-CI and DAT-WT mice. DAT-Cre, $$n = 173$$ neurons from four mice; DAT-KO, $$n = 202$$ neurons from five mice ($$p \leq 0.12$$); DAT-WT, $$n = 181$$ neurons from four mice; DAT-CI, $$n = 282$$ neurons from five mice ($$p \leq 0.28$$). g Confirmation by TH-immunofluorescence (red) that VTA dopamine neurons are lesioned by stereotaxic microinjection of 6-OHDA. Representative micrographs are from the same region of sham- and 6-OHDA-treated mice and aligned symmetrically by the midline, $$n = 4$$, 4 mice. Scale bar, 200 µm. Cal-520 was used for imaging in the FrA. h, i Representative traces and normalized SSCS frequency (minimum, maximum, and three quartiles) in FrA neurons of sham- and 6-OHDA-treated mice before and after i.p. cocaine. Sham, $$n = 151$$ neurons from four mice; 6-OHDA, $$n = 171$$ neurons from four mice ($$p \leq 0.27$$). Two-tailed Wilcoxon test for (c, f, and paired comparisons in i) and two-tailed Mann–Whitney test for (unpaired comparisons in i). * $p \leq 0.001.$ Scale bars for traces, 20 s, $200\%$ dF/F0. Source data are provided as a Source Data file. ## Cocaine-induced FrA hypoactivity is not dependent on the direct dopaminergic projection To further investigate whether the direct dopaminergic projection contributes to cocaine’s effect on the FrA, we used local microinjection of 6-OHDA to deplete dopaminergic terminals in this region. Tyrosine hydroxylase (TH) staining (Fig. 3a and S8a, b) showed that the depletion was restricted to the FrA without obvious damage to other dopamine-projecting regions, including the vmPFC, NAc, CPu, or basolateral amygdaloid nucleus, anterior part (BLA) (Fig. S8c). Surprisingly, we found that cocaine still evoked a pronounced inhibition in the FrA similar to the sham group (Fig. 3a–c), indicating that the direct dopaminergic projection is not responsible for the inhibitory effect of cocaine on FrA activity. Fig. 3Dopaminergic projection to the FrA is not necessary for cocaine-induced hypoactivity of FrA neurons.a Upper, schematic of 6-OHDA injection into the FrA. Lower, TH-staining (red) showing the depletion of dopaminergic terminals in the FrA through 6-OHDA microinjection. Pyramidal neurons in the FrA are labeled with AAV-CaMKIIα-GCaMP6s. Scale bar, 50 µm. b, c Representative traces and normalized SSCS frequency (minimum, maximum, and three quartiles) of FrA pyramidal neurons in 6-OHDA-lesioned mice. Scale bars, 20 s, $300\%$ dF/F0. Sham, $$n = 301$$ neurons from 6 mice; 6-OHDA, $$n = 218$$ neurons from five mice, $$p \leq 0.35$$ for coc (sham vs 6-OHDA), two-tailed Wilcoxon test for paired and Mann–Whitney test for unpaired comparisons, $p \leq 0.001.$ d Left, schematic of AAV-Retro-hSyn-EYFP injection into the FrA for the retrograde labeling of FrA-projecting neurons. Right, representative micrographs of four regions with retrogradely-labeled somata (scale bars, 1 mm for FrA, 100 µm for projecting regions). AI agranular insular cortex, vmPFC ventromedial prefrontal cortex, BLA basolateral amygdaloid nucleus, anterior part, PRh perirhinal cortex. $$n = 5$$ mice. Source data are provided as a Source Data file. To investigate which dopaminergic projection is responsible for the acute cocaine-induced hypoactivity in the FrA, we next screened the areas upstream of the FrA, especially those with well-known dopaminergic innervation, taking a retrograde tracing approach. The hSyn-EYFP retrograde AAV was injected into the FrA and images of consecutive sections of the whole brain showed densely-localized YFP-positive somata in several upstream areas, including the agranular insular cortex, BLA, and perirhinal cortex (Fig. 3d), consistent with previous reports33,34. Besides, we also found the vmPFC, including the IL and DP, to be positive with a relatively dense expression of YFP (Fig. 3d and S9), which had not been reported. Among them, the BLA and mPFC have been reported to be regulated by dopamine23,50. ## Dopaminergic projections to the vmPFC mediate the effect of cocaine on the FrA Next, we used a similar approach to investigate whether the BLA or vmPFC dopamine projection is necessary for the inhibitory effect of cocaine on the FrA. However, bilateral injection of 6-OHDA into the BLA (Fig. S10a) had no effect on the cocaine-induced FrA hypoactivity (Fig. S10b, c). In addition, in vivo two-photon imaging showed that cannula-guided micro-infusion of cocaine into the BLA failed to modulate neural activity in the FrA either (Fig. S10d, e). Thus, the BLA dopaminergic projection may be irrelevant to the effect of cocaine on the FrA. On the contrary, the depletion of dopaminergic terminals in the vmPFC by microinjection of 6-OHDA notably blocked the cocaine-induced reduction in SSCS frequency (Fig. 4a–c). TH-staining showed no obvious damage to dopaminergic terminals in other regions, including the FrA, NAc, CPu, or BLA (Fig. S11). Consistently, in vivo electrochemical amperometry recording with carbon fiber electrodes (Fig. 4d) showed that electrical stimulation-induced dopamine overflow in the vmPFC was increased in amplitude and decreased in uptake rate after cocaine administration (Fig. 4e, f). Importantly, micro-infusion of cocaine or dopamine into the vmPFC also induced hypoactivity of FrA neurons to an extent similar to that of i.p. cocaine injection (Fig. 4g–i). These results suggested that the vmPFC mediates the inhibition of FrA activity by cocaine. Fig. 4Dopaminergic projection to the vmPFC is necessary for cocaine-induced hypoactivity of the FrA.a Upper, schematic of 6-OHDA injection into the vmPFC. Lower, TH-staining (red) showing the depletion of dopaminergic terminals in the vmPFC through 6-OHDA microinjection. Cal-520 was used for Ca2+ imaging in the FrA. Scale bar, 100 µm. b, c Representative traces (scale bars: 20 s, $200\%$ dF/F0) and normalized SSCS frequency (minimum, maximum, and three quartiles) of FrA neurons as in (a). Sham, $$n = 406$$ neurons from seven mice; 6-OHDA, $$n = 397$$ neurons from seven mice ($$p \leq 0.77$$), two-tailed Wilcoxon test for paired and two-tailed Mann–Whitney test for unpaired comparison. d In vivo amperometric CFE (carbon fiber electrode) recording of dopamine (DA) release in the vmPFC in response to electrical stimulation (E-stim, 1 ms, 80 pulses at 80 Hz) in the MFB (medial forebrain bundle). e, f Representative amperometric traces (Iamp, scale bars: 2 s, 30 pA) and statistics (mean ± s.e.m.) of vmPFC DA overflow amplitude and half height duration (HHD) in response to electrical stimulation applied before and after intraperitoneal injection of saline or cocaine (10 mg/kg). Saline, $$n = 5$$ mice ($$p \leq 0.25$$ for amplitude, 0.88 for HHD); cocaine, $$n = 6$$ mice ($$p \leq 0.017$$ for amplitude, 0.0019 for HHD), paired two-tailed Student’s t-test. g, h Schematic of two-photon Ca2+ imaging and statistics of normalized SSCS frequency (minimum, maximum, and three quartiles) of the FrA in response to microinfusion (inf.). Sal, $$n = 191$$ neurons from four mice ($$p \leq 0.59$$); coc, $$n = 204$$ neurons from four mice; DA, $$n = 127$$ neurons from three mice, two-tailed Wilcoxon test. i Confirmation of cannula tip location and drug diffusion through the microinfusion of Chicago sky blue. Scale bar, 1 mm. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001.$ Source data* are provided as a Source Data file. ## vmPFC–D1R and –D2R neurons mediate cocaine-induced hypoactivity of the FrA and locomotor sensitization To investigate the vmPFC–FrA projections, the anterograde trans-monosynaptic tracing virus (AAV1-hSyn-Cre) was injected into the vmPFC to identify the postsynaptic neurons in the FrA (Fig. 5a–e). We found the majority of labeled neurons located in layers II/III of the FrA (mainly at a depth of 100–200 µm) and there was no anti-GAD67 fluorescence staining in these cells, suggesting a limited number of postsynaptic GABAergic neurons (Fig. 5d). Then, a retrograde AAV virus expressing YFP driven by CaMKIIα (AAV-Retro-CaMKIIα-EYFP) or vGAT (AAV-Retro-vGAT-EYFP) promotor was injected into the FrA of D1/D2-Cre and Ai9-crossed mice, in which the D1R- or D2R-positive neurons were labeled with tdTomato (Fig. 5f and S12a, b). We found that ~$28\%$ of the FrA-projecting excitatory vmPFC neurons and ~$17\%$ of the FrA-projecting inhibitory vmPFC neurons were D1R-positive, and ~$25\%$ excitatory and ~$30\%$ inhibitory neurons were D2R-positive (Fig. 5g and S12c). Averagely, among those FrA-projecting D1R and D2R neurons, about $81\%$ were excitatory and $19\%$ inhibitory for D1R, $68\%$ excitatory, and $32\%$ inhibitory for D2R (Fig. S12d).Fig. 5vmPFC–D1R and -D2R neurons synapse to the FrA.a Schematic of anterograde trans-monosynaptic virus (AAV1-hSyn-Cre) injection into the vmPFC (IL and DP) of Ai9 mice with Cre-dependent tdTomato expression. b Representative micrograph showing tdTomato expression in the vmPFC, repeated in three mice. Scale bar, 500 µm. c, d Representative micrograph of anterogradely-labeled neurons (red) in the FrA with immunofluorescence for GAD67 (green). The boxed region is enlarged in (d). Scale bars, 200 µm for (c), 100 µm for (d). e Statistics (mean ± s.e.m.) of anterogradely-labeled neurons in different layers of the FrA. $$n = 6$$ slices from three mice. Comparisons between layers II/III and layers I or V/VI were analyzed with one-way ANOVA followed by Dunnett’s multiple comparisons: F(1.606, 8.032) = 201.2, $p \leq 0.001$; multiple comparisons: $p \leq 0.0001.$ f, g Schematic and representative micrographs showing retrograde virus injection into the FrA of D1/D2-Cre and Ai9-crossed mice. White arrows indicate the retrograde virus-labeled D1R- or D2R-positive (red) glutamatergic or GABAergic neurons in the vmPFC (IL and DP, coronal slices). Scale bar, 50 µm. h Schematic showing the recording of light-evoked EPSCs and IPSCs in layers II/III of acute FrA slices. ChR2 is expressed through stereotaxic injection of AAV-Flex-ChR2-EYFP into the vmPFC of D1-Cre or D2-Cre mice. i, j Representative traces and statistics (minimum, maximum, and three quartiles) of light-evoked PSCs ($$n = 14$$, 14, 11, 12 cells from 5 D1-Cre mice and 3 D2-Cre mice) in the FrA. Scale bars, 50 ms, 100 pA. Two-way ANOVA followed by Tukey’s multiple comparisons test: D1/D2 effect, F[1, 47] = 0.69, $$p \leq 0.41$$; EPSC/IPSC effect, F[1, 47] = 0.006, $$p \leq 0.94$$; multiple comparison tests were all n.s. with $p \leq 0.9.$ k Percentages of FrA neurons with IPSCs, EPSCs, or both. D1, $$n = 60$$ cells from six mice (33 PSC+ cells, 27 PSC– cells); D2, $$n = 75$$ cells from seven mice (25 PSC+ cells, 50 PSC– cells). Only 20 of the 33 PSC+ cells and 14 of the 25 PSC+ cells were tested for both EPSCs and IPSCs. Source data are provided as a Source Data file. Next, we combined optogenetic stimulation with a patch-clamp recording of synaptic currents to identify the functional synaptic connections between the vmPFC and the FrA (Fig. 5h). After the ChR2-expressing virus was injected into the vmPFC of D1- or D2-Cre mice, light-pulse stimulation (473 nm, 1 ms) not only evoked excitatory but also inhibitory postsynaptic currents (EPSCs/IPSCs) in layers II/III of the FrA (Fig. 5i, j), which was consistent with the results of retrograde tracing assays (Fig. 5g). Notably, over half ($55\%$) of the recorded FrA layer II/III neurons received vmPFC–D1R innervation ($33\%$ for D2), and ~$50\%$ ($71\%$ for D2) of those positive neurons received both EPSC and IPSC inputs, ~$20\%$ ($21\%$ for D2) EPSCs and ~$30\%$ ($7\%$ for D2) IPSCs (Fig. 5k). These structural and functional evidence demonstrated that the synaptic innervation from the vmPFC to the FrA is manifold and related to D1R-/D2R-expressing (vmPFCD1R/D2R) neurons. To investigate the roles of vmPFCD1R/D2R neurons in the cocaine-induced hypoactivity of the FrA in awake mice, we specifically manipulated the vmPFCD1R/D2R neurons by chemogenetic tools (Fig. 6a) and by micro-infusion of the selective D1R/D2R agonist or antagonist into the vmPFC (Fig. 6c). The results of single-neuron SSCSs showed that both excitatory manipulations of vmPFCD1R and vmPFCD2R neurons by hM3Dq produced significant depression of the FrA, by ~37 and ~$19\%$, respectively (Fig. 6b). As a contrast, the inhibitory hM4Di failed to produce significant perturbation to the FrA activity in both D1 and D2 neurons (Fig. 6b). We also found that the cocaine-induced hypoactivity of the FrA was enhanced by SKF (SKF-38393, D1R agonist) and QP (quinpirole, D2R agonist), abolished by SCH (SCH-23390, D1R antagonist), and changed by sulpiride (D2R antagonist; Fig. 6d). These findings demonstrate that D1 and D2 receptors play essential and different roles by bridging the dopamine transmission from the VTA–vmPFC projection and the vmPFC innervation of FrA excitatory neurons. Furthermore, both the antagonists of D1R and D2R micro-infused into the vmPFC were capable of blocking cocaine-induced locomotor sensitization (Fig. 6e). These results demonstrate a cortico-cortical pathway by which cocaine affects the FrA and locomotor sensitization in a dopamine- and D1/D2 receptor-dependent manner. Fig. 6vmPFC modulates FrA and locomotor sensitization through dopamine D1 and D2 receptors.a, b Schematic of two-photon Ca2+ imaging and statistics (minimum, maximum, and three quartiles) of normalized SSCS frequency in the FrA with virus injection (AAV-Dio-hM3Dq/hM4Di/mCherry) into the vmPFC of D1-Cre or D2-Cre mice. Cocaine (i.p.) was injected 30 min after CNO (i.p.) injection. Data for CNO were recorded 20–30 min after injection and cocaine were 10–20 min. D1-mChe, $$n = 225$$ neurons from three mice; D1-hM3Dq, $$n = 296$$ neurons from four mice; D1-hM4Di, $$n = 333$$ neurons from four mice; D2-hM3Dq, $$n = 245$$ neurons from three mice; D2-hM4Di, $$n = 255$$ neurons from three mice, two-tailed Wilcoxon test. c, d Schematic of two-photon Ca2+ imaging and statistics (minimum, maximum, and three quartiles) of normalized SSCS frequency in the FrA following microinfusion of a D1R agonist (SKF-38393, SKF) or antagonist (SCH-23390, SCH) or a D2R agonist (quinpirole, QP) or antagonist (sulpiride, sulp) into the vmPFC following i.p. cocaine in awake mice. Sal, $$n = 191$$ neurons from four mice; SKF, $$n = 212$$ neurons from four mice; SCH, $$n = 205$$ neurons from four mice; QP, $$n = 280$$ neurons from five mice; sulp, $$n = 149$$ neurons from four mice. Two-tailed Wilcoxon test for paired comparisons, two-tailed Mann–Whitney test for unpaired comparisons. e CPP scores (e1, shown as preference during pre- and posttest) and locomotor distance for sensitization measurement (e2) under bilaterally microinfusion of D1R or D2R antagonist following i.p. cocaine. Data were presented as mean ± s.e.m. CPP scores, $$n = 8$$ mice per group; paired t-test between pre- and posttest and unpaired t-test between posttests. Locomotor sensitization, $$n = 7$$, 7, and 8 mice; in-group comparisons (between different training days) were analyzed with one-way ANOVA followed by Dunnett’s multiple comparisons; multiple comparisons were performed for each group between day 2 or day 3 and day 1; unpaired two-tailed t-test for comparisons between SCH/sulp and sal on day 3. P values were summarized in Supplementary Table 2. * $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001.$ Source data* are provided as a Source Data file. ## Discussion Previous studies have reported the prefrontal neuronal plastic changes induced by chronic cocaine use1,9–12, but little is known about how the PFC responds to acute cocaine exposure and what the response means in awake mice. In the present work, we have identified the FrA, which is inhibited by acute cocaine exposure through the vmPFC–FrA pathway in a dopamine-dependent manner, as a critical mediator of locomotor sensitization (Fig. 7), providing a cortico-cortical pathway mediating cocaine sensitization in awake mice. Fig. 7Cartoon illustration that cocaine induces hypoactivity of the FrA and locomotor sensitization through the dopamine-dependent vmPFC–FrA pathway. The working model is that acute cocaine exposure (10 mg/kg, i.p.) decreases FrA neuronal activity (SSCS), which is critical for cocaine-induced locomotor sensitization, through a dopaminergic VTA–vmPFC pathway relayed by a vmPFC–FrA cortico-cortical projection: Cocaine (i.p.) → VTA-mPFC DA transmission → mPFC NEUROND1/D2 → FrAcircuit → Behaviors. The first major finding of our study is that the FrA hypoactivity is responsible for cocaine-induced locomotor sensitization. Although the decreased activity or dysfunction of the PFC (hypofrontality) has been reported in animal10,51 and human11,52 abusers after chronic cocaine use or acute administration21,53, what specific subregion is responsible for the cocaine effect remains obscure. With single-neuron-resolution of two-photon Ca2+ imaging in awake mice, we show that most FrA neurons were reversibly inhibited by acute cocaine exposure (i.p.) and generated overall hypoactivity at the population level (Fig. 1 and S1). This inhibition was recorded only in excitatory but not inhibitory neurons in the FrA (Fig. 1g–j and S2). By combining two-photon imaging with pharmacological (antagonists of NET, SERT, and DAT) and genetic (DAT-KO and DAT-CI mice) tools, we further identified DAT as the major mediator of cocaine-induced hypoactivity in the FrA (Fig. 2). This is consistent with that most of the important psychological effects of cocaine are related to DAT and the dopamine system1–3,5,6,22. With chemogenetic manipulation, the FrA showed specificity in regulating sensitization without affecting the normal locomotion or rewarding effect (Fig. 1k–n and S3–S5). These findings not only suggest the close association of the FrA with locomotor sensitization, a measurement associated with drug-induced plasticity and maybe incentive motivation42–44, but also define this cortical region as a therapeutic target to specifically reverse cocaine sensitization. Because locomotor sensitization is associated with cocaine-induced neural plasticity and craving, which may lead to dependence and loss of self-control42,44, future work is deserved to examine possible roles of the FrA using a behavior paradigm like self-administration in abstinence, compulsive drug-seeking, and relapse, to achieve a comprehensive understanding of the FrA in cocaine-abuse disorders. The second finding is that the dopamine dependence was not through the direct dopamine innervation to the FrA. Instead, dopamine indirectly modulates the FrA activity through VTA projections to the vmPFC that synapses to FrA layer II/III neurons (Figs. 3–5 and S8–S12). As a control, dopaminergic projections to the BLA were not involved in the effect of cocaine on the FrA (Fig. S10), implying relative specificity of the VTA–vmPFC–FrA circuit in mediating cocaine-induced hypoactivity and locomotor sensitization. This dopamine-dependent cortico-cortical pathway not only provides an example to understand how the impact of cocaine reaches distributed regions in complicated neuronal networks but also sheds new light on how cortical circuits may contribute to dopamine modulation in the physiological or pathological brain. The third finding is the necessity of vmPFC–D1R and –D2R signaling in mediating cocaine-induced FrA hypoactivity and locomotor sensitization (Figs. 5, 6). Although both excitatory chemogenetic manipulations of vmPFC–D1 and –D2 neurons were sufficient to induce suppression of the FrA (Fig. 6b), the different percentages of suppression and different effects by antagonists (Fig. 6d) suggest different roles of vmPFC–D1R and –D2R signaling in cocaine exposure. Since connections between the vmPFC and the FrA include both excitatory and inhibitory projections, we also injected retrograde virus (Retro-CaMKIIα/vGAT-Cre) in the FrA and Cre-dependent hM3Dq/hM4Di virus in the vmPFC for chemogenetic manipulation of FrA-projecting vmPFC neurons (Glu+ or GABA+). Surprisingly, only the chemogenetic activation (DIO-hM3Dq) of FrA-projecting vmPFC GABA+ neurons (Retro-vGAT-Cre) profoundly abolished the locomotor sensitization (Fig. S13), implying essential roles of GABA+ projections from the vmPFC to the FrA in cocaine sensitization. These findings imply the complex and manifold local circuit connections between the vmPFC and the FrA. For example, vmPFC–D1R or –D2R neurons may innervate FrA excitatory neurons via the direct projection, or indirectly through the local circuits in either the upstream vmPFC or the downstream FrA. The circuit complexity may also derive from the VTA-mPFC projections. Since VTA dopaminergic neurons also co-release glutamate or GABA with dopamine54 and fire in various ways in response to cocaine43,55, there is no consensus on how dopamine regulates mPFC activity24,25. Especially, in contrast to the studies with slice electrophysiology, in vivo studies have shown greater heterogeneity in the dopamine regulation of PFC activity22,23,37,56, which exhibits high spatiotemporal diversity23. Although details of how vmPFC neurons function are obscure, our present work provides an underlying cortico-cortical pathway through which the cocaine-induced dopamine transmission indirectly suppresses FrA neural activity and thus mediates locomotor sensitization. Following the identification of this cortico-cortical circuit in the awake brain in the present study, future work may address: [1] how does vmPFC–FrA transmission function to mediate the FrA hypoactivity; [2] how does cocaine-induced dopamine abnormality regulates the activity of different types of neurons in the mPFC; [3] how does mPFC microcircuits translate the impact on different FrA neurons through mixed projections, especially in the awake state. In summary, by using two-photon Ca2+ imaging in the FrA of awake mice, we defined the PFC-subregion FrA as a critical region mediating cocaine-associated locomotor sensitization, and uncovered the projection of VTADA neurons to the vmPFC mediates the cocaine-induced hypoactivity of FrA neurons and the locomotor sensitization: cocaine (i.p.) → VTA-mPFC DA transmission → NEUROND1/D2 → FrAcircuit → Behaviors (Fig. 7). This provides a brain-circuits-mechanism on cocaine-abuse of how cocaine affects the PFC and locomotor sensitization during the early onset of drug abuse, shedding new light on the mechanisms underlying dopamine-related physiological and pathological changes. ## Animals and chemicals Adult (3–4 months old) male C57BL/6 J mice were purchased from Charles River Laboratories; vGAT-Cre transgenic mice57 were kindly gifted by Dr. Chen Zhang (Capital Medical University, China); DAT-Cre mice were kindly provided by Dr. Yi Rao (Peking University, China); DAT-KO mice were homozygous offspring of heterozygous DAT-Cre mice49, which was generated by a 5′-UTR knock-in strategy to express Cre recombinase under the control of DAT promoter. DAT-CI mice were kindly gifted by Dr. Howard H. Gu (Ohio State University, USA)3, which is a knock-in mouse line expressing functional DATs with triple mutations that are insensitive to cocaine; D1-Cre (Drd1a-Cre) and D2-Cre (Drd2-Cre)58 and Ai9 mice59 were gifts from Dr. Yousheng Shu (Fudan University, China). All mice were housed in the Animal Center of Peking University at 22 ± 2 °C and 50–$60\%$ relative humidity with a 12-h light/dark cycle and provided with food and water ad libitum. The use and care of animals were approved and directed by the Animal Care and Use Committee of Peking University and the Association for Assessment and Accreditation of Laboratory Animal Care. All chemicals were from Sigma-Aldrich unless stated otherwise. Cocaine hydrochloride was from Qinghai Pharmaceutical Factory (China), GBR-12783 dihydrochloride was from Shanghai Macklin Biochemical (China), and SKF-38393 hydrobromide and SCH-23390 hydrochloride were from Tocris Bioscience (United Kingdom). Drugs for intraperitoneal injection were dissolved in sterile saline ($0.9\%$ NaCl). ## Preparation for head-fixed two-photon Ca2+ imaging The fluorescent Ca2+ indicators Cal-52040 and GCaMP6s41 were used32,60 with minor modifications. All surgical instruments were sterilized before experiments and body temperature was maintained using a heating pad. Mice were anaesthetized with Avertin (250 mg/kg, i.p.) and a custom-made chamber was fixed to the skull with cyanoacrylate and dental cement. After 3 days of recovery, mice were trained to adapt to head fixation for 3 days (1–3 h per day). On the day of imaging, mice were briefly anaesthetized with 1–$1.5\%$ isoflurane in oxygen for craniotomy with a high-speed drill (RWD). Then the frontal association cortex (FrA: AP + 2.8 mm; ML + 1.0 mm) was exposed through a cranial window (1.5 × 1.5 mm) for multicell bolus loading of Cal-520 AM (500 µM; AAT Bioquest). Cal-520 AM was initially dissolved in DMSO with $20\%$ Pluronic F-127 and then diluted with loading solution (mM): 150 NaCl, 2.5 KCl, 10 HEPES, pH 7.3–7.432,40. Borosilicate pipettes (2–3 MΩ) were used to inject Cal-520 AM into the FrA by air pressure (0.02 MPa, 1 min). Before covering with a glass coverslip, the exposed brain was perfused with artificial cerebral spinal fluid (ACSF, in mM): 125 NaCl, 4.5 KCl, 26 NaHCO3, 1.25 NaH2PO4, 2 CaCl2, 1 MgCl2, 10 glucose, pH 7.3–7.4 saturated with $95\%$ O2/$5\%$ CO261. Images were captured 1.5 h after dye loading. GCaMP6s was used to observe excitatory and GABAergic neurons in the FrA through viral injection of AAV-CaMKIIα-GCaMP6s (AAV$\frac{2}{9}$-CaMKIIα-GCaMP6s -WPRE-pA, BrainVTA) in C57BL/6 J mice, or AAV-Flex-GCaMP6s in vGAT-Cre transgenic mice. After viral injection (method below), a chronic glass cranial window60 was immediately created above the FrA and a custom-made chamber was glued to the skull using dental cement for subsequent head-fixed imaging (3–4 weeks later). Before imaging, mice were trained for 3 days to adapt to head fixation as described above. ## Two-photon Ca2+ imaging A two-photon laser scanning microscope (FV1200MPE, Olympus) was used to record Ca2+ transients in the FrA. The excitation light (920 nm for GCaMP6s and 830 nm for Cal-520) was produced by a mode-locked Ti:Sa laser (Mai-Tai DeepSee, Spectra Physics) through a 25× water-immersion objective (1.05 NA, Olympus). Fluorescent Ca2+ signals from individual neurons 100–200 μm beneath the cortical surface were imaged at 7.75 Hz (256 × 256 pixels, 254.5 × 254.5 μm). To assess the population activity of stochastic neuronal firing, the averaged frequency of SSCSs events collected from all visualized neurons in 6.45 min were quantified as SSCS frequency (events/min/neuron) during the basal (before drug injection) or the drug-treated state (10–20 min after i.p. injection). Two-photon images were processed and analyzed using ImageJ (v1.51t, NIH, USA) and custom-written software in MatLab (2016b, MathWorks). Each SSCS was automatically detected as an individual Ca2+ transient where the somatic fluorescence change (dF/F0 = (F – F0)/F0) was >3 times the standard deviation of the baseline (F0)32. Regions of interest (ROIs) were manually identified to extract the trace of averaged fluorescence intensity (F) of each soma and F0 was the averaged baseline intensity calculated with an iterative algorithm subtracting the Ca2+ spike with high intensity. Glial cells were excluded when ROIs were determined based on the morphology and dynamics of the Ca2+ transients. ## Viral injection Mice were anaesthetized with Avertin (250 mg/kg, i.p.) and fixed on a digital stereotaxic instrument (RWD) for viral injection. The eyes were protected with ophthalmic ointment. Hair was removed from the head and exposed skin was scrubbed with betadine. After the incision of the skin, all coordinate measurements for different locations were made relative to the bregma unless stated otherwise. Exposed tissue was kept wet with sterile saline ($0.9\%$ NaCl) in gel foam. The skull was thinned and cracked with the tip of a 1-mL syringe needle without damage to the cortex. The indicated viruses were injected under air pressure into the targeted area using borosilicate glass pipettes with an inner tip diameter of 9–10 μm. The injection speed was controlled at 20–30 nL/min and left for >5 min before slowly withdrawing the pipette. The skin incision was closed with nylon sutures if there was no craniotomy or chamber fixation. Animals were maintained on a heating pad until fully recovered. The AAV viruses used in this study were: AAV-CaMKIIα-GCaMP6s (AAV$\frac{2}{9}$-CaMKIIα-GCaMP6s-WPRE-pA, 2 × 1012 vg/mL, BrainVTA), 200 nL, unilaterally injected into the FrA (AP + 2.80 mm; ML + 1.00 mm; 0.2–0.3 mm from the dura surface); AAV-Flex-GCaMP6s (AAV$\frac{2}{9}$-Syn-Flex-GCaMP6s-WPRE-SV40, 2 × 1012 vg/mL, Penn Vector Core), 200 nL, unilaterally injected into the FrA of vGAT-Cre mice; AAV-CaMKIIα-hM3Dq (AAV$\frac{2}{9}$-CaMKIIα-hM3Dq-mCherry- WPRE-pA, 6.0 × 1012 vg/mL, BrainVTA), AAV-CaMKIIα-hM4Di (AAV$\frac{2}{9}$-CaMKIIα -hM4Di-mCherry-WPRE-pA, 5.8 × 1012 vg/mL, BrainVTA) and AAV-CaMKIIα -mCherry (AAV$\frac{2}{9}$-CaMKIIα-mCherry-WPRE-pA, 4.1 × 1012 vg/mL, BrainVTA), 200 nL/side, bilaterally injected into the FrA; AAV-Retro-hSyn-EYFP (AAV2/R-hSyn-EYFP-WPRE-pA, 5.0 × 1012 vg/mL, BrainVTA), 200 nL, unilaterally injected into the FrA; AAV1-hSyn-Cre (1.4 × 1013 vg/mL, OBiO Technology), 100 nL/site, unilaterally injected into the vmPFC (AP + 1.70 mm; ML + 0.30 mm; DV –3.30/–3.00 mm) of Ai9 mice; AAV-Retro-CaMKIIα-EYFP (AAV2/R-CaMKIIα-EYFP-WPRE-pA, 5.0 × 1012 vg/mL, BrainVTA), 200 nL, unilaterally injected into the FrA of D1-/D2-Cre × Ai9-crossed mice; AAV-Retro-vGAT-EYFP (AAV2/R-vGAT1-EYFP-WPRE-pA, 5.1 × 1012 vg/mL, BrainVTA), 200 nL, unilaterally injected into the FrA of D1-/D2-Cre × Ai9-crossed mice; AAV-Flex-ChR2-EYFP (AAV$\frac{2}{9}$-EF1α-Flex-hChR2(H134R)-EYFP-WPRE-hGH, 4.9 × 1012 vg/mL, Penn Vector Core), 200 nL/side, bilaterally injected into the vmPFC (AP + 1.75 mm; ML ± 0.30 mm; DV –3.05 mm) of D1-/D2-Cre mice. AAV-Dio-hM3Dq (AAV$\frac{2}{9}$-EF1α-Dio-hM3Dq-mCherry-WPRE-pA, 5.0 × 1012 vg/mL, BrainVTA), AAV-Dio-hM4Di (AAV$\frac{2}{9}$-EF1α-Dio-hM4Di-mCherry-WPRE-pA, 5.2 × 1012 vg/mL, BrainVTA) and AAV-Dio-mCherry (AAV$\frac{2}{9}$-EF1α-Dio-mCherry -WPRE-pA, 5.1 × 1012 vg/mL, BrainVTA), 200 nL/side, bilaterally injected into the vmPFC (AP + 1.75 mm; ML ± 0.30 mm; DV –3.05 mm) of D1-/D2-Cre mice. Viruses or drugs were aliquoted and stored at −80 °C before use. For all experiments involving stereotaxic injections, animals targeting the wrong location were excluded after verification. ## Behavioral tests Mice were trained and tested in a custom-built acrylic arena inside an individual room with control of light, temperature, odor, and noise. The locations and locomotion of mice were automatically captured by a video tracking system LabState5.10 (Anilab Software & Instruments)32 to measure cocaine-induced CPP and locomotor sensitization43. The arena was designed with three interconnected chambers: two lateral chambers A/B (25 cm × 25 cm for each) and a middle chamber (8 cm × 25 cm). The floor of chamber A was equipped with parallel rods, chamber B with a square grid, and the middle chamber with a smooth floor. As the timeline in Fig. 1l shows, on day 0 (pretest), mice were transferred to the middle chamber and allowed free access to the three chambers for 15 min. There was no significant basal preference for chamber A or B before conditioning. During days 1–3 (conditioning), mice were repeatedly conditioned with saline and cocaine injections (i.p.) paired to opposite lateral chambers while their locomotion was recorded. Each morning, a mouse was confined to a given lateral chamber (A or B, stochastically) for 30 min immediately after cocaine (or saline in alternation), and confined to the other side immediately after saline injection (or cocaine accordingly) in the afternoon (6 h later). Mice with paired saline administration served as controls. To test the curative effect of FrA excitation, the AAV-CaMKIIα-hM3Dq mice were used and CNO (0.5 mg/kg, i.p.) was injected 30 min before cocaine (10 mg/kg, i.p.) injection (or a random saline injection in the control group). The percentages of mice receiving cocaine injection in the morning or afternoon and those paired to chamber A or B were equal to achieve a counterbalance design. On day 4 (posttest), mice were tested as on day 0. The CPP score was defined as the time (T) differences of preference in the cocaine-paired chamber between the posttest and pretest: CPP score = (Tcoc – Tsal)posttest – (Tcoc – Tsal)pretest. Cocaine-induced locomotor sensitization was assessed as the distances traveled in 30 min after cocaine injection during the 3 conditioning days. Behavioral tests were performed 4–6 weeks after the viral injection. ## Lesion of dopamine neurons The neurotoxins 6-OHDA62,63 and MPTP62,64 were used to deplete dopamine neurons/projections with slight modifications. MPTP was repetitively injected for 5 days (30 mg/kg, i.p. daily) to preferentially induce the loss of SNc dopaminergic neurons and the projections mainly targeting the CPu. The stereotaxic microinjection of 6-OHDA (2.5 µg/µL, in saline with 0.2 mg/ml ascorbic acid) was applied for the local depletion of dopaminergic terminals or somata in the targeted regions. The 6-OHDA was bilaterally injected (200 nL per site) into the FrA (in mm: AP + 2.80; ML ± 1.00; 0.2–0.3 from the dura surface), the VTA (site$\frac{1}{2}$: AP –3.20; ML ± 0.40; DV –4.40; site$\frac{2}{2}$: AP –3.60; ML ± 0.40; DV –4.15), the SNc (site$\frac{1}{2}$: AP –3.30; ML ± 1.45; DV –4.00; site$\frac{2}{2}$: AP –3.30; ML ± 1.45; DV –4.60), the vmPFC (AP + 1.70; ML ± 0.30; DV –3.20/–2.90), and the BLA (AP –1.15; ML ± 2.90; DV –4.55). The surgery for two-photon imaging preparation was the same as above and performed immediately after microinjection. One week after 6-OHDA injection (but 21–28 days in the FrA), mice were used for two-photon Ca2+ imaging, and were then sacrificed to verify the loss of dopaminergic neurons/terminals with TH-staining (details below). ## Immunofluorescence Mice were anaesthetized with Avertin (250 mg/kg, i.p.) and transcardially perfused with $4\%$ paraformaldehyde (PFA) in PBS. Brains were postfixed in $4\%$ PFA over 12 h and dehydrated in 10, 20, and $30\%$ sucrose over 72 h for cryoprotection. Then frozen sections (40 µm; CM1950, Leica) were collected for permeabilization ($0.3\%$ Triton X-100 in PBS) and blocking ($2\%$ bovine serum albumin in PBS). After that, the sections were incubated with rabbit anti-TH primary antibody (1:500; AB152, Millipore) or mouse anti-GAD67 primary antibody (1:500; MAB5406, Millipore) overnight at 4 °C. After three washes, the sections were incubated with secondary antibodies (1:500; donkey anti-rabbit 594, A21207 or donkey anti-mouse 488, A21202; Invitrogen) for 1 h and DAPI for nuclear staining at room temperature. The sections were mounted on slides with $50\%$ glycerol and kept at –20 °C. Immunofluorescence images were captured using a laser scanning confocal microscope (LSM710, Carl Zeiss) and paired sections were imaged and processed at the same settings. ImageJ (v1.51t, NIH, USA) and Adobe Illustrator (v2019-23.1.0, Adobe Systems) were used for image processing. ## Amperometric dopamine recording Carbon fiber electrodes (CFEs, 7 µm in diameter) were used for in vivo electrochemical amperometric recording of evoked dopamine overflow65. In brief, mice were placed into a stereotaxic instrument (Narishige) under anesthesia (Avertin, 250 mg/kg, i.p.) and craniotomies were made for CFE recording in the vmPFC (in mm: AP + 1.75; ML + 0.30; DV –3.30), bipolar-electrode stimulation in the ipsilateral medial forebrain bundle (AP + 1.00; ML + 1.40; DV –5.20 to –5.60), and the insertion of an Ag/AgCl reference electrode into the contralateral visual cortex (AP –3.50; ML –2.30; DV –1.00). According to our lab’s works65,66 and others’ works67,68, it’s well-established that it’s easier to get stable evoked terminal signals from the projection bundles in the MFB, which were confined to a limited region, but not from the somata region in the VTA which are usually more dispersed. The holding potential of the CFE was maintained at 780 mV under the control of a patch-clamp amplifier (low-pass filtered at 50 Hz; PC2C, INBIO) and MBA-1 software (INBIO). The electrical stimulus was generated by an isolator (A395, WPI) as a train of biphasic square-wave pulses (0.6 mA, 1 ms duration). CFE signals were recorded when stable during the basal state and 10–20 min after cocaine injection (10 mg/kg, i.p.). The amperometric traces were processed and analyzed using IGOR Pro 6.37 (WaveMetrics). ## Micro-infusion Cannula-guided microinfusion of drugs was combined with two-photon Ca2+ imaging or behavioral tests in awake mice to investigate dopamine signaling in the vmPFC and BLA. For two-photon calcium imaging, a single guide cannula (O.D. 0.41 mm) was inserted into the vmPFC (in mm: AP + 1.30; ML + 0.30; DV –2.70) at an angle of 53° to the horizontal plane to leave space for the objective (40×/0.80 W/3.5 WD, Olympus). For behavioral tests, double guide cannulae (O.D. 0.41 mm) were inserted into the vmPFC (in mm: AP + 1.75; ML ± 0.30; DV –2.50) vertically. The cannula implantation in the BLA was similar, except that it was vertically inserted at AP –1.15; ML + 2.90; DV –4.10. The guide cannula was secured with dental cement and a dummy cannula was inserted to prevent clogging. The surgery for two-photon imaging was the same as above and performed immediately after cannula surgery. After recovery for 7 days, an injector cannula (O.D. 0.21 mm) with a 0.5-mm extension beyond the tip of the guide cannula was used for microinjection. The injector cannula was connected to a 10-µL Hamilton syringe controlled by a microinfusion pump (KD Scientific). Cocaine hydrochloride (40 mg/mL), dopamine hydrochloride (40 mg/mL), SKF-38393 hydrobromide (5 mg/mL), SCH-23390 (2 mg/mL), quinpirole hydrochloride (5 mg/mL), and sulpiride hydrochloride (2 mg/mL) were dissolved in $0.9\%$ saline and infused at 0.2 µL/min for 1 min. If combined with imaging, the microinfusion was applied 5 min after i.p. injection of cocaine and images were recorded 5 min after microinfusion. If combined with behavioral tests, the microinfusion was applied 1–3 min after i.p. injection of cocaine and the animals were transferred to the behavioral chambers 1 min after the end of microinfusion. The infusion sites were verified by infusion of Chicago sky blue and sectioning after imaging. Mice with incorrect microinfusion locations were excluded. ## Neural tracing We used retrograde and trans-synaptic anterograde AAV to identify afferents of the FrA and the types of FrA neurons receiving the terminals of vmPFC efferent neurons. Retrograde AAVs (AAV-Retro-hSyn-EYFP, AAV-Retro-CaMKIIα-EYFP, and AAV-Retro-vGAT-EYFP) were unilaterally injected into the FrA. Sequential coronal sections of mouse brains were acquired as above and fluorescent images were analyzed referring to the atlas69. The anterograde trans-monosynaptic tracing virus (AAV1-hSyn-Cre)70 was unilaterally injected into the vmPFC of Ai9 (tdTomato) mice. Coronal sections of the FrA were immunostained with anti-GAD67 antibody for the identification of neuronal types receiving vmPFC inputs. The FrA was roughly divided into different layers (layer I, 0–100 µm; layers II/III, 100–500 µm; layers V/VI, 500–1000 µm) based on related studies34 and the very low density of somata in layer I. Soma numbers were counted in an area of 500 × 500 µm in vmPFC slices and 1 mm ×1 mm in FrA slices. ## Electrophysiology in brain slices Coronal brain slices were prepared for patch-clamp recording71. Briefly, mice were anesthetized with urethane (1.5 g/kg, i.p.) and transcardially perfused with 10 mL ice-cold cutting solution containing (mM): 110 choline chloride, 2.5 KCl, 1.3 NaH2PO4, 0.5 CaCl2, 7 MgCl2, 25 NaHCO3, 10 glucose, 1.3 l-ascorbate, 0.6 Na-pyruvate (pH 7.3–7.4, saturated with $95\%$ O2/$5\%$ CO2). The brains were cut at 250 µm on a vibratome (VT1200S, Leica) in an ice-cold cutting solution and incubated in ACSF for 30 min at 35 °C and another 30 min at room temperature (22–25 °C) before recording. The ACSF contained (mM): 125 NaCl, 2.5 KCl, 1.3 NaH2PO4, 2 CaCl2, 1.3 MgCl2, 25 NaHCO3, 10 glucose, 1.3 L-ascorbate, 0.6 Na-pyruvate (pH 7.3–7.4, saturated with $95\%$ O2/$5\%$ CO2). Slices in the recording chamber were superfused (2 mL/min) with ACSF during electrophysiological recording. A multi-channel perfusion device (MPS-1, INBIO) was used for local drug delivery (in ACSF) to the recorded cells. An upright microscope (BX51WI, Olympus) equipped with fluorescent, infrared, and differential interference contrast devices were used to visualize cells and guide patch pipettes (3–4 MΩ, borosilicate glass, WPI). We used an EPC$\frac{9}{2}$ amplifier and Pulse software (HEKA Elektronik) to obtain whole-cell patch-clamp recordings and signals were digitized at 20 kHz and low-pass filtered at 2.9 kHz. Electrophysiological data were processed with IGOR Pro 6.37 (WaveMetrics). Whole-cell voltage-clamp (–70 mV) recording of light-evoked EPSCs and IPSCs was applied to identify the synapses between vmPFC–D1R/-D2R and FrA layer II/III neurons. AAV-Flex-ChR2-EYFP was injected into the vmPFC of D1-/D2-Cre mice (Fig. S12) 4–6 weeks before recording. Cells in FrA slices were randomly chosen according to their pyramidal shape and layer II/III location (100–500 µm). The pipette internal solution contained (mM): 153 CsCl, 1 MgCl2, 10 HEPES, 4 Mg-ATP, and 3.3 QX314 (pH 7.3 adjusted with CsOH). Photostimulation (1 ms, 0.1 Hz) was delivered by a 473-nm laser device (VD-IIIA, Beijing Viasho Technology) under the control of an EPC$\frac{9}{2}$ amplifier. IPSCs were recorded in the presence of 50 μM D-AP5 (D[-]−2-amino-5-phosphonovaleric acid, an antagonist of NMDA receptors) and 10 μM CNQX (6-cyano-7-nitroquinoxaline-2,3-dione, an antagonist of AMPA receptors). EPSCs were recorded in the presence of 100 μM PTX (picrotoxin, an antagonist of GABA receptors). Whole-cell current-clamp (0 pA) recording of action potentials was used to verify the excitatory modulation of FrA layer II/III pyramidal neurons by chemogenetic activation. AAV-CaMKIIα-hM3Dq-mCherry was injected into the FrA. hM3Dq+ cells were identified through the fluorescence of mCherry. The pipette internal solution contained (in mM): 130 K-gluconate, 10 KCl, 2 MgCl2, 2.5 Mg-ATP, 0.25 Na-GTP, 10 HEPES, and 0.4 EGTA (adjusted to pH 7.3 with KOH). Action potentials were stimulated by current injection (200 pA, 500 ms) and recorded before and after perfusion with CNO (10 μM). ## Statistical analysis Statistical analyses were made using Prism 7 (GraphPad Software). The sample size was not predetermined but similar to those in similar studies32,43,56,72. All t-tests and non-parametric tests were two-tailed and data were presented as the mean ± s.e.m for n < 12 with data points plotted or box-and-whisker plots for n > 12 (minimum to maximum with three quartiles marked). Normality was tested for all two-photon Ca2+ imaging data by the D’Agostino-Pearson omnibus test. Non-parametric data were analyzed with the Wilcoxon test for paired comparisons or the Mann–Whitney test for unpaired comparisons and the others were analyzed with paired or unpaired Student’s t-test. For grouped analyses, one-way ANOVA followed by Dunnett’s multiple comparisons or two-way ANOVA followed by Bonferroni’s or Tukey’s multiple comparisons were used to make comparisons. All the detailed statistical methods and results are listed in the corresponding legends. All animals and samples that were successfully tested were included in our analysis, and at least three biologically individual animals were used for repetition in each experiment. 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--- title: Evidence of polygenic regulation of the physiological presence of neurofilament light chain in human serum authors: - Marisol Herrera-Rivero - Edith Hofer - Aleksandra Maceski - David Leppert - Pascal Benkert - Jens Kuhle - Reinhold Schmidt - Michael Khalil - Heinz Wiendl - Monika Stoll - Klaus Berger journal: Frontiers in Neurology year: 2023 pmcid: PMC10030935 doi: 10.3389/fneur.2023.1145737 license: CC BY 4.0 --- # Evidence of polygenic regulation of the physiological presence of neurofilament light chain in human serum ## Abstract ### Introduction The measurement of neurofilament light chain (NfL) in blood is a promising biomarker of neurological injury and disease. We investigated the genetic factors that underlie serum NfL levels (sNfL) of individuals without neurological conditions. ### Methods We performed a discovery genome-wide association study (GWAS) of sNfL in participants of the German BiDirect Study ($$n = 1$$,899). A secondary GWAS for meta-analysis was performed in a small Austrian cohort ($$n = 287$$). Results from the meta-analysis were investigated in relation with several clinical variables in BiDirect. ### Results Our discovery GWAS identified 12 genomic loci at the suggestive threshold (($p \leq 1$ × 10−5). After meta-analysis, 7 loci were suggestive of an association with sNfL. Genotype-specific differences in sNfL were observed for the lead variants of meta-analysis loci (rs34523114, rs114956339, rs529938, rs73198093, rs34372929, rs10982883, and rs1842909) in BiDirect participants. We identified potential associations in meta-analysis loci with markers of inflammation and renal function. At least 6 protein-coding genes (ACTG2, TPRKB, DMXL1, COL23A1, NAT1, and RIMS2) were suggested as genetic factors contributing to baseline sNfL levels. ### Discussion Our findings suggest that polygenic regulation of neuronal processes, inflammation, metabolism and clearance modulate the variability of NfL in the circulation. These could aid in the interpretation of sNfL measurements in a personalized manner. ## Background Neurofilament light chain (NfL) is a subunit of neurofilaments (NFs), cytoskeletal components found exclusively in neurons and particularly abundant in axons. NfL is a major component of the backbone of NFs in the central and peripheral nervous systems [1]. Axonal damage and neuronal death due to neurological diseases, including those of inflammatory, neurodegenerative, traumatic and cerebrovascular nature, result in NfL release into the cerebrospinal fluid (CSF) and blood. Recent technological advances in immunoassay detection have enabled the accurate measurement of the small amounts of NfL that reach the circulation, facilitating its application as a universal peripheral biomarker of the presence and progression of neurological conditions, and of treatment responses (1–3). Therefore, investigating the factors that influence concentrations of NfL in the periphery becomes crucial for the interpretation of results. To date, it has been demonstrated that NfL serum levels (sNfL) increase with age [4] and potential confounding factors, such as body mass index and cardiovascular risk factors, have been suggested [5, 6]. Studies in population-based cohorts have shown a polygenic nature of numerous health-related serum biomarkers, including alanine transaminase (liver function), fibrinogen (clot formation) and glycated hemoglobin (type 2 diabetes mellitus), among many others. These findings can provide novel biological insights and facilitate disease diagnosis and stratification [7]. Nevertheless, to our knowledge, no genetic associations with sNfL have been investigated. We hypothesized that the identification of genetic factors that modulate sNfL in physiological conditions will help interpretation on an individual basis, consequently improving the clinical applications of sNfL as a biomarker. To test our hypothesis, we performed a genome-wide association study (GWAS) and meta-analysis of sNfL in a total of 2,186 individuals of European descent without known neurological conditions, and correlated our findings with clinical data to identify potential sources of sNfL variability. ## Study populations The BiDirect Study was initiated in 2009 as a prospective, observational study integrating three cohorts: [1] community-dwelling adults (control cohort), [2] patients with an acute depressive episode (depression cohort), and [3] patients who recently suffered from acute myocardial infarction (MI cohort). The study, whose principal goal is the exploration of the bidirectional relationship between depression and subclinical arteriosclerosis, recruited participants in the district of Münster, Germany, and carried out extensive phenotyping and follow-up of all cohorts in parallel. The study design and methods have been previously described in detail [8]. Here, we included 1,899 BiDirect participants (977 males, 922 females; mean age: 52.1 ± 7.9) from the control [763], depression [851] and MI [285] cohorts. The Austrian Stroke Prevention Family Study (ASPS-Fam) cohort represents an extension of the prospective, population-based ASPS (Austrian Stroke Prevention Study) on the effects of vascular risk factors in normal aging. ASPS was established in 1991 in the city of Graz, Austria [9]. For ASPS-Fam, first-degree relatives of ASPS participants were invited to join the study. The study's composition and inclusion criteria have been described elsewhere [10, 11]. Here, we included 287 ASPS-Fam participants (115 males, 172 females; mean age: 64.3 ± 10.6). The basic descriptive information of the BiDirect and ASPS-Fam cohorts are shown in Table 1. Summary information on study design and composition can be found in the Supplementary material 1. All participants of the BiDirect and ASPS-Fam cohorts provided written informed consent. Methods were carried out in accordance with the ethical standards laid down in the updated version of the 1964 Declaration of Helsinki. The BiDirect Study was approved by the Ethics Committee of the University of Münster and the Westphalian Chamber of Physicians in Münster, North-Rhine-Westphalia, Germany. The ASPS-Fam protocol was approved by the Ethics Committee of the Medical University of Graz, Austria. Table 1 \| Cohort \| Log2 sNfL (mean ±SD) \| Age (mean ±SD) \| Males (n) \| Females (n) \| Total (N) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| BiDirect \| 2.16 ± 0.45 \| 52.1 ± 7.9 \| 977 \| 922 \| 1899 \| \| BiDirect-control \| 2.15 ± 0.44 \| 53.4 ± 8.2 \| 385 \| 378 \| 763 \| \| BiDirect-depression \| 2.13 ± 0.43 \| 49.9 ± 7.3 \| 348 \| 503 \| 851 \| \| BiDirect-MI \| 2.29 ± 0.5 \| 55.2 ± 6.7 \| 244 \| 41 \| 285 \| \| ASPS-Fam \| 4.99 ± 0.65 \| 64.3 ± 10.6 \| 115 \| 172 \| 287 \| ## Serum measurements of NfL Quantification of sNfL in BiDirect and ASPS-Fam was conducted at the University Hospital Basel, Switzerland, using the single molecule array (Simoa®) HDX analyzer (Quanterix, Lexington, MA, USA). In BiDirect participants, measurements of sNfL were obtained from non-fasting blood samples collected at the first visit, using the Simoa® NF-light Advantage Kit. In ASPS-Fam participants, sNfL measurement (Supplementary material 1) has been previously described in detail [4]. The sNfL values obtained at initial assessment were log2-transformed and used for all analyses herein reported. Therefore, with sNfL in our findings, we actually refer to log2 sNfL. Because it is known that sNfL concentrations increase during aging [4], we tested for age-adjusted sex- and cohort-dependent sNfL differences in BiDirect using analysis of covariance (ANCOVA). We also tested for sNfL correlations, using the Pearson's method, with markers of inflammation, renal and liver function, lipids, hormones and brain volumes derived from magnetic resonance imaging (MRI) data (106 clinical variables in total). All $p \leq 0.05$ values were considered statistically significant. Here, age represented the age at participant recruitment, when baseline phenotyping (s0) took place. Clinical variables coming from up to three subsequent follow-up visits were identified as time points s2, s4, and s6. ## Genotype data For BiDirect genotypes, genomic DNA was isolated from whole blood samples with EDTA using standard DNA extraction kits and procedures at the University of Münster. Genome-wide genotyping was performed with the Infinium PsychArray BeadChip v1 (Illumina) at Life&Brain GmbH (Bonn, Germany). Basic quality control (QC) was employed to remove samples and variants with high rates of missing data. This included removal of individuals with genotyping rate < $2\%$, cryptic relatedness (PI-HAT ≥$\frac{1}{16}$), sex mismatch and genetic outliers (distance in first two multidimensional scaling components >5 standard deviations from the mean), as well as the removal of variants with call rate < $2\%$ and minor allele frequency (MAF) < $1\%$. Genotype imputation was performed with SHAPEIT (pre-phasing) [12] and IMPUTE2 [13] using the 1,000 Genomes Project, phase 3, European population reference panel (from here on, 1KG Reference Panel). Imputed variants were filtered for the INFO metric (≥0.8), MAF≥0.01 and Hardy-Weinberg equilibrium (HWE p ≥ 1 × 10−6). Individuals were further removed from the sample based on missing phenotypic data (age and baseline sNfL measurement). The final BiDirect GWAS dataset consisted of 5,597,244 genetic variants and 1,899 individuals. For ASPS-Fam genotypes, genome-wide genotyping was performed with the Genome-Wide Human SNP Array 6.0 (Affymetrix). During the initial QC, variants with MAF < 0.05, HWE < 5 × 10−6 and low variant call rate (>$2\%$) were excluded. Individuals with sex mismatch, cryptic relatedness, low sample call rate (>$2\%$), a heterozygosity rate exceeding the mean ± 3 standard deviations and erroneous duplicates were removed. No genetic outliers were present. Genotype imputation was performed using the Michigan Imputation Server [14] and the 1KG Reference Panel. Of note, genetic variants herein comprise single nucleotide polymorphisms (SNPs), as well as small insertions/deletions (indels) present in the datasets. ## Screening for genetic associations with sNfL We conducted a discovery GWAS in the BiDirect dataset under an additive regression model, adjusting for age, sex, cohort and the first 10 principal components. A secondary GWAS in the smaller ASPS-Fam dataset was performed independently at the Medical University of Graz and was adjusted for age, sex and the first 10 principal components. After harmonization of summary statistics from both studies, we performed a weighted meta-analysis of all overlapping variants with Rsq≥0.8 and MAF≥0.01 using Plink 1.9 [15]. Variants with high heterogeneity between studies (I>40 and Q < 0.1) were subsequently neglected. ## Definition of genomic loci for sNfL For the discovery GWAS and the meta-analysis, we carried out downstream analyses on the FUMA GWAS platform [16] and defined genomic loci at the suggestive threshold of significance for genome-wide studies ($p \leq 1$ × 10−5), obtained variant annotations and identified the level of support for each signal. Linkage disequilibrium (LD) was defined by r2 ≥ 0.6 and a window of 500 kb, according to the 1KG Reference Panel. Subsequently, LD blocks were formed with variants under the suggestive threshold as lead variants, and containing all variants with $p \leq 0.05$ in the dataset that were in LD with the corresponding lead variants. Positional (gene) mapping was performed according to a maximum distance of 1 kb for the categories protein-coding, long non-coding RNA (lncRNA), non-coding RNA (ncRNA) and processed transcripts. Expression quantitative trait loci (eQTLs) were mapped using the BRAINEAC and GTEx v8 Brain databases. Only SNP-gene pairs with false discovery rate (FDR) < 0.05 were annotated. ## Functional implications of suggested candidate genes To inform the biological meaning of our findings, we created a protein-protein interaction (PPI) network using our suggested meta-analysis candidate genes as input. The network was generated with the Gene Set analysis tool of the ReactomeFIViz app for Cytoscape v.3.7.1 [17, 18]. Linker proteins and functional interaction (FI) annotations were incorporated into the network (version 2018). In addition, we performed clustering of nodes, as well as enrichment analyses of pathways and gene ontology cellular components (GO_CC) for each network cluster. Gene sets with FDR < 0.05 were considered significantly enriched. ## SNP heritability (hSNP2) We calculated the proportion of variance in sNfL concentrations explained by our discovery GWAS in BiDirect using the GREML-LDMS (LD- and MAF-stratified GREML) method implemented in GCTA [19, 20]. For all autosomal variants with MAF≥0.01 in the imputed dataset, we calculated the 200 kb segment-based LD scores, stratified variants according to LD scores of individual SNPs, computed one genetic relationship matrix for each quartile of the stratified variants, and performed a restricted maximum likelihood analysis using these four matrices. The variance explained was adjusted for the same covariates as the GWAS. SNP heritability from our meta-analysis summary statistics was calculated using LDSC software [21] with LD scores pre-computed in 1KG Reference Panel data, as suggested by the authors. ## Screening for associations with clinical variables For the lead variant of each loci resulting from our meta-analysis, we performed genotype-specific comparisons in BiDirect participants using an ANCOVA model adjusted for age. Moreover, for all variants within meta-analysis loci, we tested for associations with the same set of clinical variables used in the correlation analyses. These association tests were performed in the same manner as for baseline sNfL. The Benjamini-Hochberg method was used to correct for multiple comparisons (adjP). ## Basic characterization of sNfL in BiDirect Our initial characterization of sNfL in BiDirect found similar distributions of sNfL in the three cohorts (sNfL raw mean ± standard deviation values: control 9.49 ± 6.57, depression 9.24 ± 4.99, MI 11.76 ± 11.62; corresponding log2 values: control 2.15 ± 0.44, depression 2.13 ± 0.43, MI 2.29 ± 0.5; Figure 1A) and a positive association with age ($p \leq 2$ × 10−16, beta = 0.03), which was independent of the cohort (Figure 1B). Age-adjusted comparisons showed mean differences in sNfL levels between both patient cohorts (depression $$p \leq 8.2$$ × 10−5, MI $$p \leq 1.4$$ × 10−3) and the reference cohort, while no differences could be attributed to sex ($$p \leq 0.56$$) in this dataset (Figure 1C). Moreover, baseline (s0) sNfL correlated well with all other sNfL measurements (i.e., log- and non-transformed values from follow-up visits), and with markers of inflammation, and of the functions of kidneys, liver and thyroid glands (Supplementary material 2, Supplementary Table 1). Figure 1: Serum neurofilament light chain (NfL) levels (log2-transformed) in BiDirect. Cohorts showed similar distributions of sNfL concentrations (A). There was a positive correlation of sNfL with age (B). Mean differences associated with the diagnostic group (BiDirect cohort: depression, myocardial infarction-MI-, and population-based control individuals), but not with sex, were observed (C). p < 0.001, *p < 0.0001. ## Genetic associations with sNfL We identified no genetic associations with sNfL surpassing the desired genome-wide significant threshold ($p \leq 5$ × 10−8). But, our observations reached a significance threshold commonly accepted for suggestive associations ($p \leq 1$ × 10−5) in GWASs. Therefore, we wished to further explore these suggestive findings from our GWAS and meta-analysis. With our discovery GWAS in BiDirect ($$n = 1$$,899), we observed suggestive signals in 10 chromosomes (Figure 2A). Because the SNP2GENE tool integrates observations coming from GWAS summary statistics with information on LD structure coming from well-established reference panels to define lead variants and genomic loci, and can also be used to annotate an array of functional features for SNPs within the defined loci, we considered this tool to provide an appropriate means for the interpretation of our results. Twelve suggestive genomic loci for sNfL were defined through this analysis. These loci contained 13 lead variants (i.e., identified from independent variants and independent from each other at r2 ≥ 0.1), 14 independent signals (i.e., independent variants at the suggestive p-value threshold and independent from each other at r2 ≥ 0.6), and implicated a total of 246 genetic variants and of 18 mapped genes, from which 7 (CNTNAP5, NAT1, NATP, MTDH, RIMS2, VWA8, and RBFOX1) are protein-coding (Table 2, Supplementary material 2, Supplementary Table 2). The SNP heritability estimation performed with GCTA showed that this GWAS explained about $30\%$ of the variance in sNfL (hSNP2= 0.299). However, the analysis also suggested that a larger sample size would be required to confidently detect the genetic component of sNfL (LRT = 2.4, $$p \leq 0.061$$). Figure 2: Genetic associations with serum neurofilament light chain were only identified at the suggestive level. Manhattan and quantile-quantile (QQ) plots for the discovery genome-wide association study in BiDirect ($$n = 1$$,899) (A), and its meta-analysis with ASPS-Fam ($$n = 2$$,186) (B). Yellow lines in the Manhattan plots mark the suggestive threshold for genome-wide significance ($p \leq 1$ × 10−5). TABLE_PLACEHOLDER:Table 2 Because the ASPS-Fam cohort has a small sample size and differences in its composition, in comparison with BiDirect, were evident, we chose not to seek validation of our findings in ASPS-Fam, but to use this cohort to carry out a meta-analysis with the aim to gain statistical power ($$n = 2$$,186). After performing a weighted meta-analysis and filtering out heterogeneous variants (i.e., variants with inconsistent effects), we applied again the SNP2GENE approach to extract a relevant interpretation of our results. Even with the addition of the ASPS-Fam cohort, we did not observe genomic variants reaching genome-wide significance (Figure 2B). Nevertheless, we were able to define 7 suggestive meta-analysis loci spanning 5 chromosomes, 144 variants and 8 mapped genes, including 6 protein-coding genes (ACTG2, TPRKB, DMXL1, COL23A1, NAT1, and RIMS2), that associated with sNfL levels in individuals without neurological conditions (Table 2, Supplementary material 2, 3, Supplementary Table 3, Supplementary Figures 1–7). In comparison with our discovery GWAS, meta-analysis loci represented the identification of 4 robust signals (i.e., meta-analysis loci that overlapped GWAS loci; meta-analysis loci #4–7 in chromosomes 8, 9, and 11), as well as the addition of 3 new signals (i.e., meta-analysis loci not found with the discovery GWAS; meta-analysis loci #1–3 in chromosomes 2 and 5). SNP heritability performed with LDSC in our sNfL meta-analysis was estimated to be about $5\%$ (hSNP2 = 0.0557). Nevertheless, we observed a low Chi2 statistic (mean Chi2 = 1.01) for this analysis, which may be due to the small sample size. ## Investigation of biological context The PPI network created with the protein-coding genes implicated by our meta-analysis loci was able to link $\frac{5}{6}$ (exception of NAT1) genes by the incorporation of 9 linker proteins (Figure 3). Four small clusters were defined within this network, which illustrated the differential, yet interconnected functional properties between clusters. The most prominent pathways enriched in each cluster (Supplementary material 2, Supplementary Table 4) were related to cell signaling and organization of the extracellular matrix (lilac module: ACTG2, COL23A1, FURIN, MMP13, MMP16), senescence, inflammation and cell death (green module: AKT1, TP53, TP53RK, TPRKB), glucose and insulin metabolism (magenta module: MYH9, RAB8A, RIMS2), and immune processes (olive module: DMXL1, RICTOR). These pathways showed consistency with the associations observed between sNfL and clinical variables, including not only inflammation but also those related to thyroid and renal functions, and to blood lipids (e.g., Parathyroid hormone synthesis, secretion and action-FDR = 0.0086 in lilac module-; Thyroid hormone signaling pathway-FDR = 0.0052 in green module-; Plasma lipoprotein assembly, remodeling, and clearance-FDR = 0.03 in lilac module). Additionally, network modules were enriched for distinct cellular compartments (Supplementary material 2, Supplementary Table 5), mainly: extracellular matrix and Golgi (lilac module), cytoplasm and nucleus (green module), presynaptic cytoskeleton and transport vesicles (magenta module), and the RAVE (regulator of ATPase of vacuoles and endosomes) and TORC2 (target of rapamycin complex 2) complexes (olive module). Figure 3: Protein-protein interaction network of mapped and brain expression quantitative trail loci genes implicated by the identified (meta-analysis) suggestive loci for serum neurofilament light chain. Circles denote input genes. Diamonds denote linker proteins. Colors denote network clusters, whose enrichments for pathways and gene ontology cellular compartments can be found in the Supplementary material 2, Supplementary Tables 4, 5. Because none of the variants in our GWAS reached the common threshold accepted for genome-wide significance, we also dissected these associations. For all lead variants from our meta-analysis loci (rs34523114, rs114956339, rs529938, rs73198093, rs34372929, rs10982883 and rs1842909), we found significant differences in sNfL levels from BiDirect participants with different genotypes, particularly in those individuals with two copies of the effect/minor allele (AA genotype), as compared to those homozygous for the non-effect/major allele (BB genotype) (Table 3). With the exception of rs114956339 ($$p \leq 0.0016$$), we found no interactions for sNfL measurements between the genotypes of these variants and the diagnostic group (i.e., depression, MI and control). Table 3 \| Locus \| Variant (rsID) \| Effect allele (A) \| Non-effect allele (B) \| By genotype (P, age adjust) \| By genotype-group (P, age adjust) \| AA vs. AB (P, 2–1) \| AA vs. BB (P, 2–0) \| AB vs. BB (P, 1–0) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| rs34523114 \| AT \| A \| 7.17e-05 \| 0.596 \| 0.37 \| 0.00025 \| 0.0014 \| \| 2 \| rs114956339 \| A \| G \| 3.25e-05 \| 0.00157 \| \| \| 3.3e-05 \| \| 3 \| rs529938 \| T \| C \| 4.44e-05 \| 0.18 \| 0.00036 \| 4.7e-05 \| 0.61 \| \| 4 \| rs73198093 \| C \| G \| 1.06e-05 \| 0.311 \| \| \| 1.1e-05 \| \| 5 \| rs34372929 \| A \| AT \| 2.82e-05 \| 0.945 \| 0.0015 \| 1.9e-05 \| 0.19 \| \| 6 \| rs10982883 \| C \| T \| 2.78e-05 \| 0.815 \| 0.016 \| 0.00012 \| 0.018 \| \| 7 \| rs1842909 \| G \| C \| 9.01e-06 \| 0.594 \| 0.051 \| 8.5e-06 \| 0.0038 \| Finally, we tested the associations of meta-analysis loci with clinical variables. None of these survived correction for multiple comparisons (adjP > 0.05), therefore we focused on the top signals ($p \leq 0.05$) from these tests only. At this threshold, we found evidence suggesting associations of meta-analysis loci with several clinical variables (Supplementary material 2, Supplementary Table 1). When prioritizing these by the integration of our results from genetic association and sNfL correlation tests, we identified overlaps for 18 variables from the clinical phenotypes (Table 4). These included markers of inflammation (interferon-α, and interleukins 6 and 1α), renal function (cystatin, creatinine, albumin and urea), liver and muscle function (lactate dehydrogenase and lipase), thyroid function (free thyroxine and free triiodothyronine), and blood lipids (HDL cholesterol and triglycerides). Noticeably, the index of comorbidity (which included stroke, leg thrombosis, peripheral artery disease, hypertension, MI, diabetes, depression, cancer, kidney and lung diseases, chronic arthritis, and Parkinson's disease) and gray matter volume (relative to total brain, coming from magnetic resonance imaging data) were also prioritized. Moreover, the associations with all sNfL measurements from follow-up visits remained suggested (Supplementary material 2, Supplementary Table 1). Table 4 \| BiDirect time point \| Variable label \| Instrument \| Effective N \| # Variants p < 0.05 \| sNfL Pearson p-value \| sNfL Pearson coefficient \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| dx \| Index of comorbidity \| Comorbidity \| 1899 \| 2 \| 0.0 \| 0.1889 \| \| s0 \| Gray matter volume relative total brain \| (f)MRI \| 1208 \| 7 \| 0.0 \| −0.2749 \| \| s0 \| HDL cholesterol i.S. mmol/l \| Blood lipids \| 1849 \| 26 \| 0.0014 \| 0.0741 \| \| s0 \| Triglyceride i.S. mmol/l] \| Blood lipids \| 1850 \| 3 \| 0.036 \| −0.0488 \| \| s0 \| Interleukin-6 (IL-6) i.S. pg/ml \| Inflammation \| 1880 \| 7 \| 0.036 \| −0.0483 \| \| s0 \| Interleukin-1α (IL-1α) i.S. pg/ml \| Inflammation \| 1880 \| 7 \| 0.041 \| −0.047 \| \| s0 \| Lactate dehydrogenase i.S. μkatal/l \| Liver + muscle function \| 1850 \| 38 \| 2.9e-07 \| 0.1189 \| \| s0 \| Lipase i.S. μkatal/l \| Liver + muscle function \| 1841 \| 21 \| 0.0017 \| 0.0732 \| \| s0 \| Cystatin i.S. mg/l \| Renal function \| 1842 \| 51 \| 0.0 \| 0.3109 \| \| s0 \| Creatinine i.S. μmol/l \| Renal function \| 1850 \| 30 \| 0.0 \| 0.2028 \| \| s0 \| Urea i.S. mmol/l] \| Renal function \| 1845 \| 31 \| 0.0 \| 0.197 \| \| s0 \| Albumin in serum (i.S.) g/l \| Renal function \| 1849 \| 21 \| 0.00035 \| −0.0831 \| \| s0 \| Free triiodothyronine (ft3) i.S. pmol/l \| Thyroid function \| 1792 \| 1 \| 0.00077 \| −0.0793 \| \| s4 \| Interferon-alpha (IFN-α) i.S. pg/ml \| Inflammation \| 957 \| 21 \| 0.031 \| −0.0698 \| \| s4 \| Lactate dehydrogenase i.S. μkatal/l \| Liver + muscle function \| 968 \| 26 \| 2.6e-06 \| 0.1504 \| \| s4 \| Creatinine i.S. μmol/l \| Renal function \| 970 \| 4 \| 2.5e-09 \| 0.1899 \| \| s4 \| Urea i.S. mmol/l \| Renal function \| 971 \| 4 \| 1.3e-09 \| 0.1933 \| \| s4 \| Free thyroxin (ft4) i.S. pmol/l \| Thyroid function \| 970 \| 23 \| 0.008 \| 0.0851 \| ## Discussion With the increasing interest in the clinical use of sNfL as a peripheral biomarker for the presence, progression and treatment response of neurological conditions in general, there is a need to define which biological factors contribute to physiological variations in sNfL concentrations. Previous studies have reported age, body mass index, blood volume, renal function (as measured by serum creatinine levels), hypertension and pregnancy may act as determinants of sNfL (3–6, 22). To some extent, we corroborated the association of sNfL with aging and renal function, and observed other physiological variables potentially associated with sNfL in the BiDirect study. Nevertheless, because of the small-effect interactions and overlaps at the genetic level that we observed, more studies will be necessary to clarify whether these findings may represent true determinants of serum sNfL levels or an epiphenomenon. As our primary goal was to determine genetic factors that contribute to modulate sNfL concentrations, we performed a discovery GWAS and meta-analysis study in Europeans. Although we report here the findings from both analyses, we focused on the 7 suggestive loci resulting from our meta-analysis of the BiDirect and ASPS-Fam study populations to gain some biological insights on the implicated genomic regions. Results from our network analysis and overlapping genetic associations with a set of clinical variables show consistency. These highlighted particularly important roles for inflammation, lipids, thyroid hormones and vesicular transport. We also found in the literature, for all protein-coding mapped and/or any-tissue eQTL genes for variants in all of our meta-analysis loci, functions that are relevant for neuronal development and function. As neuronal processes may impact the release of NfL into the CSF and, consequently, its dissemination into peripheral blood, we focused on identifying potential roles of our meta-analysis loci in neuronal functions. However, as suggested by our analyses, it is possible that some variants contribute to regulate sNfL levels through effects on the body's metabolism and renal clearance. In our study, NAT1, RIMS2 and DEC1 (meta-analysis loci #4–6, respectively) were the more robustly suggested candidate genes. The NAT1 (N-Acetyltransferase 1) protein forms an enzymatic complex with ARD1 (N-Alpha-Acetyltransferase 10, NatA Catalytic Subunit; NAA10 gene) that is required for neuronal differentiation and dendritic arborization [23, 24]. The product of RIMS2 (Regulating Synaptic Membrane Exocytosis 2) functions as a Rab effector involved in synaptic membrane exocytosis [25]. DEC1 (deleted in esophageal cancer 1, DELEC1), a lncRNA gene, is a candidate tumor suppressor [26], which means that it may regulate the cell cycle and other fundamental cellular processes. Moreover, meta-analysis locus #1 mapped to ACTG2 (Actin Gamma 2) and implicated TPRKB (TP53RK Binding Protein) as a brain eQTL gene. Although the ACTG2 protein primarily localizes to the cytoskeleton of enteric smooth muscle, this gene has also been found downregulated during the chemical conversion of cultured human cortical astrocytes into neurons by treatment with small molecules [27], suggesting a role for ACTG2 in neuronal development. TPRKB is a subunit of the KEOPS (Kinase, Endopeptidase and Other Proteins of small Size) complex, which is required for the threonyl carbamoyl adenosine (t6A) transfer (t)RNA modification [28]. An increasing number of reports link defects in these modifications to various neurodevelopmental disorders, suggesting a role in the development of the nervous system [29, 30]. Additionally, when looking at any-tissue eQTL effects, genetic variants in meta-analysis locus #1 were found to regulate the expression of DCTN1 (Dynactin Subunit 1) and DGUOK (Deoxyguanosine Kinase). The product of DCTN1 is essential for the retrograde transport of vesicles and organelles along microtubules mediated by dynein. In neurons, it activates retrograde axonal transport and regulates microtubule stability [31, 32]. On the other hand, DGUOK is a mitochondrial protein that may be involved in neuronal differentiation, as suggested by experiments in retinoic acid-induced differentiated neuronal-like cells [33]. Meta-analysis locus #2 mapped to DMXL1 (Dmx Like 1). In ngr1−/− mice, this gene was upregulated in axotomized corticospinal motor neurons 4 weeks after pyramidotomy [34], suggesting a role in axonal repair. Meta-analysis locus #3 mapped to COL23A1 (Collagen Type XXIII Alpha 1 Chain), whose dysregulated expression has been reported in different brain regions of mice with repeated experience of agonistic interactions [35]. The work suggested the involvement of extracellular matrix remodeling (and of COL23A1) in the development of experimental psychopathologies. Although meta-analysis locus #7 did not map to protein-coding genes or showed eQTL effects on any in the brain datasets, we found variants in this locus with any-tissue eQTL effects on PTPN5 (Protein Tyrosine Phosphatase Non-Receptor Type 5). This gene regulates synaptic plasticity, and has been implicated in diverse neurological and psychiatric disorders (36–38). We acknowledge important limitations of our study. First, the relatively small sample size limited the power to detect genetic associations at the genome-wide level and, therefore, to estimate SNP heritability. This was indeed reflected by the statistics from our heritability analyses. Second, serum samples from non-fasting study participants were used to determine sNfL concentrations. However, it is unknown if fasting status influences sNFL levels. Future assessments of sNfL levels in fasting and non-fasting blood should clarify whether this is a relevant factor for sNfL measurement. And, third, the nature of the study design of the sample populations included in the present study derived in an enrichment of individuals with depression, cardiovascular risk factors and cardiovascular disease. While most prior research focused on neurological conditions, recent studies have shown increased levels of sNfL in patients with cardiovascular or metabolic conditions and multimorbidity [39]. In fact, we also showed this to be the case in the BiDirect study. To overcome this, we adjusted for these conditions and other confounding factors, including age. We expect that this is sufficient to adequately address condition-induced biases. Finally, we did not perform analyses within each condition cohort due to their limited sample sizes. Overall, we are positive that the future inclusion of appropriate population-based cohorts will help establish these and other genomic regions as genetic drivers of sNfL variations in individuals without neurological conditions. Further bioinformatics and functional studies should help to elucidate the biological relevance of our findings for sNfL measurements. The potential genetic and physiological factors associated with sNfL that were identified by our study warrant future investigations that will pave the way for an optimal application of sNfL as a marker of neuronal conditions. ## Data availability statement The data analyzed in this study is subject to the following licenses/restrictions: The summary statistics datasets generated in this study are available from the authors on reasonable request. The derived data supporting the conclusions presented in this article are included within the article and the corresponding additional files. Requests to access these datasets should be directed to MH-R, marisol.herrera@uni-muenster.de. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Münster and the Westphalian Chamber of Physicians in Münster, North Rhine-Westphalia, Germany and the Ethics Committee of the Medical University of Graz, Austria. The patients/participants provided their written informed consent to participate in this study. ## Author contributions MH-R: project design, data analysis, interpretation, and manuscript preparation. EH, MK, and RS: GWAS in ASPS-Fam. MS and KB: project design and critical revisions. KB: coordination of the BiDirect study. HW, AM, DL, PB, and JK: measurements of NfL. All authors contributed to the article and approved the submitted version. ## Conflict of interest DL is Chief Medical Officer at GeNeuro. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1145737/full#supplementary-material ## References 1. Yuan A, Rao MV, Veeranna Nixon RA. Neurofilaments and neurofilament proteins in health and disease. Cold Spring Harb Perspect Biol. (2017) 9 a018309. DOI: 10.1101/cshperspect.a018309 2. 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--- title: Investigating the antioxidant activity enhancer effect of Cyamopsis tetragonoloba seed extract on phenolic phytochemicals authors: - Tripti Joshi - Sumit Kumar Mandal - Sonakshi Puri - Vidushi Asati - P. R. Deepa - Pankaj Kumar Sharma journal: Frontiers in Plant Science year: 2023 pmcid: PMC10030946 doi: 10.3389/fpls.2023.1131173 license: CC BY 4.0 --- # Investigating the antioxidant activity enhancer effect of Cyamopsis tetragonoloba seed extract on phenolic phytochemicals ## Abstract ### Introduction Phenolic phytochemicals are known for antioxidant-mediated pharmacological effects in various diseases (diabetes, cancer, CVDs, obesity, inflammatory and neurodegenerative disorders). However, individual compounds may not exert the same biological potency as in combination with other phytochemicals. Cyamopsis tetragonoloba (Guar), an underutilized semi-arid legume which has been used as a traditional food in Rajasthan (India), is also a source of the important industrial product guar gum. However, studies on its biological activity, like antioxidant, are limited. ### Methods We tested the effect of C. tetragonoloba seed extract to enhance the antioxidant activity of well-known dietary flavonoids (quercetin, kaempferol, luteolin, myricetin, and catechin) and non-flavonoid phenolics (caffeic acid, ellagic acid, taxifolin, epigallocatechin gallate (EGCG), and chlorogenic acid) using DPPH radical scavenging assay. The most synergistic combination was further validated for its cytoprotective and anti-lipid peroxidative effects in in vitro cell culture system, at different concentrations of the extract. LC-MS analysis of purified guar extract was also performed. ### Results and discussion In most cases, we observed synergy at lower concentrations of the seed extract (0.5-1 mg/ml). The extract concentration of 0.5 mg/ml enhanced the antioxidant activity of Epigallocatechin gallate (20 µg/ml) by 2.07-folds, implicating its potential to act as an antioxidant activity enhancer. This synergistic seed extract-EGCG combination diminished the oxidative stress nearly by double-fold when compared with individual phytochemical treatments in in vitro cell culture. LC-MS analysis of the purified guar extract revealed some previously unreported metabolites, including catechin hydrate, myricetin-3-galactoside, gossypetin-8-glucoside, and puerarin (daidzein-8-C-glucoside) which possibly explains its antioxidant enhancer effect. The outcomes of this study could be used for development of effective nutraceutical/dietary supplements. ## Introduction Legumes, or dry beans and pulses, are members of the Fabaceae family that grow in pods of annual, biennial, and perennial plants. They are not only one of the largest but also among the economically most significant families of flowering plants due to their nitrogen-fixing capacity and restoration of nitrogen-depleted soil by crop rotation (Mbagwu et al., 2011). Legumes are generally recognized for their high concentrations of bioactive components, including phenolics, phytosterols, carbohydrates, and saponins, which help lower the risk of oxidizing substances, bacteria, diabetes, inflammatory disease, and cancer (Ayilara et al., 2022; Jat et al., 2023). Numerous research endeavours have discovered the antioxidant properties of different species of legumes, and they have found a strong correlation between antioxidant potential and total phenolic content (Asati et al., 2022; Timoracká et al., 2022). At a time when one out of every five children under the age of five is chronically malnourished, legumes are now considered a future superfood capable of eradicating hunger and contributing to health (Martín-Cabrejas, 2018; Chaudhary et al., 2022). Due to their low cost and positive environmental impact, their natural bioactive compounds are currently a trend in the food processing industry (Riaz et al., 2022). As consumers become more aware of the nutritional and nutraceutical composition of legumes, their global demand continues to rise (Pham and Luan, 2021). Cyamopsis tetragonoloba (Guar), has been used traditionally for food and fodder purposes (Pankaj and Dhankar, 2023) (Figures 1A, B). It is economically important and also known as the heart of the farmer fields, as India contributes $80\%$ of the global guar gum production. Guar can be used as a laxative, digestive aid, appetizer, or cooling agent (Mukhtar et al., 2004). Potentially, guar gum can help hypercholesterolemic insulin-dependent diabetic patients with improved glycemic control and lower serum LDL-cholesterol concentrations (Vuorinen-Markkola et al., 1992). Potent phytochemicals including phenolics and flavonoids are found in the seeds (Kuravadi et al., 2012). Owing to the presence of multiple therapeutically active molecules, like quercetin, daidzein, and kaempferol, it is used as a complementary medicinal plant (Jain and Rijhwani, 2018). For example, Kaushik et al. [ 2020] reported that C. tetragonoloba could play an important role in developing inexpensive and effective anti-dengue medicine. Figure 1: (A) C. tetragonoloba plant, (B) C. tetragonoloba seeds, (C) EC50 values of individual phenolic compounds, (D) DPPH inhibition% of phenolic compounds with and without C. tetragonoloba seed extract. CT, C. tetragonoloba; QUE, quercetin; KF, kaempferol; LUT, luteolin; CAT, catechin; MYR, myricetin; Caf. A, caffeic acid; EA, ellagic acid; TAX, taxifolin; EGCG, epigallocatechin gallate; Ch. A, chlorogenic acid. Values denote mean ± SD (n=3). a, b, c, d, and e represent statistically significant different values (P ≤ 0.05) with respect to 0.5, 1, 1.5, 2, & 2.5 mg/ml concentration of CT, respectively. Plant secondary metabolites are multifunctional metabolites produced from various biochemical pathways. The biosynthesis of aromatic amino acids, tryptophan, tyrosine, and phenylalanine, which are common precursors for phenolics and nitrogen-containing compounds is initiated by the shikimate pathway (Jan et al., 2021; Elhamouly et al., 2022). As per García-Calderón et al. [ 2020], the secondary metabolites originating from the phenylpropanoid metabolism include monolignols, flavonoids and isoflavonoids, various phenolic acids, and stilbenes, which function to: protect the plants against oxidative stress and pathogens; also as chemical signals in symbiotic nitrogen fixation with rhizobia. Further, despite the important role played by the Lotus japonicus (a model legume) in elucidating the molecular genetics of legume–rhizobia symbiosis, the class of phenolic compounds used by this species in order to attract its chosen symbiont is still unknown. Species-specific differences in flavonoid accumulation have also been observed. For example, in L. japonicas, different types of abiotic stress situations (such as UV-B irradiation) resulted in an accumulation of isoflavonoids as a possible alternative to accumulation of flavonols. Phenolics are a broad class of bioactive compounds that contain at least one benzene ring and one or more hydroxyl groups. The complexity of phenolic compounds ranges from simple phenols to highly polymerized compounds (Lin et al., 2016). These compounds are differentially distributed in the cotyledon (mainly non-flavonoid phenolics) and the seed coat (flavonoids) of legumes (Amarowicz, 2020). The distinctive bioactive potential, color and flavor of legumes are due to the most abundant phenolic compound, flavonoids, which are composed of two aromatic rings linked by a 3-C bridge, in the form of heterocyclic C ring (Pham and Luan, 2021). In humans, oxidative stress is caused by an imbalance between the formation of reactive oxygen species (ROS) and the endogenous antioxidants, leading to a reaction cascade that can damage lipids, proteins, and DNA (Rudrapal et al., 2022). Antioxidants operate as scavengers of reactive free radicals, inhibiting lipid peroxidation and other related processes, and thereby protect the body from resulting diseases (Pande and Srinivasan, 2013b). The antioxidant properties of phenolic compounds are influenced by their chemical makeup. The most important aspects of flavonoids’ activity as main antioxidants are the position and degree of hydroxylation on the B ring (Kerwin, 2004). According to the reports by World Health Organisation (WHO), nearly $80\%$ of the global population depends on plant based medicines owing to their positive impact on health and lower side-effects (Adki et al., 2020; Singh and Gaikwad, 2020). There is a plethora of research stating the antioxidant potential of individual pure phytochemicals or plant extracts. However, despite knowing their excellent antioxidant activity and natural origin, studies on the biological activities of their combinations are surprisingly limited. It is reasonable to hypothesize that edible plant extracts can be used to enhance the antioxidant activity of known phytochemicals, thereby increasing their bioactive potential. The current research focuses on investigating the antioxidant activity enhancer (AAE) effect of Cyamopsis tetragonoloba seed extract on dietary flavonoids (quercetin, kaempferol, luteolin, myricetin, and catechin) and non-flavonoid phenolics (caffeic acid, ellagic acid, taxifolin, epigallocatechin gallate (EGCG), and chlorogenic acid) using DPPH radical scavenging assay and lipid peroxidation assessment in in vitro cultured cells. The total phenolic content and total flavonoid content were calculated. LC-MS analysis was performed to identify possibly novel and unreported compounds (also as potential contributors to antioxidant activity enhancement of standard phenolic phytochemicals) from the seed extract of C. tetragonoloba. ## Chemicals and reagents Quercetin, kaempferol methanol, dimethyl sulfoxide (DMSO), DPPH, Folin Ciocalteu reagent, sodium carbonate, aluminum chloride, sodium nitrite, sodium hydroxide, Amberlite XAD7HP, Sephadex LH-20 were procured from Sigma-Aldrich Chemicals Company (United States). Luteolin, catechin, myricetin, caffeic acid, ellagic acid, taxifolin, epigallocatechin gallate (EGCG), chlorogenic acid, gallic acid were obtained from Yucca Enterprises (Mumbai, India), Mouse embryonic fibroblast cells (3T3-L1) were obtained from National Centre for Cell Science (NCCS, Pune, India), Dulbecco’s Modified Eagle’s Medium (DMEM) and fetal bovine serum (FBS) were purchased from Gibco Life Technologies (Carlsbad, CA, USA), 2-Thiobarbituric acid (TBA), Trichloroacetic acid (TCA) were purchased from Sigma Aldrich. ## Plant collection and extraction Commercially available seeds of Cyamopsis tetragonoloba (guar) were purchased from a local grocery store in Pilani market (Jhunjhunu district, Rajasthan, India). ## Preparation of seed extract The seeds of Cyamopsis tetragonoloba were ground to a fine powder using a Waring blender. The fine powder was defatted with hexane in a 1:5 (w/v) ratio at room temperature for 1 hr in an orbital shaker incubator. This was followed by centrifugation at 3000xg for 10 min. The supernatant was decanted and the pellet was extracted two more times. The hydrophobic compounds were separated in the hexane extract and the pellet was dried at room temperature. This dried pellet was extracted thrice with 5 volumes of $80\%$ methanol by the same process mentioned above. The supernatant was filtered using a Whatman filter paper (No. 1). This step was repeated twice, and all the supernatants were pooled and concentrated to dryness using a rotary evaporator (Aditya Scientific, Hyderabad, India). The concentrated extracts were stored at 4°C for further analysis. ## Total phenolic content The TPC was determined by the Folin-Ciocalteu method described by (Slinkard and Singleton, 1977; Tsao et al., 2003; Wang et al., 2011) with slight modifications. Briefly, 200 µl of the seed extract was mixed with 800 µl of $7.5\%$ sodium carbonate and 1ml of the FCR (Folin-Ciocalteu Reagent). The mixture was shaken gently and incubated at room temperature for 30 min, and the absorbance was read at 765 nm. A gallic acid standard curve was prepared with different concentrations (50-250 μg/ml), and the TPC values were expressed as micrograms of gallic acid equivalents (GAE) per gram of sample. All tests were performed in triplicates. ## Total flavonoid content The TFC was calculated by the Aluminum chloride colorimetric assay described by John et al. [ 2014]. In brief, an aliquot (1ml) of extracts or standard solutions of quercetin (200-1000 μg/ml) was added in a flask containing 4 ml distilled water. To the flask was added 300 µl $5\%$ NaNO2, followed by 300 µl $10\%$ AlCl3 after five minutes. This was followed by addition of 2ml 1M NaOH, and the volume was made up to 10 ml with distilled water. The solution was shaken, and absorbance was read at 510 nm. The TFC values were expressed as mg of quercetin equivalents per g of sample. All tests were performed in triplicates. ## Estimation of antioxidant potential The antioxidant activity of plant seed extract and pure phytochemicals was tested by DPPH (2,2-diphenylpicrylhydrazyl) assay (after 2-fold dilution). For binary combinations (to test the potential enhancement of the antioxidant activity of pure phytochemicals by C. tetragonoloba seed extract), the seed extract was used in varying concentrations (0.5-2.5 mg/ml) whereas the concentration of pure compounds was kept constant (20 µg/ml). These were mixed in a 1:1 (v/v) ratio. ## DPPH free radical scavenging assay The DPPH assay was performed as described by Hidalgo et al. [ 2010]. The methanolic solution of DPPH is purple/violet colored, which fades to pale yellow in the presence of antioxidants, and the loss in absorbance is measured at 517 nm. A 100 μM DPPH solution was prepared in methanol, and 290 μl of this solution was mixed with 10 μl of individual compound/seed extract or their combinations. The reaction was carried out in a 96-well microplate, incubated in the dark at room temperature for 1hr, and absorbance was measured at 517 nm using a microplate reader (ThermoScientific Multiskan G0). The percentage DPPH radical scavenging activity was calculated by the following equation: Where Ac is the absorbance of the control and As is the absorbance of the sample. Solution without the sample (seed extract or phytochemical) was taken as control. The results were expressed as EC50 (μM) obtained by plotting a curve between concentration and inhibition percentage. EC50 is the effective concentration necessary to get $50\%$ inhibition. The lower the EC50 value, higher will be the antioxidant activity. ## Cell viability assay The non-toxic dosage of test phytochemicals - CT and EGCG, was determined by MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) based cell viability assay, which shows the metabolic activity of cells. Murine fibroblast NIH-3T3 cells (NCCS, Pune, India) were cultured at 37°C in humidified $5\%$ CO2 in Dulbecco’s modified eagle medium (DMEM) supplemented with $10\%$ (vol/vol) fetal bovine serum (FBS), penicillin G and streptomycin (100 mg/l). After the cells were confluent, cells were trypsinized from the surface of the culture flask by using a $0.25\%$ trypsin solution. The cells were plated on the cultivation flask (surface 25 cm2) at a density of 6 X 104 NIH-3T3 cells per ml medium and incubated for 24 h prior to the experiments. The NIH/3T3 cells were seeded in a 96-well (8 × 103 cells per well) and were incubated for 24 h. The test phytochemicals, CT and EGCG, were dissolved in DMSO (stock solution 10 mM), and diluted in media to a final concentration of 5 µM to 50 µM (concentration of DMSO $0.5\%$). After 24 h of incubation, 90 µl of growth medium and 10 µl MTT dye (5 mg/ml) were added to each well and incubated for an additional three hours. The MTT solution-containing media was then removed. After adding DMSO the plate was shaken gently to dissolve the formazan crystals. The absorbance was measured at 570 nm (Multiskan FC, Thermo Scientific, DE) (Ahmad et al., 2019; Mandal et al., 2022). The percentage of cytotoxicity was determined as follows: ## Lipid peroxidation assay To evaluate the effect of phytochemicals (individual and in combination) on oxidative stress, levels of malondialdehyde (MDA), a stable end product of lipid peroxidation was estimated by TBARS (thiobarbituric acid reactive substances) assay. NIH/3T3 cells without any treatment with H2O2 or phytochemicals served as the control, while the fibroblast cells exposed to 100 µM H2O2 for 6 hrs served as the oxidative stress induced model. NIH/3T3 cells were initially treated with the phytochemicals for 3h, followed by exposure to 100 µM H2O2. After 3h, the cells were treated with lysis buffer, homogenized, centrifuged at 13000 x g at 4°C for 15 minutes and the supernatant was collected. Cell lysates of each experimental group were normalized on the basis of equal amount of protein (100 µg), and incubated with 500 µl of $10\%$ trichloroacetic acid (TCA). This was followed by reacting with 750 µl of thiobarbituric acid (TBA, $1\%$ w/v) in an acidic condition, and the solution was heated in a boiling water bath for 15 minutes to generate a pink colour adduct which was measured spectrophotometrically (Multiskan FC, Thermo Scientific, DE) at 530 nm (Wenz et al., 2019). Values were expressed as µM of malondialdehyde/mg protein. Following H2O2 treatment, a significant increment in the concentration of MDA (P ≤ 0.05) was observed in the normal fibroblast cells, indicating that it served as an oxidative stress induced cell culture model. In this induced model, the phytochemicals afforded antioxidant protection to the cells and reduced the MDA levels that were closer to the normal control fibroblast cells. The levels of the biochemical marker of lipid peroxidation, MDA, was decreased by $6.54\%$, $7.17\%$, $12.32\%$, respectively in the H2O2-induced cells, treated by CT, EGCG, and their combination, respectively. It was interesting to note that the combination of CT and EGCG markedly diminished the oxidative stress nearly by a double-fold when compared with individual phytochemical treatments. This clearly indicated the anti-oxidant enhancer effect of CT on EGCG (Figure 2C). ## Purification of phenolics and flavonoids from C. tetragonoloba seed extract In order to identify potential phytocompounds in the C. tetragonoloba seed extract leading to antioxidant activity enhancement of the pure phytochemical, the seed extract was subjected to column chromatography. Purification was done by previously reported protocol with slight modifications (Asati et al., 2022). In brief, Amberlite XAD7HP chromatography followed by Sephadex LH-20 were used to purify the defatted methanol extract. 5 gm of the extract was loaded on the matrix packed in a glass column (50 cm x 1.8 cm) and equilibrated using $100\%$ methanol. Initially, $50\%$ methanol was used to elute the column, and 20 fractions of 5 mL each were collected. This was followed by elution using $100\%$ methanol. All fractions were analyzed by TLC (Thin layer chromatography) on silica gel F254 plates (Merck, USA) using Toluene: Acetic Acid: Acetone: Formic Acid::20:4:2:1 as solvent system and plates were visualized at 254 and 366 nm. The fraction exhibiting the maximum bands (C5) was further characterised using HPLC and LC-MS. ## HPLC and LC-MS analysis of the purified fraction The purified fractions were filtered by 0.45 µm Puradisc filters and HPLC (Shimadzu Corporation, Tokyo, Japan) analysis was performed for the purpose of optimization. Photo diode array detector was used; absorbance was monitored from 200 to 365 nm. The protocol reported by Song et al. [ 1998] for chromatographic separation was standardized by slight modifications. In brief, separation was performed on a C18 column (SpherisorbR, 250 mm x 4.6 mm, particle size 5 µm, Waters) with optimized mobile phase A (pH 3.0 Milli Q water) and B (Acetonitrile). Glacial acetic acid was used as pH modifier. The system was equilibrated for an hour at 1 ml/min flow rate. Initially, phase A and acetonitrile concentrations were $10\%$ and $90\%$, respectively, for the purpose of washing. After sample injection, $0.1\%$ glacial acetic acid in acetonitrile was maintained at $15\%$ for 5 minutes. Solvent B reached $35\%$ in 33 minutes and $10\%$ in 40 minutes. The solvent flow rate was l mL/min for the first 5 min, increased to 1.5 mL/min over 0.5 min and maintained for 39 min, and then returned to 1 mL/min. The C5 fraction obtained through Sephadex LH-20 chromatography (obtained as mentioned in the previous section on purification) was further analysed by LC-MS. This was performed as per Chaudhary et al. [ 2020] and Venuprasad et al. [ 2014]. Q-TOF Micromass spectrometer (Waters Corporation, Milford, MA, USA) was used. Chromatographic separation was done using Spherisorb 5 µm ODS2 column with the help of auto sampler (flow rate of 0.2 mL/min, 280 nm wavelength and 20 μL injection volume). Solvents were: (A) Formic acid ($0.1\%$ v/v) and 10 mM ammonium fluoride and (B) acetonitrile + $0.1\%$ Formic acid. Gradient (in solvent B) was: (i) $30\%$, from 0 to 15 min, (ii) $55\%$, from 15 min, (iii) $95\%$, from 25 to 45 min, and (iv) $35\%$, at 45–48 min; spray voltage 4 KV; gas temperature 325°C; gas flow 10 L/min; and nebulizer 40 psi. Electrospray mass spectra data were recorded on positive and negative ionization mode for a mass range m/z 50–m/z 1000. The instrument’s MassLynx database was used to examine the products. RIKEN-RESPECT was used to evaluate mass spectrum fragments (Sawada et al., 2012). ## Statistical analysis Experiments were done in triplicate, and the values were calculated as mean ± standard deviation. One-way analysis of variance (ANOVA) was performed to assess the statistically significant difference between the mean values. P-value ≤ 0.05 was considered statistically significant. ## Total phenolic content and total flavonoid content The antioxidant activity of plants is directly proportional to theirphenolic/flavonoid content (Joshi et al., 2022). It has been reported that phenolic compounds are best extracted with methanol ($80\%$ concentration) as compared to other solvents due to its polarity and solubility of phenolics (Moteriya, 2015). The TPC and TFC of the defatted methanolic extract of C. tetragonoloba seeds were 280 ± 9.5 mg GAE/g and 496 ± 15.2 mg QE/g of the extract, respectively. Various factors, including growth and storage conditions (climate, soil, water), and time of harvest are responsible for different phytochemical composition (Wright et al., 2001). Our research group has earlier reported the variation in total flavonoid content of P. cineraria pod extracts obtained from trees in different geographical regions (Asati et al., 2022). Sharma et al. [ 2017] reported that the TPC and TFC of different guar cultivars collected from various states of India ranges between 60.03 to 204.67 mg GAE/g and 4.26 to 12.43 mg QE/g, respectively. Thus, cultivar selection is important for functional food development from traditional plants. ## Antioxidant activity of standard phenolic compounds The antioxidant activity of standard phenolics were tested by DPPH free radical scavenging assay. EGCG (EC50- 42.69 ± 0.16 µg/ml) and ellagic acid (EC50- 45.08 ± 0.40 µg/ml) showed the maximum antioxidant activity among the 10 tested compounds (Figure 1C). The order of antioxidant activity from lowest to highest was as follows- taxifolin< kaempferol< catechin ≤ chlorogenic acid< quercetin< myricetin = caffeic acid< luteolin< ellagic acid ≤ EGCG. This result is in accordance with the trend of antioxidant activity reported by other researchers (Hirano et al., 2001; Hidalgo et al., 2010). It is believed that the gallate group at position 3 plays the most crucial role in their ability to scavenge free radicals, with an extra hydroxyl group inserted at position 5’ in the B ring also contributing to their scavenging capabilities (Heim et al., 2002; Braicu et al., 2011). Arrangement and number of hydroxyl moieties on the ring, presence of catechol group in the B ring, and 2, 3 double bonds in the C ring, are some characteristics that strongly correlate with antioxidant potential. According to Freeman et al. [ 2010], these groups can also be used to find the reduction potentials, as a molecule with lower reduction potential has more tendency to donate its electron and act as a strong antioxidant. The results are in accordance with these reports; Quercetin, myricetin, and luteolin showed almost similar antioxidant activities because of almost similar structure (a catechol group in the B ring and a 2, 3 double bond in the C ring (Freeman et al., 2010)) while EGCG has the lowest reduction potential, thereby showing maximum activity. ## Antioxidant activity enhancer effect of C. tetragonoloba seed extract on standard phenolic phytochemicals C. tetragonoloba seed extract was used in combination with 10 phenolic compounds to test for possible synergism (antioxidant activity enhancement). The DPPH radical scavenging activity of the pure compounds with and without the seed extract were compared (Figure 1D). It was observed that with increase in the concentration of extract from 0.5-2.5 mg/ml, the DPPH % inhibition also increases. Table 1 shows the percentage inhibition of the combinations at different concentrations and the types of interaction. An interaction can be said to be synergistic when the experimental value is greater than the theoretical value (calculated by summing up the inhibition percentage pertaining to antioxidant activity of individual phytochemicals and seed extracts), additive when the experimental and theoretical values are equal; and when experimental value is less than the theoretical value, it is an antagonistic interaction. Table 1 \| Combinations \| Unnamed: 1 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.1 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.2 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.3 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.4 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.5 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.6 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.7 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.8 \| DPPH Inhibition % at different concentrations (mg/ml) ± STDEV.9 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Combinations \| \| 0.5 \| \| 1 \| \| 1.5 \| \| 2 \| \| 2.5 \| \| \| QUE + CT \| E \| 30.78 ± 1.81* \| Syn \| 39.67 ± 0.46* \| Syn \| 57.55 ± 1.84 \| Ad \| 69.02 ± 0.76* \| An \| 86.55 ± 0.15* \| An \| \| \| T \| 25.94 ± 0.39 \| Syn \| 37.53 ± 1.33 \| Syn \| 55.68 ± 2.11 \| Ad \| 72.19 ± 2.57 \| An \| 89.28 ± 2.40 \| An \| \| KF + CT \| E \| 17.90 ± 1.55* \| Syn \| 33.49 ± 1.67* \| Syn \| 52.06 ± 2.04* \| Syn \| 60.22 ± 1.36 \| Ad \| 73.25 ± 0.67* \| An \| \| \| T \| 14.30 ± 1.19 \| Syn \| 25.89 ± 1.25 \| Syn \| 44.04 ± 1.93 \| Syn \| 60.55 ± 1.32 \| Ad \| 77.64 ± 2.71 \| An \| \| LUT + CT \| E \| 16.40 ± 0.55* \| An \| 33.59 ± 2.91* \| An \| 44.94 ± 2.98* \| An \| 69.20 ± 1.14* \| An \| 84.64 ± 0.77* \| An \| \| \| T \| 26.99 ± 0.24 \| An \| 38.58 ± 0.99 \| An \| 56.73 ± 2.15 \| An \| 73.24 ± 2.17 \| An \| 90.33 ± 2.61 \| An \| \| CAT + CT \| E \| 16.74 ± 1.31 \| Ad \| 32.59 ± 1.67 \| Ad \| 48.52 ± 1.10 \| Ad \| 66.26 ± 1.12 \| Ad \| 84.87 ± 0.38* \| Syn \| \| \| T \| 18.24 ± 1.43 \| Ad \| 29.83 ± 2.04 \| Ad \| 47.98 ± 0.96 \| Ad \| 64.49 ± 2.25 \| Ad \| 81.58 ± 1.66 \| Syn \| \| MYR + CT \| E \| 25.33 ± 2.16* \| Syn \| 41.14 ± 0.64* \| Syn \| 55.98 ± 0.69* \| Syn \| 71.68 ± 3.77 \| Ad \| 80.51 ± 1.09* \| An \| \| \| T \| 20.75 ± 0.94 \| Syn \| 32.34 ± 1.58 \| Syn \| 50.49 ± 1.45 \| Syn \| 67.00 ± 2.12 \| Ad \| 84.09 ± 2.02 \| An \| \| Caf. A + CT \| E \| 24.31 ± 1.04* \| Syn \| 40.61 ± 1.36* \| Syn \| 57.01 ± 2.07* \| Syn \| 76.04 ± 1.90* \| Syn \| 86.72 ± 1.20 \| Ad \| \| \| T \| 21.18 ± 0.51 \| Syn \| 32.77 ± 0.69 \| Syn \| 50.92 ± 2.32 \| Syn \| 67.43 ± 1.82 \| Syn \| 84.52 ± 2.88 \| Ad \| \| EA + CT \| E \| 35.89 ± 1.33* \| Syn \| 43.74 ± 2.37* \| Syn \| 61.92 ± 2.57* \| Syn \| 73.97 ± 3.71 \| Ad \| 86.15 ± 0.70* \| An \| \| \| T \| 24.39 ± 1.94 \| Syn \| 35.98 ± 2.39 \| Syn \| 54.13 ± 0.81 \| Syn \| 70.64 ± 2.14 \| Ad \| 87.73 ± 1.77 \| An \| \| TAX + CT \| E \| 12.06 ± 0.88* \| An \| 31.88 ± 0.80 \| Ad \| 51.49 ± 1.42* \| Syn \| 63.07 ± 0.97 \| Ad \| 85.36 ± 0.62* \| Syn \| \| \| T \| 18.01 ± 2.05 \| An \| 29.60 ± 2.75 \| Ad \| 47.75 ± 0.35 \| Syn \| 64.26 ± 2.88 \| Ad \| 81.35 ± 0.99 \| Syn \| \| EGCG + CT \| E \| 41.58 ± 1.89* \| Syn \| 62.67 ± 2.50* \| Syn \| 70.91 ± 1.60* \| Syn \| 83.82 ± 1.03* \| Syn \| 88.20 ± 0.69* \| Syn \| \| \| T \| 22.37 ± 2.74 \| Syn \| 33.96 ± 3.54 \| Syn \| 52.11 ± 0.83 \| Syn \| 68.62 ± 3.74 \| Syn \| 85.71 ± 0.20 \| Syn \| \| Ch. A + CT \| E \| 14.28 ± 1.09* \| An \| 33.57 ± 1.51 \| Ad \| 55.50 ± 1.93* \| Syn \| 71.09 ± 2.38 \| Ad \| 85.77 ± 0.44 \| Ad \| \| \| T \| 21.46 ± 1.84 \| An \| 33.05 ± 2.78 \| Ad \| 51.20 ± 1.83 \| Syn \| 67.71 ± 3.76 \| Ad \| 84.80 ± 1.44 \| Ad \| As seen from Figure 1D, when the seed extract (varying concentrations) was added to the phenolic compounds (20 µg/ml), most of the combinations showed synergistic antioxidant effect at lower concentrations of extract (0.5-1 mg/ml), while on further increasing the extract concentration, additive (at 1.5 mg/ml) and antagonistic (at 2-2.5 mg/ml) interactions were observed. The results agreed with the previous report that phytochemicals need to be combined in specific ratios to show synergistic effect (Joshi et al., 2022). When the extract concentration was 0.5 mg/ml, the DPPH % inhibition of extract with EGCG was $41.58\%$, which was 4-folds and 2.07-folds higher than that of seed extract ($10.37\%$) and EGCG ($20\%$). All the concentrations of extract showed synergistic interaction with EGCG. Therefore, this synergistic seed extract-EGCG combination was further validated for its cytoprotective and anti-lipid peroxidative effects in in vitro cell culture system. Similar results were reported by Zhang et al. [ 2016], that mulberry leaf polysaccharides (MLPs) can be used as antioxidant activity enhancers of flavonoids. It was reported that despite having low antioxidant activity themselves, MLPs showed synergistic interaction with flavonoids. In another study, combination of C. tetragonoloba with garlic and capsaicin (responsible for pungent flavor of red pepper), offer a significant increase in the antioxidant status (Pande and Srinivasan, 2013a; Pande and Srinivasan, 2013b). Romano et al. [ 2009] reported the antioxidant activity enhancing effect of rosemary extract on synthetic antioxidants (butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT)). This could give the food sector a strong reason to combine natural and synthetic antioxidants in processed food products to increase storage stability and prevent any potential hazardous effects from using excessive levels of antioxidants. Flavonoids (quercetin, kaempferol, and isorhamnetin) present in the almond skin have been shown to act synergistically with vitamin E and C (Chen et al., 2005). In another study, the combination of longan peel extract (LP), vitamin E, and ascorbyl palmitate (derivative of ascorbic acid) lowered the free radicals in tuna oil, contributing to the antioxidant effect; thus, LP could have an application as a food additive against lipid oxidation in oils (Rakariyatham et al., 2021). This study also offered mechanistic insights into antioxidant synergy among phytochemicals. As C. tetragonoloba and the tested phytochemicals (polyphenolic compounds) have been used for their antioxidant property for many years, the current study suggests potential usage of their combination in order to achieve a greater therapeutic effect. These findings may be helpful for people who want to increase their antioxidant intake – without compromising on safety - as well as for the development of novel medications and functional foods with higher antioxidant potential. There are different hypotheses for possible mechanisms responsible for the above-mentioned interactions. Synergistic interactions could be due to- a) the regeneration of strong antioxidants by the weaker ones (Marinova et al., 2008), b) formation of stable intermolecular adducts with strong antioxidant activity (Olszowy, 2020), c) the type and concentration of antioxidant (Shi et al., 2007). Hypotheses for antagonistic interactions are- a) regeneration of weaker antioxidants by stronger antioxidants, b) polymerization of antioxidants decreases their activity, c) disappearance of free antioxidant radicals due to irreversible reactions. Further studies need to be conducted to validate the specific mechanism for antioxidant synergism observed between the tested phytochemicals and C. tetragonoloba seed extract in the current study. ## Non-cytotoxic dosage of phytochemicals determined in NIH/3T3 cells The viability of the normal fibroblast cells (NIH/3T3) was assessed by MTT assay to evaluate the non-cytotoxic dosage range for CT and EGCG, at various concentrations. The safe dosage of CT, and EGCG was considered to be the concentration at which at least $80\%$ of cells were viable (non-toxic dosage). After 24 hours incubation, the test phytochemicals showed dose-dependent decrease in cell viability, wherein CT and EGCG revealed $80\%$ cell viability upto a dosage of 20 µg/ml (Figures 2A, B). Figure 2: (A, B) represent the dose dependent changes in cell viability of normal fibroblast cells exposed to different concentrations of CT and EGCG, respectively. Values denote mean ± SD of three experiments performed in triplicate. (C) shows the reduction in oxidative stress (lipid peroxidation) in the H2O2 induced fibroblast cells treated with phytochemicals (20 µg/ml of both CT and EGCG). Values denote mean ± SD of two experiments done in triplicate. The oxidative stress mimic (H2O2 group) and the treated groups were compared with the normal control group where statistically significant difference was expressed at P ≤ 0.05 (denoted by ) (GraphPad Prism v8.0.2).* ## LC-MS analysis of the enriched and purified fraction C5 obtained from C. tetragonoloba The fractions eluted out of Sephadex LH-20 column were analysed by TLC on Silica gel F254 plates (Supplementary Material, Figure S2). One fraction, i.e. C5, was chosen for mass spectrometric analysis. LC-MS analysis was carried out in order to identify the phytochemicals in CT seed extract with a possible role in interaction with the standard phenolic phytochemicals. In order to retain maximum structural information, a constant value of collision energy was given to each compound for obtaining mass spectra with different fragmentation patterns. The different m/z values were analysed using the RIKEN ReSPect database which is extensively designed to study the mass spectra of the plant based secondary metabolites. The gradient flow method was chosen to separate the flavonoids in liquid chromatography. The significant peaks eluted out had the retention times of 3.98 min, 18.0 min, and 19.65 min respectively (Figure 3). These peaks were then subjected to ESI-MS full scan mode analyses in order to identify the protonated ions. The individual m/z spectra are given in Supplementary Material (Figure S3). Figure 3: LC-MS chromatogram of purified fraction (C5) of C. tetragonoloba seeds obtained after Sephadex LH-20 chromatography. Major peak 1 eluted out at 3.98 minutes showed fragments with m/z of values 305.1310 indicating the presence of dihydroflavonol catechin hydrate. The second major peak, which was eluted out at 18.00 minutes showed the fragment with m/z observed at 481.472 indicated the presence of flavonols, Myricetin-3-Galactoside and Gossypetin-8-glucoside. The third major peak at 19.65 minutes indicated the presence of Puerarin which is daidzein 8-C-glucoside belonging to isoflavonoid class and was identified via the m/z fragments of 268.0568 and 297.0857. The retention time, chemical formula and mass of individual compounds are given in the Supplementary Material (Table S1). In the present work, it has been seen that in comparison to the extract alone, both the C. tetragonoloba seed extract and kaempferol showed high fold increase in the antioxidant activity when combined at lower concentrations (Figure 1). This could be due to the presence of catechin and myricetin in the seed extract. We assume that the aglycone version of the identified compounds could be involved in synergistic interactions due to available and reactive hydroxyl groups. Many flavonoids are present as aglycones and convert to glycosides as the fruit matures on the plant. Again, upon consumption, the glycosides in food are cleaved into aglycones which are often more bioactive than the glycosylated versions. However, antioxidant synergy with flavonoid glycosides, such as quercetin-3-glucoside, has also been reported (Hidalgo et al., 2010). The authors mentioned that kaempferol showed synergistic interaction with catechin and myricetin. Here, the chromatogram obtained was according to the polarity of the compounds eventually detected by MS analysis. The analysis showed that the methanolic extract contains only one isoflavonoid viz. puerarin, which was present in its glycosylated form. At relatively less abundance, as observed from the MS count ion, of Vitexin, which is a flavone, was also detected in the extract. The neutral ion losses from the different compounds indicated that the phytochemical constituents in the pods were present in glycosylated as were their aglycone forms. Kobeasy and El-salam [2011] investigated the major flavonoids present in the C. tetragonoloba collected from the Egypt region and showed the presence of luteolin and quercetin in aqueous extracts of seeds. Additionally, Morris and Wang [2017] explored the potential of C. tetragonoloba L. beans as a good source of kaempferol and quercetin in different cultivars grown in Georgia, USA under laboratory conditions. To summarize, in the current study, a novel attempt at using C. tetragonoloba seed extract to enhance antioxidant activity of polyphenolic phytochemicals (of dietary importance) was made using both cell-free and cell culture systems, followed by purification of phenolics/flavonoids from the seeds (along with detailed phytochemical characterization using LC-MS technique). A representative graphical abstract of the overall work done is given in Supplementary Material (Figure S1). ## Conclusion The current study sought to investigate the antioxidant activity enhancer effect of an edible desert legume, Cyamopsis tetragonoloba. Although the seeds of this plant have been used as a commercial source of guar gum, they remain relatively underutilized as sources of nutraceuticals. Furthermore, negligible work has been done towards sourcing antioxidant activity enhancers (AAE) from such edible legumes. The post-COVID era has witnessed a boost in the global nutraceutical industry. Consumers have developed preference towards nutraceuticals and dietary supplements obtained from plants due to lower toxicity. On the basis of the results of this study, it could be concluded that phenolic compounds present in C. tetragonoloba seed extract can interact with other compounds (standard phytochemicals) and act as antioxidant activity enhancers. These interactions can be synergistic, additive, or antagonistic, based on various characteristics, like chemical structures, availability of hydrogen ions, type of antioxidant assay used, concentrations, and combination ratios. The results obtained support our hypothesis of edible legumes as a host for a variety of natural antioxidant activity enhancers. Furthermore, the use of legumes as food ingredients and nutraceuticals is extremely promising for developing functional foods with positive health effects, often attributed to the antioxidant potential. The use of edible legume plants growing in the wild in Indian (semi) arid regions in formulating these nutraceuticals can be beneficial from both economic and environmental aspects as these plants are capable of growing on marginal and less fertile lands, and do not need heavy application of water or fertilizers. Being edible and safe, the seed extract of C. tetragonoloba can be used in food industry as an antioxidant activity enhancer. Those plant cultivars which are less useful as sources of guar gum (yield- or quality-wise), or even degummed seeds or guar gum industrial waste, could be attractive candidates for the same. The current study would potentially pave the way for more such research towards desert plants as sources of antioxidants/antioxidant activity enhancers, as well as mechanistic elucidation of biological activities of specific ‘plant extract- phytochemical’ combinations. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Author contributions PD and PS contributed to conception and design of the study. TJ, SM, SP and VA performed the experiments. TJ wrote the first draft of the manuscript. SM, SP and VA wrote sections of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1131173/full#supplementary-material ## References 1. Adki K., Laddha A., Gaikwad A., Kulkarni Y.. (2020) 365-391. DOI: 10.1016/B978-0-12-818553-7.00026-7 2. 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--- title: Antiretroviral drugs efavirenz, dolutegravir and bictegravir dysregulate blood-brain barrier integrity and function authors: - Chang Huang - Tozammel Hoque - Reina Bendayan journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10030948 doi: 10.3389/fphar.2023.1118580 license: CC BY 4.0 --- # Antiretroviral drugs efavirenz, dolutegravir and bictegravir dysregulate blood-brain barrier integrity and function ## Abstract The implementation of combined antiretroviral therapy (cART) significantly reduces the mortality associated with human immunodeficiency virus (HIV) infection. However, complications such as HIV-associated neurocognitive disorders (HAND) remain a major health concern. We hypothesized that the toxicity of antiretroviral drugs (ARVs) may contribute to the pathogenesis of HAND in addition to cerebral viral infection. To address this question, we evaluated the impact of HIV integrase strand transfer inhibitors (dolutegravir and bictegravir), and a non-nucleoside reverse transcriptase inhibitor (efavirenz) on the integrity and permeability of various human and mouse blood-brain barrier (BBB) models, in vitro, ex vivo and in vivo. We observed a significant downregulation of tight junction proteins (TJP1/Tjp1, OCLN/Ocln and CLDN5/Cldn5), upregulation of proinflammatory cytokines (IL6/Il6, IL8/Il8, IL1β/Il1β) and NOS2/Nos2, and alteration of membrane-associated transporters (ABCB1/Abcb1a, ABCG2/Abcg2 and SLC2A1/Slc2a1) mRNA expression, in vitro, in human (hCMEC/D3) and primary cultures of mouse microvascular endothelial cells, and ex vivo in isolated mouse brain capillaries treated with efavirenz, dolutegravir, and/or bictegravir. We also observed a significant increase in BBB permeability in vivo following treatment with the selected ARVs in mice applying NaF permeability assay. Taken together, these results suggest that clinically recommended integrase strand transfer inhibitors such as dolutegravir may exacerbate HIV-associated cerebrovascular pathology, which may contribute to the associated short-term neuropsychiatric side effects and the high incidence of mild forms of HAND reported in the clinical setting. ## Introduction Implementation of combined antiretroviral therapy (cART) has significantly reduced the mortality associated with human immunodeficiency virus (HIV) infection and increased the life expectancy of people living with HIV (PLWH) (Günthard et al., 2016). However, the long-term use of these drugs has been reported to induce neurological and psychiatric adverse effects with variable frequency and severity (Abers et al., 2014; Bertrand et al., 2021). In particular, HIV-associated neurocognitive disorders (HAND) have become a major complication of HIV and great health concern (Animut et al., 2019; Wang et al., 2020). Depending on severity, HAND has been categorized in three stages: HIV-associated dementia (HAD), asymptomatic neurocognitive impairment (ANI), and minor neurocognitive disorder (MND). Although the incidence of HAD, characterized by severe cognitive impairment and motor dysfunction has remarkably declined since the implementation of cART, the milder forms such as ANI and MND continue to occur and are even on the rise (Elbirt et al., 2015; Clifford, 2017). The blood-brain barrier (BBB) is the major physiological barrier that separates the brain parenchyma from the systemic circulation and plays a central role in maintaining central nervous system (CNS) homeostasis (Xu et al., 2019; Kadry et al., 2020). This barrier is mainly composed of microvascular endothelial cells (EC) that are continuously sealed by intercellular tight junction proteins (TJs) and adherent junction complexes (AJ) which strictly restrict the paracellular diffusion of small and larger molecules (Varatharaj & Galea, 2017; Bors & Erdö, 2019; Xu et al., 2019). TJ proteins primarily include: occludin (OCLN) and claudin-5 (CLDN-5) as transmembrane proteins, and zonula occludens -1 (ZO-1) as accessory protein for anchoring TJ protein complex to the actin cytoskeleton (Fanning et al., 2002; Greene et al., 2019). Pericytes and astrocytes foot processes which are embedded in the basement membrane of the EC cells are important in maintaining BBB function by providing structural and metabolic support to the microvasculature (Abbott et al., 2006; Armulik et al., 2010). In addition to the physical characteristics, the BBB displays very effective biochemical barrier properties by expressing multiple drug efflux transporters and metabolic enzymes (Dauchy et al., 2008). Previous data demonstrated that the brain microvascular endothelial cells express a variety of membrane transporters belonging to the ATP-binding cassette (ABC) and Solute Carrier Families (SLC). In particular, the efflux transporters, P-glycoprotein (P-gp) and Breast Cancer Resistant protein (BCRP) are robustly expressed at the apical membrane of brain capillary endothelial cells as “gatekeepers” of the BBB by preventing harmful substances from entering the brain parenchyma from the blood circulation (Lee et al., 2001; Bendayan et al., 2002). As glucose is the primary metabolic fuel for the mammalian brain, a continuous and highly regulated supply is critical to maintaining normal brain function (Shah et al., 2012). Glucose uptake into the brain is primarily mediated by the facilitative glucose transporter Glut-1 expressed by brain microvascular endothelial cells, and its regulation is essential for normal CNS function (Devraj et al., 2011; Koepsell, 2020). Taken together, an intact BBB is essential to maintain normal CNS function and its dysregulation is known to be associated with several neurodevelopmental and neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease (Erickson & Banks, 2013). BBB disruption occurs early during HIV infection and is not significantly improved by the implementation of cART, as Rahimy et al. reported an elevated ratio of albumin level in the cerebrospinal fluid (CSF) to that in serum among PLWH receiving antiretroviral drugs (ARVs) (Louboutin & Strayer, 2012; Rahimy et al., 2017; Bertrand et al., 2019). An increased BBB permeability is a critical contributor to HAND pathogenesis as its disruption facilitates the CNS infiltration of free virions, infected or uninfected macrophages and leukocytes as well as ARVs from the periphery, resulting in an escalated susceptibility of inflammatory assault and toxicity in the brain (Atluri et al., 2015; Chaganti et al., 2019). Based on molecular mechanisms and resistance profiles, ARVs are classified in six major pharmacological classes: nucleoside reverse transcriptase inhibitors (NRTI); non-nucleoside reverse transcriptase inhibitor (NNRTI); integrase strand transfer inhibitor (INSTI), protease inhibitor (PI); fusion inhibitors and coreceptor antagonist (Arts & Hazuda, 2012; Pau & George, 2014). Current regimen for HIV treatment generally consists of two NRTIs administered in combination with a third active ARV drug from one of three drug classes: INSTIs, NNRTI, or PIs with a pharmacokinetic enhancer (Hirsch, 2008). Although side effects are inevitable for every drug, it is crucial to ensure that the anti-HIV drugs do not aggravate any pathological conditions caused by HIV infection. However, a cohort study on HIV positive stable patients demonstrated that the discontinuation of ARVs significantly improved neurocognitive function, with no neurocognitive improvements observed with cART re-implementation among those who had developed neurocognitive impairment (Robertson et al., 2010). Although limited, emerging clinical evidence suggests that ARVs-mediated toxicity in the CNS may play a critical role in the pathogenesis of HAND (Bertrand et al., 2021). Efavirenz (EFV), a NNRTI, has long been considered a first-line therapy owing to its potency and efficacy in viral suppression and immune function restoration (Bengtson et al., 2018). However, its clinical use has been discouraged in most developed countries as a result of various neurological and neuropsychiatric adverse reactions, including insomnia, dizziness, depression and psychosis (Gutiérrez et al., 2005; van de Wijer et al., 2016; Qin et al., 2020). While the underlying mechanisms for this toxicity are not fully understood, previous literature suggests its potential in impairing BBB by inducing endoplasmic reticulum (ER) stress, mitochondrial and autophagy dysfunction (Apostolova et al., 2015). Dolutegravir (DTG), an INSTI, is the current World Health Organization (WHO) recommended preferred first- and second-line treatment for PLWH (Estill & Bertisch, 2020). It is commonly prescribed in combination with a two-drug backbone of tenofovir alafenamide and emtricitabine or abacavir and lamivudine (Estill & Bertisch, 2020). However, the reported rate of DTG discontinuation due to neuropsychiatric adverse events was significantly higher than other INSTIs-based cART, reaching as high as $6\%$ within 12 months of treatment in the general population and even higher ($18\%$) amongst women and older patients (Hoffmann et al., 2017). With a positive correlation between DTG plasma-trough concentrations and CNS side effects reported in a Japanese population, the safety of using DTG has gained emerging concern, especially amongst PLWH with pre-existing neuropsychiatric disorders (Cailhol et al., 2017; Yagura et al., 2017; Yombi, 2018). Bictegravir (BTG), a recently approved second-generation INSTI that was structurally derived from DTG is primarily recommended as a first-line cART alternative (Stellbrink et al., 2020). The most common side effects reported with the use of BTG include nausea and headache (Hill et al., 2018). Although limited, Hoffmann et al. reported a comparable rate of neuropsychiatric effects between BTG and DTG, suggesting a potential pharmacological class effect of INSTI (Hoffmann et al., 2021). While studies are suggesting a CNS toxicologic potential of INSTI, the underlying mechanism is poorly understood. The present study aimed to investigate the effect of DTG and BTG in inducing structural and metabolic dysfunction of the BBB using human and mouse BBB model systems in vitro, ex vivo and in vivo. ## Reagents/materials All cell culture reagents were obtained from Invitrogen (Carlsbad, CA, United States), unless indicated otherwise. Real-time quantitative polymerase chain reaction (qPCR) reagents, including reverse transcription cDNA kits and qPCR TaqMan primers, were purchased from Applied Biosystems (Foster City, CA, United States) and Life Technologies (Carlsbad, CA, United States), respectively. All buffers were purchased from Sigma-Aldrich. Ficoll (Polysucrose 400) was purchased from BioShop. PluriStrainer 30 μm were purchased from PluriSelect Life Science. ## Cell cultures Immortalized human cerebral microvessel endothelial cell line (hCMEC/D3), an established model of human BBB (Weksler et al., 2013) was generously provided by P.O. Couraud (Institut Cochin, Department Biologie Cellulaire and INSERM, Paris, France); primary cultures of mouse microvascular endothelial cells were kindly provided by Dr. Isabelle Aubert (University of Toronto, ON, Canada). hCMEC/D3 cells (passage 27–39) were cultured in Endothelial Cell Basal Medium-2 (Lonza, Walkersville, MD, USA), supplemented with vascular endothelial growth factor, insulin-like growth factor 1, epidermal growth factor, fibroblast growth factors, hydrocortisone, ascorbate, GA-1000, heparin, and $2.5\%$ fetal bovine serum (FBS) and grown on rat tail collagen type I-coated plates. Primary mouse (C57BL/6) brain microvascular endothelial cells were cultured (passage 2-5) in complete Mouse Endothelial Cell Medium (Cell Biologics Inc., Chicago, Illinois, United States), supplemented with vascular endothelial growth factor, endothelia cell growth supplements, heparin, epidermal growth factor, hydrocortisone, L-glutamine, antibiotic-Antimycotic Solution, and $5\%$ FBS, and grown on gelatin-coated plates. All cell lines were maintained in a humidified incubator at 37°C with $5\%$ CO2 and $95\%$ air atmosphere with fresh medium replaced every 2–3 days. Cells were sub-cultured with $0.25\%$ trypsin-EDTA upon reaching $95\%$ confluence. ## Mouse brain capillary isolation Brain capillaries were isolated from male C57BL/6 (10–12 weeks old) mice purchased from Charles River Laboratories (Laval, QC, Canada) as described previously (Chan and Cannon, 2017). Briefly, animals were anesthetized by isoflurane inhalation and decapitated once a deep anesthetic surgical plane was achieved. Brains were collected immediately, cortical gray matter was removed and homogenized in ice-cold isolation buffer (phosphate-buffered saline (PBS) containing calcium, magnesium, and supplemented with 5 mM glucose and 1 mM sodium pyruvate). Ficoll solution ($30\%$ final concentration) was added to the brain homogenates, mixed vigorously and centrifuged at 5,800 g for 20 min at 4°C. The resulting pellet of capillaries was re-suspended in isolation buffer supplemented with $1\%$ bovine serum albumin (BSA) and filtered through a 300 μm nylon mesh. The filtrate containing the capillaries was passed through a 30 μm pluriStrainer and washed with 50 mL isolation buffer containing $1\%$ BSA. Capillaries were harvested with 50 mL ice-cold isolation buffer and centrifuged at 1,600 g for 5 min. The resulting pellet containing the capillaries was snap-frozen in liquid nitrogen and kept at −80°C until further analysis. All experiments, procedures, and animal care were conducted in accordance with the Canadian Council on Animal Care guidelines and approved by the University of Toronto Animal Care Committee. ## Gene expression analysis The mRNA expression of specific genes of interest was quantified using qPCR. Total RNA was isolated from cell samples (hCMEC/D3, primary mouse BBB cells) and isolated mouse brain capillaries using TRIzol reagent (Invitrogen) and treated with DNase I to remove contaminating genomic DNA. RNA concentration (absorbance at 260 nm) and purity (absorbance ratio $\frac{260}{280}$) was assessed using NanoDrop One Spectrophotometer (Thermo Scientific). A total amount of 2 μg of RNA was then reverse transcribed to cDNA using a high-capacity reverse transcription cDNA kit (Applied Biosystems) according to the manufacturer’s instructions. Specific human/mouse primer pairs for TJP1/Tjp1 (ZO-1/Zo-1; Hs01551861_m1/Mm01320638_m1), OCLN/Ocln (OCLN/Ocln; Hs00170162_m1/Mm00500912_m1), CLDN5/Cldn5 (CLDN5/Cldn5; Hs00533949_s1/Mm00727012_s1), IL6/Il6 (IL6/Il6; Hs00174131_m1/Mm00446190_m1), IL1β/Il1β (IL1β/Il1β; Hs01555410_m1/Mm00434228_m1), NOS2/Nos2 (iNOS/inos; Hs01075529_m1/Mm00440485_m1),ABCB1/Abcb1a (P-gp/P-gp; Hs00184500_m1/Mm00440761_m1), ABCG2/Abcg2 (BCRP/Bcrp; Hs01053790_m1/Mm00496364_m1), SLC2A1/Slc2a1 (Glut-1/Glut-1; Hs00892681_m1/Mm00441480_m1), and human primer pairs for CXCL8 (IL8; Hs00174103_m1) were designed and validated by Life Technologies for use with TaqMan qPCR chemistry. All assays were performed in triplicates with the housekeeping gene for human/mouse cyclophilin B (PPIB/Ppib; Hs00168719_m1/Mm00478295_m1) as an internal control. For each gene of interest, the critical threshold cycle (CT) was normalized to cyclophilin B using the comparative CT method. The difference in CT values (ΔCT) between the target gene and cyclophilin B was then normalized to the corresponding ΔCT of the vehicle control (ΔΔCT) and expressed as fold expression (2−ΔΔCT) to assess the relative difference in mRNA expression for each gene. ## ARVs treatment Confluent hCMEC/D3 or primary mouse brain microvascular endothelial cell monolayers at 1.0 × 106/well grown on 6-well plates were treated with either DMSO (vehicle control) or EFV (7,500, 10,000 ng/mL) or DTG (2,000, 5,000 ng/mL) or BTG (3,000, 6,000 ng/mL) for a period of 6, 24 or 48 h at 37°C. Freshly isolated mouse brain capillaries resuspended in 10 mL isolation buffer were also exposed to DMSO (vehicle control), EFV (10,000 ng/mL) or DTG (5,000 ng/mL) or BTG (6,000 ng/mL) for 5 h at room temperature. Doses of EFV, DTG and BTG were carefully chosen to correspond to human therapeutic plasma levels (Cottrell et al., 2013; Hill et al., 2018). At the desired time interval, treated cells or brain capillaries were harvested using TRIzol lysis buffer and subsequently processed for gene analyses. Cell viability was assessed in hCMEC/D3 cells treated with ARVs using a standard MTT assay previously described by our laboratory (Whyte-Allman et al., 2017). Following the treatment with ARVs for 24 h, cells were incubated for 2 h at 37°C with 5 mg/mL MTT solution in PBS. The formazan content in each well was dissolved in DMSO and quantified by UV analysis at 580 nm using a SpectraMax 384 microplate reader (Molecular Devices, Sunnyvale, CA). Cell viability was assessed by comparing the absorbance of cellular reduced MTT in ARV-treated cells to that of vehicle (DMSO)-treated cells. ## In vivo ARVs treatment and peanut butter pellet habituation in C57BL/6 mice Wild-type male C57BL/6 (10-12-week-old) mice were purchased from Charles River Laboratories (Laval, QC, Canada). EFV or DTG or BTG in powder form was added in peanut butter (PB) (Kraft Canada Inc.) and mixed by hand with a spatula for 10 min to make a homogenous suspension. PB pellets (100.5 ± 1.5 mg in weight) were made to formulate the concentration of drug that was equivalent to EFV at 10 mg/kg/day or DTG at 5 mg/kg/day or BTG at 5 mg/kg/day. Doses of EFV, DTG and BTG were chosen to achieve human therapeutic plasma levels, such that a dose of EFV of 5 mg/kg/day, DTG of 5 mg/kg/day and BTG of 5 mg/kg/day for 14 days will yield a peak plasma concentration of ∼3,000 ng/mL, ∼5,000 ng/mL and ∼6,000 ng/mL, respectively at steady sate (Kala et al., 2018; Mohan et al., 2021; Mohan et al., 2022). Frozen pellets were then stored at −80°C until use. Each mouse was single housed and was introduced to the taste of PB once daily for 5 consecutive days prior to the initiation of the animal study. After 5 days training, the average pellet consumption time was ≤1 min. During the treatment, each mouse was dosed once daily at 10:00 a.m. with one regular pellet (vehicle control) or drug pellet for 14 days. At 24 h following the last PB pellet administration, mice were subjected to NaF assay for BBB permeability measurement or capillary isolation for gene expression measurement of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 and Il1β. ## Sodium fluorescein (NaF) BBB permeability assay NaF solution was prepared on the day of the experiment. Briefly, NaF powder (Sigma-Aldrich) was diluted in $0.9\%$ saline to reach a concentration of 30 mg/mL. Each animal received 100 µL (120 mg/kg) of NaF solution through intra-peritoneal (i.p.) injection and was subject to anesthesia after 20 min (Roszkowski & Bohacek, 2016). Blood samples (600 µL) were collected from the right ventricle before intracardial perfusion with 30 mL of PBS solution. Meninges and choroid plexuses were removed, and the brains were collected. Each brain was homogenized in 2 mL of PBS and vortexed for 2 min after the addition of 2 mL of $60\%$ trichloroacetic acid (Sigma-Aldrich) to precipitate proteins. Homogenized samples were kept in the cold room (4°C) for 30 min and centrifuged at 18,000 g at 4°C for 10 min. Fluorescence was measured at an excitation wavelength at 440 nm and emission wavelength at 525 nm using spectrophotometer (Chai et al., 2014). The cerebral extraction ratio (CER) was calculated as ([tissue florescence]/[g brain])/([serum florescence]/[ml blood]) × 100 = CER%. ## Data analysis All experiments were repeated at least three times using cells obtained from different passages or different mouse brain capillary preparations. Each data point from a single experiment represents triplicate measurements. For in vivo experiments, samples were collected from 5 to 6 animals per treatment group. Results are presented as mean ± SEM. All statistical analyses were performed using Prism 6 software (GraphPad Software Inc., San Diego, CA, United States). Statistical significance between two groups was assessed by two-tailed Student’s t-test for unpaired experimental values. Multiple group comparisons were performed using one-way analysis of variance (ANOVA) with Bonferroni’s post hoc test. $p \leq 0.05$ was considered statistically significant. ## Effect of EFV on TJ proteins, pro-inflammatory cytokines and transporters in hCMEC/D3 cells To examine the effect of EFV on TJ proteins and transporters expression, inflammatory and oxidative stress responses, hCMEC/D3 cells were exposed to EFV at 7,500 and 10,000 ng/mL for 6, 24 and 48 h. TJP1 and OCLN mRNA expression were downregulated by ∼$40\%$ and ∼$25\%$, following 6 h exposure to 7,500 and 10,000 ng/mL EFV, respectively. CLDN5 mRNA expression was reduced by ∼$25\%$ and ∼$60\%$ with 7,500 and 10,000 ng/mL EFV following 6 h exposure, respectively. EFV (7,500 ng/mL) exposure for 24 h resulted in a ∼$25\%$ and ∼$50\%$ downregulation of ABCB1 and ABCG2 mRNA expression, respectively. A $25\%$ downregulation of SLC2A1 mRNA expression was also observed at 24 h following EFV exposure at 10,000 ng/mL. Pro-inflammatory cytokines and NOS2 mRNA expression were significantly upregulated following 24 and 48 h exposure to EFV. Notably, a robust induction of IL6 mRNA expression (∼5 folds) was observed after 24 h EFV exposure at 10,000 ng/mL. In addition, a significant upregulation of IL1β (∼15 folds at 7,500 ng/mL; ∼40 folds at 10,000 ng/mL) and NOS2 (∼7 folds at 7,500 ng/mL; ∼9 folds at 10,000 ng/mL) mRNA expression was observed following 48 h EFV exposure. IL8 mRNA expression was also upregulated following 48 h EFV treatment (∼1.5 folds at 7,500 ng/mL; ∼2.5 folds at 10,000 ng/mL) (Figure 1). An MTT assay was performed to verify that the EFV treatment did not significantly alter cell viability (data not shown). FIGURE 1: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and NOS2 in hCMEC/D3 cells exposed to EFV (7,500, 10,000 ng/mL) or vehicle (DMSO) control for 6/24/48 h mRNA expression of TJP1, OCLN, CLDN5, ABCB1, ABCG2, SLC2A1, IL6, IL1β, IL8 and NOS2 in hCMEC/D3 cells exposed to EFV relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping human cyclophilin B gene from n = 4 independent experiments. One-way ANOVA with Bonferroni’s post-hoc test, , p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. ## Effect of EFV on TJ proteins, proinflammatory cytokines and transporters in primary cultures of mouse brain microvascular endothelial cells and isolated mouse brain capillaries To examine the effect of EFV on TJ proteins and transporters expression, inflammatory and oxidative stress responses, primary cultures of mouse brain microvascular endothelial cells were exposed to EFV at 7,500 and 10,000 ng/mL for 6 and 24 h. Exposure to EFV (6 h) with 7,500 ng/mL and 10,000 ng/mL downregulated Tjp1 mRNA expression by ∼$25\%$ and ∼$40\%$, respectively, and downregulated Ocln mRNA expression by ∼$20\%$ and ∼$50\%$, respectively. Similarly, a significant (∼$25\%$) downregulation of Cldn5 mRNA expression was also observed after EFV exposure at 10,000 ng/mL for 6 h. In addition, Abcb1a mRNA expression was significantly reduced by ∼$50\%$ following 6 h 10,000 ng/mL EFV treatment. 6 h exposure to EFV resulted in ∼$25\%$ (7,500 ng/mL) and ∼$50\%$ (10,000 ng/mL) reduction in Abcg2 mRNA expression and resulted in a ∼3 folds (7,500 ng/mL) and ∼5 folds (10,000 ng/mL) increase in Slc2a1 mRNA expression. No significant change was observed in the mRNA expression of Il1β or Nos2. However, Il6 mRNA expression was robustly upregulated by ∼5 folds and ∼25 folds following 7,500 and 10,000 ng/mL EFV treatment after 24 h, respectively (Figure 2A). FIGURE 2: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to EFV (7,500, 10,000 ng/mL) and vehicle (DMSO) control for 6 or 24 h and in isolated mouse brain capillaries exposed to EFV (10,000 ng/mL) and vehicle (DMSO) control for 5 h (A) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1, Il6, Il1β and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to EFV relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 4 independent experiments. (B) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 and Il1β in isolated mouse brain capillaries treated for 5 h with EFV (10,000 ng/mL) relative to vehicle (DMSO) was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 3 independent experiments, where each experiment contained pooled brain tissues from 6 animals per group. One-way ANOVA with Bonferroni’s post-hoc test (A), unpaired two-tailed Student’s t-test (B): , p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. The effect of EFV was additionally investigated in isolated mouse brain capillaries, a robust ex vivo model of the BBB. Treatment of mouse brain capillaries with 10,000 ng/mL EFV for 5 h showed over $50\%$ decrease in Tjp1, Ocln and Cldn5 mRNA expression and an upregulation (∼2.5 folds) of Il1β mRNA expression. A robust decrease (more than $50\%$) in mRNA expression of Abcb1a, Abcg2 and Slc2a was also observed following the same treatment condition (Figure 2B). ## Effect of DTG treatment on TJ proteins, proinflammatory cytokines and transporters in hCMEC/D3 cells To examine the effect of DTG on TJ proteins and transporters expression, inflammatory and oxidative stress responses, hCMEC/D3 cells were exposed to DTG at 2000 and 5,000 ng/mL for 6, 24 and 48 h mRNA expression of TJP1, OCLN, CLDN5, drug efflux transporters ABCB1, ABCG2 and SLC2A1 remained unchanged following exposure to DTG at both doses at 6 or 24 h. However, DTG exposure (5,000 ng/mL) significantly induced IL6 (∼10 folds) mRNA expression after 24 h, and significantly upregulated IL1β (∼4 folds), IL8 (∼7 folds) and NOS2 (∼6 folds) mRNA expression after 48 h (Figure 3). An MTT assay was performed to verify that the DTG treatment did not significantly alter cell viability (data not shown). FIGURE 3: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and NOS2 in hCMEC/D3 cells exposed to DTG (2,000, 5,000 ng/mL) or vehicle (DMSO) control for 6/24/48 h mRNA expression of TJP1, OCLN, CLDN5, ABCB1, ABCG2, SLC2A1, IL6, IL1β, IL8 and NOS2 in hCMEC/D3 cells exposed to DTG relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping human cyclophilin B gene from n = 4 independent experiments. One-way ANOVA with Bonferroni’s post-hoc test, , p < 0.05; *, p < 0.001; *, p < 0.0001. ## Effect of DTG on TJ proteins, proinflammatory cytokines and transporters in primary cultures of mouse brain microvascular endothelial cells and isolated mouse brain capillaries To examine the effect of DTG on TJ proteins and transporter expression, inflammatory and oxidative stress responses, primary cultures of mouse brain microvascular endothelial cells were exposed to DTG at 2,000 and 5,000 ng/mL for 6 and 24 h. Significant and robust decreases in Tjp1 (∼$70\%$), Ocln (∼$65\%$), and Cldn5 (∼$80\%$) mRNA expression were observed following 6 h DTG exposure at 5,000 ng/mL. Although the data did not reach significance, a trend of downregulation for Tjp1, Ocln and Cldn5 mRNA expression was observed at 2000 ng/mL for 6 h. In addition, 24 h treatment of DTG significantly downregulated Abcb1a mRNA expression by ∼$50\%$ at 5,000 ng/mL mRNA expression of Abcg2 was also robustly downregulated by ∼$20\%$ and ∼$70\%$ by 2,000 and 5,000 ng/mL DTG after 24 h, respectively. However, only 2,000 ng/mL DTG exposure led to a modest but significant upregulation (∼$25\%$) in Slc2a1 mRNA expression after 24 h. No significant change was observed for the mRNA expression of Il1β or Nos2. However, Il6 mRNA expression was robustly upregulated by ∼15 folds following 5,000 ng/mL DTG treatment after 24 h (Figure 4A). FIGURE 4: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to DTG (2000, 5,000 ng/mL) and vehicle (DMSO) control for 6 or 24 h and in isolated mouse brain capillaries exposed to DTG (5,000 ng/mL) and vehicle (DMSO) control for 5 h (A) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1, Il6, Il1β and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to DTG relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 4 independent experiments. (B) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 and Il1β in isolated mouse brain capillaries treated for 5 h with DTG (5,000 ng/mL) relative to vehicle (DMSO) was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 3 independent experiments, where each experiment contained pooled brain tissues from 6 animals per group. One-way ANOVA with Bonferroni’s post-hoc test (A), unpaired two-tailed Student’s t-test (B): , p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. Treatment of mouse brain capillaries with 5,000 ng/mL DTG for 5 h showed a modest but significant decrease in Tjp1 (∼$15\%$) mRNA expression and an upregulated Il1β (∼1.7 folds) mRNA expression, whereas Ocln and Cldn5 mRNA expression remained unchanged. A significant decrease in Abcb1a (∼$25\%$) was also observed following DTG treatment. No significant change was observed for Abcg2 or Slc2a1 mRNA expression (Figure 4B). ## Effect of BTG treatment on TJ proteins, proinflammatory cytokines and transporters in hCMEC/D3 cells To examine the effect of BTG on TJ proteins and transporters expression, inflammatory and oxidative stress responses, hCMEC/D3 cells were exposed to BTG (3,000 and 6,000 ng/mL) for 6 and 24 h. The mRNA expression of TJP1, OCLN, CLDN5, and the drug efflux transporter ABCG2 remained unchanged. BTG exposure (3,000 and 6,000 ng/mL) for 24 h mildly but significantly downregulated both ABCB1 and SLC2A1 mRNA expression by ∼$20\%$. IL6 mRNA expression was upregulated by ∼3 folds following 24 h BTG exposure at 3,000 but not 6,000 ng/mL, whereas IL1β, IL8 and NOS2 mRNA expression remained unchanged (Figure 5). FIGURE 5: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and NOS2 in hCMEC/D3 cells exposed to BTG (3,000, 6,000 ng/mL) or vehicle (DMSO) control for 6 or 24 h mRNA expression of TJP1, OCLN, CLDN5, ABCB1, ABCG2, SLC2A1, IL6, IL1β, IL8 and NOS2 in hCMEC/D3 cells exposed to BTG relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping human cyclophilin B gene from n = 4 independent experiments. One-way ANOVA with Bonferroni’s post-hoc test, , p < 0.05; *, p < 0.01. ## Effect of BTG on TJ proteins, proinflammatory cytokines and transporters in primary cultures of mouse brain microvascular endothelial cells and isolated mouse brain capillaries To examine the effect of BTG on TJ proteins and transporters expression, inflammatory and oxidative stress responses, primary cultures of mouse brain microvascular endothelial cells were exposed to BTG at 3,000 and 6,000 ng/mL for 6 and 24 h Tjp1 mRNA expression was mildly but significantly downregulated by ∼$20\%$ following 24 h exposure to 6,000 ng/mL BTG. No significant changes were observed for Ocln or Cldn5 mRNA expression. In addition, a mild but significant decrease (∼$25\%$) in Abcg2 mRNA expression was observed following 24 h exposure to 6,000 ng/mL BTG treatment, whereas Abcb1a and Slc2a1 expression remained unchanged. No significant dysregulation was observed for gene expression of pro-inflammatory cytokines or Nos2 following 24 h BTG treatment (Figure 6A). FIGURE 6: mRNA expression of TJ proteins, transporters, proinflammatory cytokines and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to BTG (3,000, 6,000 ng/mL) and vehicle (DMSO) control for 6 or 24 h and in isolated mouse brain capillaries exposed to BTG (6,000 ng/mL) and vehicle (DMSO) control for 5 h (A) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1, Il6, Il1β and Nos2 in primary cultures of mouse brain microvascular endothelial cells exposed to BTG relative to vehicle (DMSO) control was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 4 independent experiments. (B) mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 and Il1β in isolated mouse brain capillaries treated for 5 h with BTG (6,000 ng/mL) relative to vehicle (DMSO) was assessed by qPCR. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 3 independent experiments, where each experiment contained pooled brain tissues from 6 animals per group. One-way ANOVA with Bonferroni’s post-hoc test (A), unpaired two-tailed Student’s t-test (B): , p < 0.05; **, p < 0.001. Treatment of mouse brain capillaries with 6,000 ng/mL BTG for 5 h showed a significant (∼$30\%$) decrease in Tjp1 mRNA expression while Ocln and Cldn5 mRNA expression remained unchanged. A ∼$50\%$ upregulation of Abcb1a mRNA expression was observed, while Abcg2, Slc2a1 and Il1β mRNA expression remained unchanged (Figure 6B). ## EFV and DTG, but not BTG, increase BBB permeability in vivo To assess whether the downregulated mRNA levels of TJ proteins following ARVs exposure would lead to an increased BBB permeability in vivo, C57BL/6 mice were orally treated with EFV (10 mg/kg/day), DTG (5 mg/kg/day) or BTG (5 mg/kg/day) for 14 days, followed by an i. p. injection of NaF (120 mg/kg) that was allowed to circulate for 20 min. Mice were then euthanized, and blood was collected through cardiac puncture before perfusion. Brains were carefully removed and processed for NaF quantification. Treatment with EFV induced a significant increase (∼$50\%$) in NaF levels in the brain, whereas DTG increased NaF levels by ∼$75\%$. Although a trend in increased NaF level was also observed with BTG exposure compared to vehicle control, the difference did not reach significance. These results demonstrate that the disruption of TJ integrity caused by EFV and DTG in the in vitro and ex vivo models have functional implications on BBB permeability in vivo (Figure 7). FIGURE 7: Quantification of NaF level in the mouse brain. Mice were treated for 14 days with either vehicle (regular PB pellet), DTG (5 mg/kg/day), BTG (5 mg/kg/day) or EFV (10 mg/kg/day). Diffusion of sodium fluorescein (NaF) from plasma into the brain parenchyma was used as the indicator of BBB permeability. PB = peanut butter. Data are mean ± SEM, expressed as fold change compared to vehicle, n = 5 per group. One-way ANOVA with Bonferroni’s post-hoc test: , p < 0.05; ***, p < 0.0001. ## Effect of chronic exposure of DTG and BTG on mRNA expression of TJ proteins, drug efflux transporters, glucose transporter and selected proinflammatory cytokines in brain capillaries isolated from C57BL/6 mice treated with DTG or BTG for 14 days To investigate ARVs-induced BBB disruption, we next sought to quantify mRNA expression of TJ proteins, drug efflux transporters, glucose transporter and proinflammatory cytokines, in the context of ARVs treatment in vivo. C57BL/6 mice were treated with PB pellets containing DTG (5 mg/kg/day) or BTG (5 mg/kg/day), or with the vehicle control (regular PB pellet) for 14 days, and brain capillaries were isolated. Gene expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 or Il1β in isolated brain capillaries was normalized to the housekeeping gene Ppib, and to the expression in vehicle-treated mice. Amongst TJ protein markers, DTG induced a significant decrease in Tjp1 (∼$10\%$) and Ocln (∼$45\%$) but not Cldn5 mRNA expression. BTG, similarly, downregulated Tjp1 (∼$10\%$) and Ocln (∼$20\%$) but not Cldn5 mRNA expression. Additionally, we observed a significant decrease in Abcb1a (∼$40\%$) and Abcg2 (∼$40\%$) mRNA expression by DTG treatment. BTG treatment exerted a similar but milder decrease in Abcb1a (∼$25\%$) and Abcg2 (∼$25\%$) mRNA expression. Slc2a1 expression was not affected by either DTG or BTG. Interestingly, Il1β mRNA expression was induced by DTG (∼2 folds), but not by BTG (Figure 8). FIGURE 8: mRNA expression of TJ proteins, membrane associated transporters and proinflammatory cytokine in mouse brain capillaries. The mRNA expression of Tjp1, Ocln, Cldn5, Abcb1a, Abcg2, Slc2a1 or Il1β genes was assessed by qPCR in brain capillaries isolated from mice treated with DTG (5 mg/kg/day), BTG (5 mg/kg/day) or vehicle (regular PB pellet) for 14 days. Results are presented as mean relative mRNA expression ±SEM normalized to the housekeeping mouse cyclophilin B gene from n = 6 animals per group. One-way ANOVA with Bonferroni’s post-hoc test: , p < 0.05; , p < 0.01; , p < 0.001; **, p < 0.0001. ## Discussion Despite the implementation of cART which has successfully transformed HIV-1 infection from a deadly disease to a treatable chronic condition, HIV remains incurable with the life-long burden of ARVs intake (Elbirt et al., 2015). HAND, manifested by the alteration of cognitive, behavioural, motor and autonomous functions, is one of the major health complications among PLWH on cART (Clifford, 2017). HAND has been mainly attributed to limited viral suppression due to the ineffective penetration of ARVs into the brain (Atluri et al., 2015; Saylor et al., 2016). However, the full neuropathogenesis of HAND is not completely understood. Penetration-Effectiveness (CPE) rank, developed by pharmacokinetics’ characteristics of ARVs, is commonly used to estimate the efficacy of ARVs in the CSF (Letendre et al., 2008; Mukerji et al., 2018). Although ARVs with good CNS penetration, as indicated by a high CPE score, are generally more effective in controlling CSF viral replication, these ARVs are associated with poorer neurocognitive performance (Caniglia et al., 2014; Beardsley & Le, 2015; Mukerji et al., 2018). Taking consideration of such evidence, we hypothesized that the use of ARVs is associated with CNS toxicity, particularly by inducing BBB dysfunction. Herein, we applied in vitro experiments using cultures of human brain microvessel endothelial cells (hCMEC/D3) and primary cultures of mouse brain microvascular endothelial cells as well as ex vivo experiments using isolated mouse brain capillaries and in vivo mouse experiments to investigate whether ARVs induce BBB dysfunction that may ultimately contribute to the development of HAND. INSTI based cART are the current preferred first line clinical regimen in most clinical settings. DTG, an INSTI with the highest CPE score [4] is associated with various neuropsychiatric side effects and a relative high discontinuation rate in the clinic (Cailhol et al., 2017; Povar-Echeverría et al., 2021). Therefore, we focused on investigating the potential DTG-mediated toxicity in various BBB models. To further address whether a pharmacological class effect could occur, BTG, a second generation INSTI that was structurally derived from DTG, was also examined in the current study. EFV is a NNRTI which has been removed from the first line regimens in the United States but continues to be used as a cost-effective option in many developing countries (Bengtson et al., 2018). EFV was widely implicated in mild-to-severe neuropsychiatric adverse events and neurocognitive impairment (Gutiérrez et al., 2005; Qin et al., 2020). A considerable number of in vitro and in vivo experiments have underlined its high risk in CNS toxicity such as neurotoxicity and BBB dysfunction (Apostolova et al., 2015). Therefore, EFV was used as a positive control in our study. The concentrations of ARVs applied in this study were carefully chosen to correspond to the therapeutically relevant plasma concentrations reported in human subjects (Villani et al., 1999; Elliot et al., 2016; Rigo-Bonnin et al., 2020). Brain endothelial cells play a central role in the formation of BBB and TJs constitute the most important junctional complex in regulating the paracellular diffusion of small molecules. Our results demonstrated that all three ARVs (EFV, DTG, BTG) at therapeutically relevant plasma concentrations downregulated the mRNA expression of at least one TJ protein in our in vitro human and mouse BBB models, and ex vivo, in isolated mouse brain capillaries. Our gene expression data using EFV, corroborates previous findings by others demonstrating EFV can alter CLDN5/Cldn5 expression and increase microvessel endothelial permeability in human and mouse BBB models (Bertrand et al., 2016). In addition, we showed that acute EFV exposure downregulated gene expression of two most relevant drug efflux transporters, ABCB1 and ABCG2 in vitro human and mouse BBB cell models and ex vivo mouse brain capillaries. While EFV is neither considered a substrate nor an inhibitor of P-gp (Dirson et al., 2006), studies in rats have shown that it is a substrate and inhibitor of BCRP in gastrointestinal epithelial cells (Peroni et al., 2011). In the current study, we found EFV exerted a downregulatory effect on ABCG2 mRNA expression at the BBB. Inconsistency of gene expression of glucose transporter and proinflammatory cytokines was observed in our BBB models exposed to EFV. We observed a downregulation of SLC2A1 mRNA expression in human BBB cells and mouse brain capillaries, but upregulation in primary mouse BBB cells. Furthermore, EFV mediated robust upregulation of mRNA expression of proinflammatory cytokines (IL6, IL8, IL1β) and oxidative stress marker (NOS2) in the human BBB cells, but only induced Il6 mRNA expression in primary mouse cells and Il1β in isolated mouse brain capillaries. Species differences, incubation time points and model-specific response could play a role in the observed inconsistency. In comparison to EFV, our gene expression data illustrates a greater potential of DTG in disrupting TJs in primary cultures of mouse microvascular endothelial cells, reflected by the downregulation of the mRNA expression of three TJ proteins by up to $80\%$. Similar to EFV, DTG robustly induced gene expression of proinflammatory cytokines (IL6, IL8, IL1β) and oxidative stress marker (NOS2) in hCMEC/D3 cells, but with only Il6 being robustly induced in primary cultures of mouse brain microvascular endothelial cells, and Il1β being mildly upregulated in mouse brain capillaries. Our results are consistent with data of a previous study performed by Ma et al., who reported that DTG significantly downregulated TJP1, CLDN5 and JAM-2 gene expression in bovine brain microvascular endothelial cells, and increased mRNA levels of proinflammatory cytokines TNFα and IL1β in the cell culture supernatants (Ma et al., 2020). However, the DTG dose (25 μM/∼10,480 ng/mL) used in that study exceeded the clinically relevant plasma concentrations observed in human (up to ∼5,000 ng/mL) and may explain why certain changes were not observed in our hands. In addition, we observed a robust downregulation of the efflux drug transporters, Abcb1a and Abcg2 mRNA expression in the primary cultures of mouse brain microvascular endothelial cells following DTG treatment. Previous studies reported DTG being a substrate and inhibitor of P-gp and BCRP in vitro, in intestinal epithelial cells (Cottrell et al., 2013; Reese et al., 2013). Although such inhibitory effects were not observed at clinically relevant concentrations (Reese et al., 2013), potential drug-drug interactions between DTG and co-administered medications that are substrates or inhibitors of P-gp or BCRP remain to be investigated. Furthermore, although DTG pharmacokinetics were not significantly altered in the clinic by the co-administration of lopinavir/ritonavir which are known P-gp and BCRP inhibitors (Song et al., 2011), an in vitro study using bovine BBB model showed a potential synergistic toxic effect in impairing BBB integrity with the administration of DTG in combination with the P-gp inhibitor sertraline, a selective serotonin reuptake inhibitor, that is commonly used as antidepressant therapy (Ma et al., 2020). Considering depression and other psychiatric disorders are common co-morbidities among PLWH, drug-drug interactions involving drug efflux transporters remain a concern, as any increased penetration of ARVs or psychiatric medication can potentially result in elevated microglial activation and neurotoxicity that may lead to cognitive impairment (Wu et al., 2017). In comparison to DTG, which robustly altered TJ proteins in primary cultures of mouse brain microvascular endothelial cells, acute exposure to BTG, had a minor effect on mRNA expression of Tjp1, Ocln, Cldn5, and proinflammatory cytokines (Il6, Il1β, Nos2). We additionally observed a BTG-mediated decrease in ABCB1 and SLC2A1 mRNA expression in hCMEC/D3 cells, a downregulated Abcg2 gene expression in mouse cells and an upregulated gene expression of Abcb1a in isolated mouse brain capillaries. BTG has been reported to be a substrate of P-gp and BCRP, however, the clinical relevance of these transporters interactions remains unknown (Deeks, 2018). To our knowledge, we are the first research group to report any effect of BTG on TJ proteins, membrane-associated transporters, and inflammatory markers at the BBB in vitro, ex vivo and in vivo. In brief, our in vitro and ex vivo data illustrate that the acute exposure of EFV and DTG significantly, disrupted TJ proteins and induced inflammatory response in in vitro and ex vivo BBB models. Unexpectedly, DTG elicited a stronger effect than EFV, which was considered a positive control for its well-known induced CNS toxicity. Herein, we also conducted animal studies to further investigate whether the observed in vitro changes have in vivo implications. Considering that most ARVs are taken in the long term, we treated animals for a prolonged time period to mimic chronic exposure to ARVs before experimental assessment. To mimic the oral administration of ARVs in most clinical settings and to avoid the detrimental effects of repetitive oral gavage in mice, we implemented an alternative method of oral administration by formulating ARVs into PB pellets. Considering the duration of treatment and species-specific differences in pharmacokinetics, doses of ARVs were carefully selected to yield human therapeutic plasma concentrations in mice (Osborne et al., 2020; Mohan et al., 2022). Our NaF permeability assay demonstrated that 14-day chronic treatment with EFV or DTG, in mice, significantly increased the permeability of the BBB in vivo, reflected by a ∼$40\%$ and ∼$70\%$ increase in brain NaF level, respectively, suggesting a greater potential of DTG in compromising BBB integrity than EFV. In contrast, BTG treatment did not induce a significant effect on BBB permeability in these animals. Our mRNA expression data on isolated brain capillaries collected from mice treated with DTG or BTG for 14 days demonstrated a dysregulation of TJ gene expression. DTG elicited a stronger effect than BTG in downregulating both Tjp1 and Ocln, potentially explaining the greater effect of DTG in increasing BBB permeability we observed from the NaF assay. Chronic exposure to DTG or BTG for 14 days significantly decreased the expression of drug efflux transporters, and DTG upregulated Il1β gene expression, suggesting their potential in disrupting BBB functionality in addition to its integrity. Notably, our positive control EFV data was consistent with a previous study performed by Bertrand et al., in which a ∼$30\%$ increase in BBB permeability was observed in mice treated with EFV for 14 days, along with a robust decrease in Cldn5 protein expression in isolated mouse brain capillaries (Bertrand et al., 2016). Interestingly, our data showed that chronic exposure to DTG or BTG disrupted BBB primarily by targeting ZO-1 and OCLN, suggesting a potential differential mechanism from EFV-mediated BBB disruption which predominantly targeted CLDN5. In general, our data from acute exposure of ARVs in vitro and ex vivo BBB models provided similar responses in vivo, following a prolonged exposure time. However, the ARVs-mediated changes of glucose transporter expression were only observed in vitro, and further studies need to be performed to validate these findings in vivo. Our in vivo NaF assay data confirmed a more deleterious effect of DTG than EFV in increasing BBB permeability and our gene data revealed a similar effect of DTG and BTG in disrupting the physical and physiological function of BBB by altering TJ proteins and membrane associated transporters, suggesting a pharmacological class effect of INSTIs. However, the deleterious effect on BBB permeability was more robust for DTG than BTG treatment, indicating a potential safer toxicological profile of BTG in the CNS. A very limited number of studies on BTG-induced CNS toxicity are currently available except for a few clinical reports. Hoffmann et al. concluded that BTG-based regimen leads to a comparable neuropsychiatric adverse event-related discontinuation rate but some favourable patient-reported outcomes in randomized clinical trials compared with DTG-based regimen (Hoffmann et al., 2021). Another study which enrolled 24 HIV + individuals with HIV-related CNS impairment on BTG-based cART indicated a significantly higher BTG concentration in cerebrospinal fluid among patients aged over 51 years old (Gelé et al., 2021), suggesting a potentially elevated risk of BTG-mediated toxicity among the aged population. To date, to the best of our knowledge, we are the first laboratory to report the effect of BTG in the CNS using in vitro, ex vivo and in vivo BBB models. Our study presents some limitations. The CNS concentration of ARVs was not quantified in the mouse model due to the limited volume of cerebrospinal fluid. Although the ARV doses were chosen to yield a human equivalent concentration in the plasma based on previous studies in rodents (Kala et al., 2018; Mohan et al., 2021; Mohan et al., 2022), complementary pharmacokinetic studies would be valuable to further validate the findings. In summary, our studies demonstrated the potential of ARVs in altering the functionality of the BBB by inducing inflammation and drug/nutrient transporter changes in addition to structural impairment. A compromised BBB facilitates viral entry of free virions and infected monocyte-macrophages, leading to enhanced cerebral viral infection (Atluri et al., 2015; Elbirt et al., 2015; Clifford, 2017). The activation of neighbouring resident microglia and astrocytes in response to viral infection is often followed by a robust secretion of proinflammatory cytokines and neuronal toxins, which as a result, leads to severe brain damage (Tavazzi et al., 2014). In addition, the inflammatory response may also facilitate a positive feedback loop, aggravating the BBB impairment by enhancing cellular trafficking, solute permeability and altering signal transduction cascades such as NF-κB, prostaglandin E2 synthesizing-enzymes, cyclooxygenase-2 and microsomal prostaglandin E synthase 1 (Wilhelms et al., 2014; Galea, 2021). Furthermore, a compromised BBB also facilitates the brain entry of ARVs, resulting in elevated drug exposure and risk of toxicity in the brain parenchyma which can cause progressive neuronal damage (Apostolova et al., 2015). Taken together, the BBB disruption, associated inflammation and drug toxicity can have an additive or synergistic negative effect on cognitive function among PLWH, and may contribute to the high frequency of short-term neuropsychiatric adverse events associated with cART (Gutiérrez et al., 2005; Yombi, 2018; Hoffmann et al., 2021), and in a long term, high incidence of ANI and MND observed in the clinic (Elbirt et al., 2015; Clifford, 2017). By studying the first line INSTIs and EFV, our study revealed a comparable potency of DTG and EFV but not BTG in inducing inflammation and disrupting integrity and functionality of the BBB. These findings suggest a safer CNS toxicological profile of BTG compared to DTG and EFV. Further studies are required to understand the underlying toxicological mechanisms exerted by these drugs in order to minimize the risk of BBB-mediated CNS complications. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. ## Ethics statement The animal study was reviewed and approved by University of Toronto Animal Care Committee. ## Author contributions CH and TH performed the experimental work. CH, TH, and RB verified the underlying data. RB conceived the study and directed the research. CH, TH, and RB drafted the manuscript. All authors have read and approved the final version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Abbott N. J., Rönnbäck L., Hansson E.. Astrocyte-endothelial interactions at the blood-brain barrier. Nat. Rev. Neurosci. (2006) 7 41-53. DOI: 10.1038/nrn1824 2. Abers M. S., Shandera W. X., Kass J. S.. Neurological and psychiatric adverse effects of antiretroviral drugs. CNS Drugs (2014) 28 131-145. DOI: 10.1007/s40263-013-0132-4 3. Animut M. 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--- title: “Community health workers bring value and deserve to be valued too:” Key considerations in improving CHW career advancement opportunities authors: - Julie Smithwick - Jenesha Nance - Sarah Covington-Kolb - Ashley Rodriguez - Mike Young journal: Frontiers in Public Health year: 2023 pmcid: PMC10030954 doi: 10.3389/fpubh.2023.1036481 license: CC BY 4.0 --- # “Community health workers bring value and deserve to be valued too:” Key considerations in improving CHW career advancement opportunities ## Abstract ### Introduction Community health workers (CHWs) are critical members of the public health workforce, who connect the individuals they serve with resources, advocate for communities facing health and racial inequities, and improve the quality of healthcare. However, there are typically limited professional and career building pathways for CHWs, which contribute to low wages and lack of career advancement, further resulting in turnover, attrition, and workforce instability. ### Methods The Center for Community Health Alignment (CCHA), within the Arnold School of Public Health at the University of South Carolina, utilized a mixed-method data collection strategy to provide a more in-depth understanding of this issue and ways that employers, advocates, and CHWs can address it. ### Results Themes across data sources emphasized the importance of retaining skilled and experienced CHWs and educating other health professions about CHWs' critical roles, and reported that doing so will result in decreased attrition professional growth, and improved program quality. CHWs and allies concluded that higher wages, valuing lived experience over formal education, and participation in additional training opportunities should be the primary factors considered for career advancement. ### Discussion Utilizing input from experienced CHWs and CHW allies nationally, this article describes the importance of supporting CHW career advancement, shares best practices, and suggestions for designing strategies that organizations/employers can use to improve CHW career pathways to better support the CHW workforce and reduce attrition. ## 1. Introduction There is growing recognition of how community health workers (CHWs) make significant impacts on the health and wellbeing of individuals and communities most affected by inequities (1–8). The American Public Health Association (APHA) definition of a CHW, endorsed by the National Association of CHWs (NACHW), is “a frontline public health worker who is a trusted member of and/or has an unusually close understanding of the community served. This trusting relationship enables the worker to serve as a liaison/link/intermediary between health/social services and the community to facilitate access to services and improve the quality and cultural competence of service delivery [9].” Several systematic reviews and randomized control trials have found CHWs are associated with improved health outcomes in diabetes (10–13), multiple chronic diseases (14–16), cardiovascular risk reduction [17], hypertension [18], cancer screenings [19, 20], asthma [21], and mental health [22]. CHWs help healthcare to accomplish the “Triple Aim” by contributing to improved care experiences and outcomes while maintaining cost-effectiveness (17, 19, 23–28). In addition, CHWs can contribute to enhancing the quality and cultural responsiveness of health and social services and support system-level changes that can have long-lasting impact [29]. While CHWs have been working in the United States for generations, during the recent 20 years, professional institutions have increasingly recognized their role and value in addressing health needs and gaps. In 2002, one of the Institute of Medicine's findings in their “Unequal Treatment” report was “Community Health Workers offer promise as a community-based resource to increase racial and ethnic minorities' access to health care and to serve as a liaison between healthcare providers and the communities they serve [2].” In 2010, the U.S. Department of Labor created a “Standard Occupational Classification” for CHWs, 21-1094. Furthermore, in 2010, the Patient Protection and Affordable Care Act mentioned CHWs 14 times, identified CHWs as a healthcare profession, and called for funding allocation for health promotion among underserved populations [30]. During the COVID-19 pandemic, institutions such as the U.S. Centers for Disease Control and Surveillance (CDC) have funded and collaborated with CHWs to provide health outreach and education to hard-hit communities [31]. Similarly, in 2022, the American Rescue Plan awarded over 220 million dollars toward community health worker training and capacity building nationwide [32]. As frontline public health workers during the COVID-19 pandemic, CHWs played important roles including communicating COVID-19 prevention and vaccination information to their communities in culturally responsive terms [33], as well as assisting in navigating the overwhelmed healthcare system and connecting clients to virtual medical care and mental health services [34]. They also had a critical role in helping people address the myriad of social needs that the pandemic raised. As a result of the increased need in healthcare and public health spaces, coupled with increased evidence of effectiveness, the community health worker workforce is evolving rapidly in terms of professional identification and institutional recognition. In practice, “CHW” is an umbrella term that encompasses dozens of titles, including peer health advisor, care navigator, outreach worker, community health representative, promotores, and more. This multitude of titles is one factor that has affected the potential consolidation of the role into a recognizable and definable profession [35]. To address this, the CHW Core Consensus Project (C3 Project), building off the 1998 National Community Health Advisor Study, defined a set of core CHW roles and competencies in 2016 that CHW membership associations, public health institutions, and several states have endorsed [36]. The C3 Project and its predecessors identified CHWs' “connection to the community served” as their “most critical quality” [36]. CHW employers prioritize this quality to identify and hire CHWs, more so than levels of formal education or years of employment experience. However, the latter forms the basis for how many institutions and organizations calculate salaries within their compensation and promotion structures. The result is that CHWs often have low salaries and status in the workplace and few opportunities for professional growth without leaving the profession. The National CHW Common Indicators Project, a multi-institution national initiative to synthesize and improve CHW evaluation, highlights the importance of CHW workforce conditions and career advancement via two indicators. Indicator #1 is “CHWs' level of compensation, benefits, and promotion,” prioritizing this as an indicator of program quality. Indicator #12 is “Supportive and Reflective Supervision,” with CHWs recognizing this as, “...crucial factors affecting the ability of CHWs to grow as professionals, experience job satisfaction, and effectively promote health in their communities.” Considering that the improvement of the CHW work environment is reflected in two priority indicators for CHW success, pathways for advancement should be more clearly defined and incorporated into the workflow of organizations that employ CHWs. The literature on the CHW workforce is growing, yet still somewhat limited. Global literature identifies high attrition rates for CHWs, with contributing factors including low and inconsistent salaries, lack of support, and leaving for better positions (37–45). In a systematic review, Kok et al. found professional growth as a motivating factor for CHWs, and a lack of career advancement options as a demotivating one [46]. In the United States, CHW attrition rates can also be high, with low salaries, low professional status, and lack of opportunities for professional growth driving dissatisfaction [47, 48]. Farrar et al. [ 48] found that addressing barriers to education, training, and promotion led to an improvement in job satisfaction for CHWs. Anabui et al. [ 47] found that despite the challenges faced in the workplace, most CHWs want to retain their identities, rather than move to other health or helping professions for advancement. They found that clear criteria and opportunities for promotion within their field, including incentives that recognize lived experiences, were motivating for CHWs. While NACHW, APHA, and the C3 Project have defined CHWs' roles, skills, and qualities, and many state CHW associations have established core competency training and certifications, there is minimal standardized guidance around best practices for hiring or promoting CHWs. Existing guidance emerged from the 1998 National Community Health Advisor (NCHA) Study, which recommended 18 qualities that employers should seek when hiring CHWs. This guidance, although not commonly adopted, could be built upon and shared to promote the enhancement of the CHW workforce. Opportunities for advancement are particularly critical for CHWs, many of whom are themselves from (or closely tied to) populations experiencing inequities, such as members of racial minority groups, immigrants, people with low income, people who were formerly incarcerated, or people in recovery. Lack of mobility may perpetuate their economic and social vulnerabilities. The purpose of this project was to gather and synthesize first-hand perspectives from CHWs and their allies on this workforce challenge. By collecting CHW input from multiple sources, we describe why it is important to support CHW career advancement, and what employers and advocates can do to develop CHW career tiers or advancement strategies and reduce the threat of losing this vital workforce at a time when the need for their work and expertise has become even more evident. ## 2. Methods With support from Johnson & Johnson, in line with their Our Race to Health Equity initiative [49], the Center for Community Health Alignment's Community Health Worker Institute (CHWI) and Center for Applied Research and Evaluation (CARE) at the University of South Carolina Arnold School of Public Health (ASPH) gathered insight from four sources for this project: qualitative data from the South Carolina CHW Ambassadors and CHW Best Practices Council; the National Association of CHWs' (NACHW) 2021 Annual CHW Workforce Survey, a rapid-feedback session of CHWs at the NACHW Unity Conference; and qualitative interviews with six CHW managers or supervisors. The Institutional Review Board at the University of South Carolina approved the study. ## 2.1. The South Carolina CHW ambassadors and CHW best practices council A foundational component of CHWI is its engagement of a team of “CHW Ambassadors,” experienced CHWs representing a variety of races, ethnicities, geographical locations, populations served, and organizational affiliations. In 2019, the CHWI team selected 10 applicants to be the first cohort of CHW Ambassadors. They contributed to developing plans and strategies for CHW training, capacity building, data collection, and other critical components of CHWI's mission. In addition to the Ambassadors, CHWI invited seven CHW managers or supervisors to be part of a CHW Best Practices Experts Council, whose goal was to determine best practices for implementing and developing CHW programs and growing the CHW workforce in South Carolina effectively. The input from the Best Practices Expert Council on the need for CHW career advancement and a tiered certification structure was the primary impetus for this study. For a year, the council met six times to discuss job descriptions and the factors that should merit increased benefits, recognition, pay, and leadership opportunities for CHWs. The Council discussed various viewpoints until a consensus was reached. ## 2.2. NACHW's 2021 National CHW workforce survey The National Association of CHWs (NACHW) invited key influencers, funders, and CHWs leaders to join workgroup sessions toward developing this survey. The workgroup conducted extensive literature review searches, cross-referenced keywords, and identified knowledge gaps regarding the profession. CHWI collaborated with NACHW and its team of CHW advisors to add three additional questions regarding career advancement to the 51-question survey. The survey launched in June 2021, and collected responses through September 2021, ultimately compiling 867 completed surveys (772 in English and 95 in Spanish). ## 2.3. A rapid feedback session at the 2021 NACHW annual Unity Conference In July 2021, 160 CHWs and CHW allies attended an invited session at the virtual NACHW Unity Conference, titled “CHWs' Career advancement strategies: Nothing about us without us.” Two CHW leaders from the Center for Community Health Alignment conducted the session, which was in English. The facilitators posed open-ended questions about CHW career pathways and collected responses via Mentimeter, an online polling platform. In total, 80 individuals responded to four questions posed in a rapid-polling activity. Among attendees that responded to the poll, 70 ($87\%$) identified as CHWs, and 10 ($13\%$) as CHW allies (a supporter, an employer, or a researcher of CHWs). Of CHWs, $35\%$ had been a CHW for more than 5 years, $18\%$ for < 1 year, and $36\%$ between 1 and 4 years. Most of the CHWs and allies worked in a healthcare setting ($34\%$) or a community-based organization ($33\%$). Others worked in a public health department ($9\%$), university ($9\%$), or CHW organization or association ($8\%$). Participants did not divulge their geographical locations. The questions were either open-ended or multiple choice and were collected and organized by themes using a deductive approach. ## 2.4. Individual interviews Members of the research team conducted six semi-structured interviews with CHWs that are managers or supervisors of other CHWs. The interview guide consisted of several open-ended questions about the CHW career paths at their organizations. Topics included CHW compensation, promotion opportunities, and ways to support future CHWs in career progression. Four CHW managers were from South Carolina organizations, with another from Chicago, Illinois, and one from Dallas, Texas. Three managers worked in community-based settings, while the others worked in healthcare settings. Interviews were conducted via the Zoom telecommunication platform. CHWI recruited interviewees based on their knowledge of the CHW profession and experience managing CHW teams. To protect participant privacy, recordings, transcriptions, and analyses were de-identified and stored on a private cloud-based drive. In recognition of their content expertise, participants received a stipend for their participation. The interviews were transcribed using Otter.ai software. To analyze data, information was coded and analyzed for themes using Microsoft Excel [50, 51]. The study team used the constant comparative method [52]. As a group, we read through each transcript, noted emergent themes, and compared themes identified in previous transcripts. Furthermore, the study team discussed various viewpoints between team members until a consensus was reached. ## 3. Results Several themes emerged across the data sources regarding the need to advance the CHW workforce (the “why”) including the importance of retaining skilled CHWs, building respect and appreciation for the profession, and professional growth that will improve program and organizational quality. The themes about the development of opportunities for CHW advancement (the “how”) included the importance of salary improvement, making advancement decisions based on lived experience more than formal education, and providing opportunities for additional CHW training and professional development. The responses to the NACHW workforce survey are listed in Tables 1, 2, and Unity Conference session feedback is in Tables 3–5. ## 3.1.1. Retention Studies about CHW attrition found that a lack of opportunities for professional growth contributes to turnover rates [47, 48]. This is reflected in our findings, first emerging from the CHW Best Practices Council, which identified that the lack of advancement opportunities caused many CHWs to leave the field. They advocated that employers of CHWs and the South Carolina CHW Credentialing Council make CHW retention a high priority by developing tiered levels of certification. Participants at the Unity Conference agreed, “Having more options for career advancement generally means more people staying in the field, only becoming better at what they do, with the pay they deserve, helping as much people as they can.” Another said, “Because the way things are now, we are losing strong CHWs from the field. Those that are most connected to the community are now removed which causes a loss of trust.” ## 3.1.2. Respect for the CHW position The relationship between CHW advancement and a perceived lack of respect from other professionals emerged at the Unity Conference (Table 3). One CHW stated, “We are treated like housekeeping... a necessary position that is undervalued.” Another respondent said, “We are living in poverty, have skills and experience and education, but are trapped, underpaid and underappreciated with no hope of improvement; we are dedicated missionaries.” Another wrote, “Because for many companies [the CHW profession] is an unknown field and they do not understand how valuable CHWs are and don't know what to do with us.” Yet another stated, “I think people believe it's just volunteer work.” The interviewed managers agreed. One manager stated, “Yeah, I just think the... [profession]... is brand new. Like, we are learning about all the benefits that our work offers to the community. … We need to create awareness to the funders about the value that we provide to the community and to the medical services and the government, how much money they saved… I think like doing research, like a study of the return on investment… real numbers that you can show to the funders, so they can see the benefit of our profession.” Another suggested that awareness building is an important role for CHW allies, “Continue to work and push work with organizations, to develop Community Health Worker programs, and help them to understand the role of Community Health Workers and why they're important, continue to advocate for Community Health Worker pay.” Educating other professionals and team members about the role of CHWs will help showcase their value in the workplace, further incentivizing them to stay and grow. ## 3.1.3. Professional growth and quality improvement The interviewees also mentioned that CHWs' professional growth will ultimately result in improved services to patients and participants. “ We all want to get better all the time and, and make more money, and just think that you are growing, that you are learning, and that you are growing in your profession. Just like it is good for us and it motivates you to have like a better position, a better salary. I think that's important for everybody.” Another interviewee stated, “Oh, it gives them something to strive for, it gives them something to look forward to, you know, I'm saying it allows them to take the initiative to improve themselves, in professional development.” ## 3.2.1. Salary Across data sources, CHWs identified “salary increases” as the most important factor to consider in advancement opportunities. “ Higher salary” was the most frequently mentioned priority for CHWs in both the NACHW Workforce Survey (Table 1) and the Unity Conference session (Table 4) when asked what should be incorporated into advancement pathways. One conference attendee commented, “CHWs are often one paycheck away from needing the services we provide.” The CHW managers are aware of the impact of low salaries on their CHWs; one said, “It's just I have been advocating for my team to get a better salary, but it is very hard for us to get the resources. … I really would like to have more for my Community Health Workers because they work so hard. And they really deserve to have a better salary.” However, they also deal with the challenges of finding funding sources for those salaries. One manager said, “You know how it works when you work with grants, you have this person for 1 year and she learned so many things, she's gained so many skills, and then she has to leave because I don't have any more funds.” Two CHW managers suggested institutional funds or insurance payments be aligned with CHW services. One suggested, “Oh, that's the biggest challenge for me that I would really like... maybe the hospitals to take over these programs and say, ‘okay, I'm going to pay you like, to permanently to do the work.”' Another mentioned, “[A] patient came in, and we assisted them with food insecurity, boom, that should be a code. Right? That should be a compensation point.” ## 3.2.2. Valuing lived experience above formal education At the Unity conference session, many responses to the question, “What factors should be considered to move CHWs from one level to the next?” were related to experience as CHWs and lived experience as a member of the communities served (Table 5). Answers to the question included, “Respecting experience and not just the letters after your name,” and “Experience in the field and the resources we bring... how we are respected in the community.” One CHW said, “Because CHW's speak in everyday people's language! That's why what we do works!” Multiple interviewed CHW managers agreed they would hire or promote other CHWs based on lived experience. One manager stated, “So I struggle with that sometimes, because I sometimes do get caught in okay, what kind of education do they have? … But then I had to, I had to catch myself sometimes. That's not what is at the heart of a Community Health Worker. It's the community. It's the person. Its, you know, what role have they played in their community?” When asked what factors are important in promoting CHWs, another CHW manager stated that, “experience dealing with the population that we're trying to target… and being able to meet those patients where they're at,” was important. Another manager said, “I found out some of the ones, you know, that that didn't have that that degree or, or may not always have had a medical experience are the best ones.” The value of lived experience and community connections contrasts with many employing institutions' emphasis on formal education and credentials. In the NACHW workforce survey, when asked about barriers to their success as CHWs, $25\%$ of CHWs identified higher-level education and $21\%$ “other credentials” [53]. CHWs have deep knowledge and experience in the community; determining how to place value on this quality in an institutional setting is critical. ## 3.2.3. CHW training and professional development While most respondents did not prioritize associating CHW advancement with formal education, there is a theme of wanting CHW training, professional development, and certification as means of advancing. The CHW Best Practices Council recommended that additional CHW-focused education and experience should help move a CHW from one tier to the next. The national survey respondents agreed; CHWs ranked “additional CHW training” as the top factor to be valued in their career paths (Table 2). This was followed by CHW certification, then experience mentoring other CHWs. An interviewee also agreed, suggesting training as a method of supporting CHW advancement, “I would say continue to provide educational pathways for Community Health Workers, professional development.” Another interviewee suggested that organizations should, “…assess company needs and provide training and education.” ## 4. Discussion These findings show that more effective and equitable career pathways for CHWs would provide motivation for CHWs to remain in the workforce, promote retention, recognize effective work and leadership, and contribute to organizational quality improvement. CHW career advancement pathways should recognize the unique contributions of CHWs to health improvement, take into consideration the best practices offered by the workforce, and support CHWs in addressing the challenges and barriers that exist. In the past few years, a growing number of institutions and organizations have realized the need to hire and integrate CHWs into their work. However, the CHW field faces multiple threats that can result in attrition and burnout and put the workforce and employers at risk of not being able to meet the growing demand. CHWs have reported feeling undervalued, underpaid, and not respected. By not providing opportunities for CHWs to grow in their field, organizations risk losing those CHWs with the most experience, as opposed to building on that expertise to support newer CHWs. ## 4.1. Challenges to be addressed The barrier that was most frequently mentioned by CHWs and CHW allies was funding, both in terms of having enough funding to offer higher salaries and having sustainable funding so that positions are not dependent on grants and other one-time funding sources. Due to the value of CHWs' lived experience and connection to the populations they serve, CHWs can be at high risk of poverty, which makes offering livable wages essential. In addition, time limited and insufficient compensation pose substantial risks to the CHW workforce. Our findings suggest that having more sustainable and equitable funding will facilitate the retention of CHWs. Another major challenge pertains to human resource systems and processes placing value on higher education and formal degrees rather than lived experience. Foremost, higher education can be financially cumbersome for CHWs, many of whom hail from marginalized populations. Similarly, CHW managers, CHWs, and the C3 Project agree that lived experience is a core quality of a CHW. Therefore, loosening requirements surrounding formal education and valuing lived experience can assist in finding and promoting the right individuals for CHW positions. It can also assist in building financial equity for CHWs because they will have opportunities for advancement without incurring additional debt. Further emphasis needs to be placed on understanding and dismantling systemic racism within organizations due to the part that it plays in maintaining hierarchical leadership that keeps those with privilege in power. The role of privilege and power dynamics needs to be recognized and addressed in order to determine paths to leadership that are more equitable, open, and transparent. Finally, there is a need for more awareness of CHW's roles and contributions to public health and healthcare. Many stated that employers such as hospitals and state or federal agencies lack awareness of the roles, qualities, and importance of CHWs. Similarly, community members may not understand the role of a CHW. Developing strong communication plans that include training employers and marketing to the public can assist in raising awareness of the CHW profession, creating more respect for the CHW role and scope, helping keep experienced CHWs in the workforce, and incentivizing others to join the field. ## 4.2. Designing CHW advancement pathways Almost all CHWs and allies reported that most CHWs want to stay in and be able to progress within their field, as opposed to having to leave the CHW workforce to pursue additional opportunities. However, many CHWs stated that employers will not allow them to progress without adopting a new title or professional identity. Developing a tiered CHW advancement framework, such as the ones in the case examples included, can guide employers and advocates in their efforts to promote and retain CHWs. Advancement factors can include additional training, professional development, certifications, mentorship of other CHWs, years in the CHW field, and the quality of their work. CHWs strongly favor their profession's own training, certification, and experience as factors for advancement, more than formal education or external qualifications. Upon advancing, CHWs should be granted opportunities to participate in leadership roles, mentorship, program design, and advocacy. ## 4.3. Examples Later are two examples of organizations that have created and implemented CHW career advancement pathways, with strong and intentional input from CHWs. ## 4.3.1. CHW tiered system at Baylor Scott & White Health Baylor Scott & White Health (BSWH), the largest not-for-profit healthcare system in Texas and one of the largest in the United States, employs CHWs to help patients navigate an increasingly complex healthcare system, facilitate self-management of chronic diseases, and connect patients to primary care medical homes. BSWH CHWs act as peers, navigators, advocates, educators, and promoters of improving outcomes and quality of life for the patients they serve. In 2007, BSWH hired one CHW as a diabetes educator. In 2009, the team expanded to four CHWs through the Diabetes Equity Project, and in 2011, BSWH created new positions, marking the start of BSWH's current CHW career ladder efforts. Between 2011 and 2014, the integration of CHWs expanded even more through various grant and funding opportunities. As the system developed more CHW programs, CHW supervisors recognized the need for a systemic approach to CHW support that could bridge the programs. They created the CHW Development Council to meet this need. This Council utilized key data derived from annual workforce feedback surveys at the BSWH CHW Summit and the Texas core competencies for public health professionals to build a career ladder. Rather than encouraging BSWH CHWs to pursue growth through other career ladders such as becoming social workers or other healthcare professionals, they determined it was more effective and supportive to build a CHW career ladder that respects the CHWs' passion and path, thus allowing BSWH to retain talented and invaluable CHWs at all levels for the last decade. The CHW Development Council developed the advancement levels in Table 6 and provided the levels to supervisors as a framework they can use to help grow and retain their CHWs. Currently, BSWH employs over 120 CHWs. Table 6 \| Position \| General description \| \| --- \| --- \| \| CHW in-training \| Embedded community member that has the desired experience and knowledge of their community but does not necessarily have the required Texas Department of Health Services CHW certification; they are required to complete certification training within 1 year of employment. \| \| CHW I \| Experienced CHW, with current DSHS certification \| \| CHW II \| Experienced CHW, with current DSHS certification, that may have taken a team lead role and/or have applicable experience \| \| CHW supervisor \| Experienced, “veteran” CHW, with current certification, who has the capacity to manage a team \| \| CHW manager \| Experienced CHW that supports multiple care settings, may directly oversee CHW staff, and may serve at a system level in a project management capacity to support and drive CHW initiatives \| ## 4.3.2. South Carolina CHW tiers As previously mentioned, the South Carolina CHW Best Practices Council (BPC) identified CHW career advancement as a high priority for the CHW workforce in South Carolina. The South Carolina CHW Credentialing Council (SCCHWCC), which is supported by the South Carolina Community Health Worker Association, is the statewide approving body for CHW training programs, certification, and continuing education. The SCCHWCC is composed of at least $51\%$ CHWs, along with representatives from the state Medicaid institution, the state public health department, AHEC, higher education, and a health insurance entity. The BPC worked to draft examples of tiered CHW job descriptions, based on the different requirements for the position. The Council also recommended that, statewide, there should be multiple tiers of CHW certification, and that additional CHW-focused education and experience should be what help move a CHW from one tier to the next. Based on the work initiated by the BPC, CHWCC made enhancements to the CHW credentialing process, creating a three-tiered system for certified CHWs. The Credentialing Council drafted these tiers and reviewed them multiple times with the BPC. In April 2022, the three-tiered certification in Table 7 was approved and it was launched in January 2023. In the future, efforts will be made to educate employers on the certification tiers, to encourage them to adopt them, along with appropriate advancement in salaries and opportunities for CHWs. Table 7 \| CHW position \| General description \| \| --- \| --- \| \| Certified community health worker (CCHW) \| • Successful completion of a SCCHWCC approved CHW Core Competency Curriculum: 80 h classroom, 80 h practicum, SCCHW Examination • Registering on the South Carolina Community Health Worker Association (SCCHWA) CHW Portal \| \| Certified community health worker II (CCHW II) \| • Minimum of 4 years working the field • Bi-Annual Recertification (24 h every 2 years) • Certification of Completion of a minimum of 4 CHW Specialty Tracks • Demonstration of at least 1 year in CHW leadership (CHW ambassador, SCCHWA board, active involvement in SCCHWA workgroup, active involvement in CHW regional or national committee or initiative, CHW Preceptor, CHW supervisor, CHW program leadership or program development at organizational level) • Updated profile on the SCCHWA CHW Portal • Application submission \| \| Certified community health worker III (CCHW III) \| • Minimum of 8 years working the field • Bi-Annual Recertification (24 h every 2 years) • Certification of Completion of a minimum of 6 CHW Specialty Tracks • Demonstration of at least 2 years in CHW leadership (CHW ambassador, SCCHWA board, active involvement in SCCHWA workgroup, active involvement in CHW regional or national committee or initiative, CHW Preceptor, CHW supervisor, CHW program leadership or program development at organizational level) • Updated profile on the SCCHWA CHW Portal • Application submission \| ## 4.4. Limitations Limitations of this study include a lack of consistency across data sources; the questions were similar, but not identical. To address this, the team gathered data from multiple sources to make sure the themes were accurate and consistent. Respondents of the national CHW survey may have been impacted by survey fatigue bias, due to the length of the questionnaire. In addition, the interviews and Unity Conference sessions were in English. Aligning interview data of Spanish speakers with the survey data could have brought more insight to researchers around the differences between how English and Spanish-speaking CHWs are employed and promoted. Further research can be conducted in various languages and with CHWs from different backgrounds to determine if there are nuances in factors affecting CHW advancement. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by University of South Carolina Institutional Review Board. The NACHW survey and conference poll were anonymous, and did not require written consent. Individual interview participants provided their verbal consent to participate in this study. ## Author contributions JS and MY designed the study and led data collection. JN and SC-K contributed to analyzing the data and preparing the manuscript. AR reviewed for relevancy to the field and provided case study input. All authors contributed to the article and approved the submitted version. ## Conflict of interest AR was employed by Baylor Scott & White Health. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## CHW interview guide Do I have permission to record this interview? Thank you for taking the time to interview with me today. First, I'm going to get some information about you and your role within your organization. 1. Tell me a little about yourself and your work at your organization. a. How long have you been a CHW? How long have you worked at your organization? b. About how many CHWs does your organization employ? c. How many do you directly supervise? d. What are the funding sources for your CHWs pay? 2. Next, we're going to discuss the career path for CHWs in your organization. When I say career path, I mean how people grow or advance in their job, such as opportunities for promotions or pay raises. Does your organization have a career ladder for CHWs? If so, tell me about them. a. Does your organization have education or experience requirements to promote CHWs? b. What barriers or challenges have you faced when developing a career ladder? How did you address these barriers? c. Why do you think having a career ladder is important? d. What factors should be considered to move CHWs from 1 level to the next? e. What qualities/personality traits do you look for when hiring or promoting a CHW? 3. What does compensation look like for CHWs at your organization? What is the lowest and highest salary range? a. What types of benefits do your CHWs receive? b. Any other benefits (mileage, per diem, etc) c. What about the CHW model at your organization do you think is unique? 4. If you had the opportunity to change anything about the CHW profession, what would it be? a. Have you heard anything from former CHWs about why they left the position? 5. In your opinion, how can we better support CHWs in advancing their careers? 6. What else can you share with me that could be helpful as we try to work on this? ## References 1. Shantharam S, Andrade N, Bhatt A, Bostic M, Chase A, Fulmer E. Best Practices for Heart Disease and Stroke: A Guide to Effective Approaches and Strategies (2022.0) 2. Smedley BD, Stith AY, Nelson AR. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (2003.0) 3. 3.Heart Disease Stroke Prevention: Interventions Engaging Community Health Workers: Community Preventive Services Task Force. (2017). 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--- title: 'Examination of sleep in relation to dietary and lifestyle behaviors during Ramadan: A multi-national study using structural equation modeling among 24,500 adults amid COVID-19' authors: - Moien A. B. Khan - Ahmed S. BaHammam - Asma Amanatullah - Khaled Obaideen - Teresa Arora - Habiba Ali - Leila Cheikh Ismail - Dana N. Abdelrahim - Mohammed Al-Houqani - Kholoud Allaham - Rand Abdalrazeq - Wahid Sharif Aloweiwi - Somayea Sultana Mim - Ammar Mektebi - Sohrab Amiri - Sahabi Kabir Sulaiman - Syed Fahad Javaid - Mohammad Delwer Hossain Hawlader - Fatimah Isma’il Tsiga-Ahmed - Iffat Elbarazi - Saskiyanto Manggabarani - Gamechu Atomsa Hunde - Sabrina Chelli - Mitra Sotoudeh - MoezAlIslam Ezzat Faris - Abasi-Okot Akpan Udoyen journal: Frontiers in Nutrition year: 2023 pmcid: PMC10030961 doi: 10.3389/fnut.2023.1040355 license: CC BY 4.0 --- # Examination of sleep in relation to dietary and lifestyle behaviors during Ramadan: A multi-national study using structural equation modeling among 24,500 adults amid COVID-19 ## Abstract ### Background Of around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors. ### Methods This cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters. ### Results In total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7–9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters. ### Conclusion Increasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF. ## Introduction Muslims constitute the world’s second-largest religious group, as the estimated 2 billion Muslims equates to about $25\%$ of the global population (8 billion) [1]. A foundation pillar of Islam is fasting during Ramadan month [2]. During this time, adult Muslims are mandated to fast from dawn to sunset. This involves complete abstinence from food, drink, sex, and smoking for 11–20 h per day, depending on the geographical location and the solar season that crosses the lunar fasting month [2]. During Ramadan, Muslims take two main meals: the breaking of the day’s fast meal at sunset (“Iftar”), and a pre-dawn meal taken in anticipation of the coming fasting hours (“Suhoor”) [3]. During night hours, Muslims are free to eat, dine, pray, socialize, and perform life activities as permitted by Islamic ruling [4]. Fasting during Ramadan is a form of diurnal intermittent fasting or a time-restricted eating model [5], with dietary and lifestyle changes that persist for 29–30 consecutive days around fixed time points. Various forms of intermittent fasting have been reported to induce a plethora of health benefits [6, 7]. Among these, religious forms of fasting such as Ramadan intermittent fasting (RF) have received continuous attention [7]. For example, several beneficial health impacts of RF have been demonstrated in numerous studies over the last seven decades [8]. Previous studies reported RF was associated with reduced metabolic syndrome components [9], reduced body weight [10], reduced body fatness [11] with emphasis on visceral fats [12], decreased inflammatory and oxidative stress markers [13], and improved glucometabolic regulation [14] and liver function tests [15]. On the other hand, RF has also been associated with disrupted circadian rhythms, and changes to sleep-wake timings as well as hormones such as leptin, adiponectin, ghrelin, cortisol, and melatonin (16–19). These changes were associated with sudden changes in meal timings, diet composition, food group consumption, and sleep continuity during Ramadan, which may interfere with the metabolic impacts of RF [18, 20, 21]. Insufficient sleep has previously been associated with weight gain and cardiometabolic risk [22]. Of note, several studies reported significant and sudden delays in bedtime and waking time and a considerable reduction in total sleep time during RF [20, 23], which may contribute to weight gain but be counteracted by reduced food intake during this time. Empirical results for non-RF times showed that diet quality, timing, and quantity impacted sleep duration and sleep quality [24, 25]. Therefore, it is relevant to assess the association between sleep and dietary and lifestyle factors during RF. However, Ramadan-related dietary and lifestyle practices vary across nations and cultures, which may impact sleep length, sleep-wake timings, and sleep disruption to different degrees. This means results for sleep and lifestyle changes obtained from a specific country or culture cannot be generalized to other countries. This is bolstered by the data that dietary, sleep, and lifestyle behaviors vary substantially across the world’s population and, as a result, so does metabolic health status (26–34). The present study examined changes in dietary and lifestyle factors during RF and explored their associations with sleep duration and quality among fasting people from a range of different cultures and ethnicities. This study was based on the hypothesis that RF is associated with significant changes in sleep timing, duration, and quality. We also hypothesized that changes in sleep quality and duration are likely to be affected by various dietary and lifestyle changes that occur during the month of fasting. Because multiple lifestyle factors are closely connected with sleep outcomes during RF, we used structural equation modeling (SEM) to examine how specific dietary and lifestyle modifications influenced three sleep parameters (sleep duration, sleep quality, and sleep disturbances) among fasting Muslims during RF in several countries with varying dietary and lifestyle habits and background cultures. ## Study design, settings, sample size, and population The present study used a cross-sectional, observational design with targeted recruitment of adult Muslims who fasted during Ramadan month. This study obtained data related to dietary and lifestyle changes during RF in the context of the COVID-19 pandemic. Data collection started on May 10, 2021 (corresponding to the 27th Ramadan month in 1,442 Hijri) and concluded on 10 June 2021 (29th Shawwal 1,442 Hijri). The inclusion criterion was adult Muslims (aged ≥18 years) who observed RF. We excluded those who answered “Yes” to the question “Have you been diagnosed with or treated for a mental health problem?” Individuals following specific diets and those engaged in shift work were also asked not to complete the survey at the beginning of the electronic questionnaire. This study was initiated and supervised by researchers in the UAE. Snowball sampling was used to collect data. Voluntary collaborators from various countries were invited through Facebook research groups. In total, 116 collaborators from 29 countries participated in data collection. These research collaborators randomly drew on their networks using the different web-based platforms such as emails, WhatsApp, and Facebook. Each researcher was given a survey form along with a unique link to collect data, and data were pooled when data collection was completed. This method facilitated the wide distribution of the survey questionnaire during the pandemic period when there were many lockdown restrictions. An a priori G* power estimate was calculated for each country using one-tailed Student’s t-tests and a bivariate correlation analysis model to identify the required sample size. An estimated effect size of 0.2, alpha error of 0.05, and power of 0.90 indicated that a minimum of 207 participants were needed from each country included in this study. This study adhered to the code of ethics as set out in the Declaration of Helsinki [35]. Before data collection started, approval was obtained from the Social Sciences Research Ethics Committee of the United Arab Emirates University (Approval number ERS-2021-7308) and Tehran University of Medical Science (Approval number IR.TUMS.FNM.REC.1400.022). Furthermore, participants were informed about the objectives and procedures of the study before providing informed consent. No monetary or non-monetary incentive was given for participation. Participants gave their consent as a first step on the online questionnaire form. ## Tool development and translation The data collection tool was prepared as a structured, self-administered, electronic questionnaire that assessed demographic information, dietary intake, eating habits, sleep parameters, and physical activity (PA) level during Ramadan. Web-based questionnaires were circulated, and participants were recruited using convenience and snowball sampling methods. As no changes to the questionnaire were deemed necessary after the pilot testing, participants from the pilot study samples were included in the final sample for the analyses. The questionnaire developed for this study was translated into 13 different languages (Arabic, English, French, Turkish, Urdu, Bengali, Persian, Indonesian, Pashto, Dari, Amharic, Malay, and Afaan Oromo). The questionnaire translation and cultural adaptation process followed the “Principles of Good Practice for the Translation and Cultural Adaptation Process” [36]. First, forward translations were performed by two independent translators fluent in both English and the local language, and then back-translated to English. After reviewing the backward translations, the version of the questionnaire for each language was proofread and underwent further editing (as necessary) before being pilot tested with 30 participants, which resulted in the final versions of the survey questionnaire [36]. A structured, self-administered questionnaire was developed from previously validated questionnaires (37–43). After collecting relevant questions from these questionnaires, the self-structured questionnaire for this study was translated and then pre-tested to ensure the questions were unambiguous. ## Data cleaning Response options with the same meaning were unified for all variables. Participants who missed answering major questions (e.g., consent, age, sex, nationality, education, occupation, health status estimation, country of residence, and the number of fasting days) were omitted from the analyses. Those that did not fast during Ramadan were also omitted; participants that reported “zero” as the number of fasting days were considered non-fasting and omitted from our study. Body mass index (BMI, kg/m2) was calculated for all participants using their reported height and weight, after which they were classified and analyzed using established BMI categories [43]. Participant’s country of residence was used to cluster the study population into three main geographical regions rather than their nationality. These regions were based on the latest update of the world map. Participants from non-countries (e.g., territories and self-proclaimed countries) were omitted. Dietary pattern questions were re-classified from four category variables into two category variables. Finally, we excluded questionnaires for participants younger than 18 years and those with abnormal inserted values (weight, height, age). ## Questionnaire measures Sociodemographic information collected included sex and age (years), country of residence, nationality, region, marital status (single, married, divorced, or widowed), living area (city, town, or village), household income, living conditions (alone, with friends, or with family), education level, and the number of fasting days experienced (this question reflected the fact that some people may fast less than 29–30 days for various reasons such as travel or illness). As this was a multicenter study, total household income was classified into five quintiles for standardization purposes: upper (top $20\%$), upper-middle (upper $20\%$), basic middle (middle $20\%$), marginal middle (lower $20\%$), and lower (lowest $20\%$) [44]. In addition, participants were asked to classify their economic status as per their economic conditions in relation to their community and local region/country. A smoking behavior questionnaire was used to identify smoking behaviors (cigarette and shisha) before and during the month of Ramadan [37, 38]. Questions assessing participants’ PA levels were derived from the International Physical Activity Questionnaire Short Form [41]. We assessed participants’ general PA levels, as well as the frequency of 10 min of heavy and light PA, and overall self-evaluated energy levels before and during RF. We merged heavy and light PA into a single PA variable for analysis. Questions covering screen time and sleep parameters were based on the validated Copenhagen Psychosocial Questionnaire [41, 42]. Participants provided information on time spent using computers for work as well as television/social media or computers for entertainment (both day and nighttime) and spending time with family. Self-reported computer/laptop use for study, work, or entertainment was merged into one variable. We collected participants’ self-reported height (cm) and weight (kg) and then calculated and categorized BMI based on the World Health Organization definition [43]. The dietary part of the questionnaire collected information on several factors before and during RF, including modification of eating practices, snacking frequency, intake of water, consuming large quantities of food, feeling hungry, and the consumption of 20 different food items and beverages (vegetables, fruits, cereals, oils and fats, milk and milk products, pulses/dried legumes, dates, fish and seafood, white meat, red meat, sugar, salt, fried foods, salty snacks, carbonated beverages, energy drinks, tea and coffee, bakery products, homemade foods, traditional foods, delivered food, restaurant food, and snacks) [39, 40]. To evaluate dietary intake, we classified foods into eight groups: (I) fruits, vegetables, and dates; (II) cereals, pulses (dried legumes), and pastries; (III) milk products, fish and seafood, white meat (chicken and turkey), and red meat; (IV) oils, fats, and fried foods; (V) sugar, carbonated beverages, energy drinks, and tea and coffee; (VI) salt and salty snacks; (VII) homemade food; and (VIII) traditional foods. Each of these food item groups had four response options in the original questionnaire: “not consumed,” “decreased,” “remained as usual,” or “increased.” For the analyses, these responses were re-categorized into binary categories using two different couple of terms based on the type of food item, first couple of term was sufficient/insufficient, which was defined as sufficient (remained as usual or increased intake), or insufficient (no intake or decreased intake). These terms were applied for (I) fruits, vegetables, and dates; (II) cereals, pulses (dried legumes), and pastries; (III) milk products, fish and seafood, white meat (chicken and turkey), and red meat. A second couple of terms is frequent/infrequent, where frequent was denoted (remained as usual or increased intake) while infrequent (no intake or decreased intake). These terms were used for (IV) oils, fats, and fried foods; (V) sugar, carbonated beverages, energy drinks, and tea and coffee; (VI) salt and salty snacks; (VII) homemade food; and traditional foods, because there was no limit of the sufficiency of these four groups. Similarly, some other variables had four response options also in the original questionnaire: “not consumed,” “decreased,” “remained as usual,” or “increased.” Those variables were categorized in the same way into binary variables as frequent/infrequent style, where frequent was denoted (remained as usual or increased intake) while infrequent (no intake or decreased intake). This was applied also to the following behaviors: consuming water, practicing physical activity, consumption of delivered food, restaurant dining, and using the computer. While smoking was originally defined as smokers and non-smokers. ## Sleep parameters Three sleep parameters were assessed based on participants’ estimations before and during RF: sleep quality, sleep duration (<7, 7–9, and >9 h), and sleep disturbance. We considered 7–9 h of sleep per night as optimal sleep duration [45], <7 h as short sleep duration, and >9 h as long sleep duration, based on previously published consensus by sleep experts [46]. Sleep quality was self-reported as poor, good, or very good. Participants were also asked to indicate if they experienced any of the listed sleep disturbances before and during RF: (I) slept poorly and restlessly; (II) hard to go to sleep; (III) woke too early and was unable to get back to sleep; (IV) woke several times and found it difficult to get back to sleep; and (V) no sleep disturbances. These questions were drawn from a reliable and validated instrument [42]. ## Statistical analyses Statistical analyses and the application of SEM were based on the consideration of dietary and lifestyle behaviors as the main exposures for people observing RF, and the three sleep quality parameters (sleep duration, perceived sleep quality, and sleep disturbance) as the main outcomes of interest. ## Structural equation modeling Structural equation modeling is a multivariate statistical analysis technique used to analyze structural relationships. This technique combines multiple regression and factor analyses and is used to investigate the structural relationship between measured variables and latent constructs. This technique allows accurate estimations of interrelated variables and multiple dependencies in a single analysis [47, 48]. Two types of variables are used in SEM: exogenous and endogenous variables. Exogenous variables are equivalent to independent variables, and endogenous variables are equivalent to dependent variables. In this study, SEM was performed using Smart PLS 3 software [49]. Dietary intake and eating and lifestyle behaviors (including consumption of major food groups, delivered food, dining in restaurants, smoking, PA, and computer use) were considered exogenous variables, and sleep duration, sleep quality, and sleep disturbance were considered endogenous variables. Evaluation of the model was performed in two steps. First, we evaluated the measurement model to assess the psychometric properties of the variables. In the second step, the structural model was evaluated by considering multicollinearity, multiple correlations, predictive relevance, and path coefficients. In the model examined in the present study, only discriminant validity was calculated. As we used observed variables, reliability and convergent validity for variables with a single indicator should equal 1. Furthermore, we assessed the correlation between independent variables in the model to explore multicollinearity issues. The predictive relevance (Q2) of the model was calculated by eliminating specific data points. The final step in the evaluation of the structural model was to identify significant paths or associations between independent and dependent variables. Discriminant validity was measured using the Heterotrait-Heteromethod ratio of correlations (HTMT) criteria, which is an advanced method of determining discriminant validity. HTMT represents the ratio of Heterotrait-Heteromethod correlations to Monotrait-Heteromethod correlations. HTMT correlations reflect the correlation of indicators with other constructs in the model, whereas Monotrait-Heteromethod correlations are the correlations of indicators with the same constructs in a model [50]. An HTMT value between two constructs below 0.9 suggests that discriminant validity has been established and the variables are distinct from each other [51]. Good discriminant validity means that indicators of all variables are distinct from each other (Supplementary Table 1). The structural model was then evaluated by exploring multicollinearity, multiple correlations, and the predictive relevance of the model. The correlation between independent variables was assessed to explore multicollinearity issues using variance inflation factor (VIF) statistics. Hair et al. [ 52] indicated the VIF value should be less than 5. We found that the VIF values for the independent variables corresponding to all three sleep parameters were <5.0; therefore, multicollinearity was not present. The predictive capabilities of the model were calculated using the coefficient of determination (R2) and predictive relevance (Q2). R2 was obtained using a bootstrapping procedure with 5,000 sub-samples, calculated through the blindfolding procedure by selecting an omission distance of 7. A descriptive table was prepared using SPSS version 26 (IBM, Armonk, NY, USA). Categorical variables were expressed as frequency and percentage. Continuous data were described using mean ± standard deviation. Descriptive statistics were used to present sociodemographic data. STATA version 16.1 (StataCorp® LLC, TX, USA) was used for the statistical analyses. Any missing data were predicted using linear regression. P-values < 0.05 were considered statistically significant. ## Results In total, 28,179 participants were recruited for this study and provided data; 3,638 participants were excluded after applying the exclusion criteria, leaving 24,541 participants for inclusion in the final analyses. Data were collected from 27 countries distributed across three main regions (Figure 1). The Middle East and North Africa (MENA) region, including The Gulf Cooperation Council (GCC) countries (Bahrain, $$n = 688$$; Kingdom of Saudi Arabia, $$n = 421$$; Qatar, $$n = 466$$; and the UAE, $$n = 3$$,359); Middle East non-GCC countries (Egypt, $$n = 2171$$; Iraq, $$n = 1$$,101; Iran, $$n = 2$$,946; Jordan, $$n = 680$$; Palestine, $$n = 1$$,706; Syria, $$n = 1$$,356; and Yemen, $$n = 845$$); and North Africa countries (Algeria, $$n = 592$$; Ethiopia, $$n = 212$$; Libya, $$n = 777$$; Morocco, $$n = 1$$,390; and Tunisia, $$n = 529$$). The South Asian region included Bangladesh ($$n = 12$$,69) and Pakistan ($$n = 2$$,312). The Southeast Asia region comprised Indonesia ($$n = 1$$,605). FIGURE 1: Worldmap showing the number of participants using color density for the 27 participating countries. The MENA region with its three sub-regions (GCC, Middle Eastern non-GCC, and North Africa) comprised the largest proportion of the study population, followed by the South Asian region and the Southeast Asia region (Table 1). As depicted in Figure 2, females comprised almost two-thirds of the study participants. The most common age group was 18–32 years, followed by 33–47 years. The majority of participants were single and about $28\%$ were married. The largest proportion of participants lived in cities, followed by villages and towns. Based on the participant’s country of residence, the majority of participants had basic middle-income status, followed by the upper-middle and marginal middle. The largest proportion of participants was living with their families and engaged in fasting for most of Ramadan month (21–30 days), followed by those fasting for two-thirds and one-third of the fasting month. Most participants were non-smokers, and around two-thirds had a university-level education. More than half of the participants were students, and one-third were employed. Medication in computer use was common among participants during Ramadan. Finally, the largest proportion of participants had normal body weight ($47.3\%$), followed by those with overweight, underweight, and class I obesity (Figure 2). Detailed sociodemographic characteristics for the three regions are reported in Table 2. The SEM output is presented in Figure 3, which depicts the relationships between the three sleep parameters and various dietary and lifestyle behaviors practiced during RF. Collinearity statistics (VIF) of the study variables are reported in Table 2. As shown in Table 2, all independent variables (eating behaviors, dietary intake, and lifestyle behaviors) were weak but significant predictors of the dependent variables (three sleep quality parameters). The coefficient of determination (R2) and predictive relevance (Q2) of the model are reported in Table 3. FIGURE 3: Structural equation modeling (SEM) for the relationships between sleep components and the dietary and lifestyle behaviors during Ramadan fasting month among the 24,500 study participants. TABLE_PLACEHOLDER:TABLE 3 Table 4 shows that people who modified their delivered food pattern and consumed sufficient cereals, pulses/dried legumes, and bakery products (plant-based proteins) during RF reported optimal sleep duration (7–9 h). Furthermore, sufficient PA was positively associated with optimal sleep duration. Apart from the positive effects of consumption of delivered foods, the impact of sufficient consumption of plant-based proteins and sufficient PA on sleep duration had a weak effect size. TABLE 4 \| Eating, dietary, and lifestyle behavior \| B \| SE \| P \| Confidence intervals \| Confidence intervals.1 \| f 2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Smoking \| -0.01 \| 0.01 \| 0.12 \| –0.02 \| 0.001 \| 0.001 \| \| Frequent ordering of food delivery \| 0.02 \| 0.01 \| 0.001 \| 0.01 \| 0.04 \| 0.001 \| \| Frequent eating out in restaurants \| 0.001 \| 0.01 \| 0.75 \| –0.02 \| 0.01 \| 0.001 \| \| Frequent computer use \| 0.001 \| 0.01 \| 0.96 \| –0.01 \| 0.01 \| 0.001 \| \| Physical activity \| 0.05 \| 0.01 \| 0.001 \| 0.04 \| 0.07 \| 0.001 \| \| Eating vegetables, fruits, dates \| 0.01 \| 0.01 \| 0.12 \| 0.001 \| 0.02 \| 0.001 \| \| Cereals, pulses (dried legumes), bakery products consumption (Plant-protein sources) \| 0.04 \| 0.01 \| 0.001 \| 0.02 \| 0.05 \| 0.001 \| \| Milk, fish, chicken, meat consumption (Animal-protein sources) \| 0.01 \| 0.01 \| 0.17 \| 0.001 \| 0.02 \| 0.001 \| \| Oils, fats, fried food consumption \| 0.001 \| 0.01 \| 0.51 \| –0.02 \| 0.01 \| 0.001 \| \| Sugars, carbonated beverages, energy drinks, coffee, and tea consumption (caffeine sources) \| 0.01 \| 0.01 \| 0.13 \| 0.001 \| 0.03 \| 0.001 \| \| Adding salt, and salty snacks consumption \| -0.01 \| 0.01 \| 0.22 \| –0.02 \| 0.01 \| 0.001 \| \| Eating homemade, traditional foods \| -0.01 \| 0.01 \| 0.42 \| –0.02 \| 0.01 \| 0.001 \| Table 5 shows that smoking was associated with lower self-evaluated sleep quality. Furthermore, sufficient PA and sufficient consumption of vegetables, fruits, dates, cereals, pulses, and bakery products during RF were associated with better subjective sleep quality. Participants’ quality of sleep improved when they reduced their consumption of delivered food during Ramadan. However, consumption of salt and salty snacks and eating homemade traditional foods was associated with lower sleep quality, although the effect size of the impact of these factors on sleep quality was weak. Smoking was associated with increased sleep disturbance during RF (Table 6). Both modifications in dining out and eating homemade traditional foods were positively associated with sleep disturbance. Unexpectedly, sufficient PA, sufficient consumption of fruits and vegetables, and sufficient consumption of both plant and animal-based proteins were positively associated with sleep disturbance; the effect size for these factors was weak but significant. At the regional levels, significant differences were found between the three regions concerning the various dietary, sleep, and lifestyle behaviors, except for the delivery of food delivery (Table 7). ## Discussion This large-scale study examined the relationship between dietary and lifestyle factors and sleep duration and quality during RF across various countries in the context of the COVID-19 pandemic. Our hypothesis that RF was associated with significant changes in sleep timing, duration, and quality was confirmed. Furthermore, our hypothesis that changes in sleep quality and duration were related to changes in dietary and lifestyle behaviors during RF was also confirmed. In line with the observed differences in the different dietary, sleep, and lifestyle behaviors between the investigated regions, changes in these outcomes were significantly different between the three investigated regions upon Ramadan, except for the delivery of food. This could be understood in light of the basic inherent differences in cultural, economic, and ethnic fundamental backgrounds between the different regions. ## Sleep duration We found a conflicting relationship between the three sleep quality parameters and PA, dietary intake, and food group consumption both at home and dining out; this may be attributable to the presence of various undetected confounding factors. Such discrepancies are expected when using an observational research design. This study revealed that sleep duration, self-evaluated sleep quality, and sleep disturbance had variable associations with the investigated dietary and lifestyle factors before and during RF. Reduced consumption of delivered foods, sufficient consumption of plant-based proteins, and sufficient PA were all associated with better-self-evaluated sleep quality in our study population. Food quantity, quality, and eating habits along with PA are considered major determinant factors that profoundly affect sleep quality [24, 25, 30, 53]. Delivered foods are typically takeaway fast food, which tends to be energy-dense and high in fat, sugar, and animal protein, and low in plant-based and dietary fiber foods (54–56). Previous studies reported that excessive and reduced consumption of these energy-dense foods was associated with increased body weight and adverse health consequences, such as poor sleep quality and short sleep duration [57, 58]. We found an unexpected positive association between sleep duration and the decreased modification in the consumption of delivered foods. This may imply the presence of confounders and other undetected interfering factors in the relationship between sleep duration and consumption of delivered foods. Further, this unexpected positive correlation between sleep duration with the frequent ordering of delivered food could be due to the Covid-19 situation where the individuals are under-locked down or in mobility-restricted situations and cannot eat out. Unique plant proteins and bioactive phytochemicals mean that plant-based food has a high anti-inflammatory potential that maintains good health, lowers the risk for chronic ailments [59], and improves sleep quality [60, 61]. This may be explained by the alleviation of low-grade systemic inflammation, which adversely affects regulatory hormones for sleep mechanisms and is associated with disturbed sleep quality (62–64). In addition, a large body of literature supports the role of regular PA in improving sleep quality [65, 66]. This is attributed to the effect of PA in improving hormonal balance, reducing inflammation, and increasing the need for longer sleep to allow muscle recovery [67, 68]. In the same context, it is worth noting that the role of PA is increasingly emphasized as an influential factor for human health as revealed by recent work by Ammar et al. reporting that low PA was a considerable risk factor for the global disease burden, as low PA contributed $0.6\%$ of all age-standardized disability-adjusted life years globally in 2019 [69]. ## Sleep quality Smoking is a detrimental factor that has been repeatedly linked to decreased sleep quality [70, 71]. Similar to previous studies, we found that non-smokers had better self-evaluated sleep quality. This was consistent with extant evidence that non-smokers are less prone to developing low-grade systemic inflammation [72, 73], which is directly associated with decreased sleep quality because smoking is a leading source of potent oxidizing agents and harmful chemical toxicants that trigger an inflammatory response [73]. The anti-inflammatory bioactive substances in plant foods have been shown to alleviate inflammatory conditions and minimize the risk of decreased sleep quality [25]. This has been demonstrated in previous studies investigating sleep quality among adopters of the Mediterranean diet compared with those eating Western diets, with the latter having consistently poorer sleep quality than their Mediterranean diet counterparts [53, 60, 74]. Therefore, the low intake of antioxidants and bioactive phytochemicals from plant foods among smokers [75, 76] may indirectly aggravate oxidative stress and subsequent inflammatory status, resulting in poor sleep quality. Similarly, excessive fat intake, particularly heat-treated commercial fat in fast foods, is known for its high proinflammatory potential [77, 78], which adversely affects sleep quality (62–64). Therefore, our finding that quality of sleep was better among participants that reduced modification in consumed delivered foods throughout RF was unsurprising. We also found that lower screen use was significantly associated with better sleep quality among participants during RF. Exposure to short-wavelength light (blue light) that is emitted from digital devices and fluorescent/LED bulbs [79, 80] before bedtime may reduce sleepiness, increase alertness, and affect sleep quality [81]. Upon exposure to blue light, retinal ganglion cells send signals to the “central body clock” (i.e., the suprachiasmatic nucleus of the hypothalamus) via the retinohypothalamic tract that inhibits the secretion of melatonin and stimulates the secretion of the stress hormone cortisol [82]. ## Sleep disturbance Consumption of traditional homemade foods is expected to replace frequent ordering of delivered foods, which may be associated with improved sleep quality and decreased sleep disturbance [83]. In many Western diets, animal protein foods are mostly consumed in the form of high-fat foods, such as fried or processed meats (e.g., fried chicken, luncheon meat, and burgers) [84], which are associated with an elevated inflammatory state [85]. However, in other parts of the world (non-Western countries), animal protein is cooked through various heat treatments (e.g., baking, braising, and stewing), which decreases the risk of potential inflammation resulting from consuming processed animal proteins [86, 87]. Unexpectedly, we found that both delivered foods and traditional homemade foods were associated with an increased risk for sleep disturbance. In contrast to existing evidence [88], consumption of animal protein was not associated with high sleep disturbance or poor sleep quality in our study. ## Strengths and limitations To our knowledge, this is the largest study pertaining to the effect of RF on dietary, sleep, and lifestyle modifications in terms of sample size during the COVID-19 period. Our sample included different populations from different countries, and from various racial and ethnic backgrounds. Therefore, this was the first multi-national study to evaluate the complex relationships between different eating behaviors, dietary intake, lifestyle behaviors, and sleep quality parameters in the context of RF on such a large scale. Furthermore, our SEM provided robust results regarding the relationships between different dietary and lifestyle factors and three distinct sleep parameters among individuals observing RF. Although this study had several strengths, we acknowledge that there were some limitations. First, the claim that SEM allows conclusions regarding causal relationships between dependent and independent variables has been critically revised [89]; therefore, causality cannot be inferred in the present cross-sectional study. Second, all data were based on self-report and therefore may entail memory recall, which might have introduced recall and social desirability biases. In addition, depending on when participants completed the survey, the number of self-reported days fasted during Ramadan needed to be estimated if the survey was completed during Ramadan, and might not have been accurate because of unforeseen circumstances such as illness or travel. Moreover, the use of an online web-based survey and non-random, convenience sampling techniques might have introduced selection bias, meaning it is difficult to generalize the study results to all people fasting during RF. Although our questionnaire was derived from validated questionnaires, the lack of validation of the developed questionnaire in the present study might have resulted in some inaccuracies and inconsistencies. Fourth, the lack of a clear exclusion statement for participants diagnosed with eating and sleep disorders might have introduced some inaccuracies in the reported findings, although eating and sleep disorders are not very well diagnosed in many low- and middle-income population groups. Similarly, the lack of exclusion criteria for specific populations (e.g., athletes and older adults) might have decreased the homogeneity of the study population and allowed other factors to interfere with the targeted outcomes. Firth, the lack of control non-fasting group may allow for the effect of confounding factors to impact the sleep outcomes during RF. Finally, data were collected during lockdowns because of the COVID-19 pandemic, which might have interfered with habitual dietary and lifestyle behaviors during RF in non-pandemic times [90]. This is an important factor to consider as the lockdown period was reported to induce favorable and unfavorable dietary and lifestyle behaviors, as well as certain changes in circadian rhythm, PA, and sleep quality [27, 30, 31, 39]. ## Conclusion The SEM analysis showed that consuming plant-based proteins and practicing PA were strongly correlated with optimal sleep duration (7–9 h) among the Muslims who were fasting during the Ramadan month. In addition, smoking was significantly linked to both increased sleep disruption and decreased sleep quality. Consumption of dates, vegetables, fruits, and plant-based proteins appeared to be related to better-quality sleep. Furthermore, better sleep quality was linked to the decreased use of electronic devices (i.e., less exposure to blue light) and decreased consumption of delivered foods at night during RF. Contradictory findings were discovered regarding the connection between the three sleep parameters and eating-in versus eating-out. These findings suggested that improving the intake of fruits, vegetables, and plant-based proteins during RF are important factors that could help improve sleep quality during the month of Ramadan. Regular practice of PA and avoiding smoking are also important factors that may aid in improving sleep among individuals who practice fasting during Ramadan. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The study was conducted adhering to the code of ethics of the Helsinki Guidelines. Before collecting data, the study was approved by the Social Sciences Research Ethics Committee (REC) of the United Arab Emirates University (Approval Number ERS-2021-7308) and Tehran University of Medical Science (Approval Number IR.TUMS.FNM.REC.1400.022). Furthermore, the objectives and procedures of the study were stated before seeking informed consent from participants. The patients/participants provided their written informed consent to participate in this study. ## Ramadan intermittent fasting research collaborators National Pirogov Memorial Medical University, Vinnytsia, Ukraine: Abasi-Okot Akpan Udoyen (orcid.org/0000-0002-2947-4416). Faculty of Medicine, Al Quds University, Jerusalem, Palestine: Abdelrhman Muwafaq Janem (orcid.org/0000-0002-9854-7965). Faculty of Medicine, Helwan University, Cairo, Egypt: Abdullah Taha Zayed (orcid.org/0000-0001-7502-6342). Al-Quds University, Bethlehem, Palestine: Adriana Johny Skafi (orcid.org/0000-0001-7183-9231). Faculty of Medicine, Mansoura University Behbbit, Samannoud, Egypt: Ahmed Ashraf Elmoghazy (orcid.org/0000-0001-5313-7949). Qatar University, Mesaieed, Qatar: Ahmed Daniyal Nawaz (orcid.org/0000-0001-9424-2665). Department of Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates: Ahmed Juma AlKaabi. RCSI-UCD, Ayer Keroh, Malaysia: Amalin Najiha Binti Mohd Sabri. Iran Sports Medicine Research Center, Neuroscience Institute Sports Medicine Research Center, Neuroscience Institute Tehran University of Medical Sciences, Tehran, Iran: Amir Human-Hoveidaei (orcid.org/0000-0003-4607-354X). Kasr Alainy Faculty of medicine, Cairo, Egypt: Amir N. Attia (orcid.org/0000-0002-1537-3307). Kütahya Univerity of Health Sciences, kütahya, Türkiye: Ammar Mektebi. Trinity College Dublin, Dublin, Ireland: Amna Mohammed Al Zadjali. University of Tunis El Manar, Medical School of Tunis, Military Hospital of Tunis, Tunis, Tunisia: Anis Riahi (orcid.org/0000-0002-0411-983X). Department Public Health, Universitas Aufa Royhan Di Kota Padangsidimpuan, Padangsidimpuan, Indonesia: Anto Jamma Hadi (orcid.org/0000-0003-0944-5754). Orenburg state Medical University, Orenburg, Russia: Ashish Ramesh Dubey. Services Institute of Medical Sciences, Services Hospital Lahore House officer Services Institute of Medical Science, Lahore, Paksitan: Ayesha Iqbal. Lebanese university, Beirut, Lebanon: Bachar Jalal El ali. University of Aleppo, Aleppo, Syria: Bakri Yahia Roumi Jamal. Chemistry department, American University of Beirut, Beirut, Lebanon: Baraa Moujahed Hajjar. Department of Medicine, Vinnytsia National Medical University, Abuja, Nigeria: Chika Chizitelu Madekwe (orcid.org/0000-0002-5943-1636). Dentistry Programme of Mulawarman University Kerayan, Mulawarman University, Samarinda, Indonesia: Cicih Bhakti Purnamasari (orcid.org/0000-0003-1485-7817). Dentistry Programme, Mulawarman University Medical Education, Samarinda, Indonesia: Cicih Bhakti Purnamasari (orcid.org/0000-0003-1485-7817). Jordan University of Science and Technology, Irbid, Jordan: Dawlah Qasem Murshed Ahmed Saeed (orcid.org/0000-0001-8399-7923). Sbks Medical College, Ahmedabad, India: Dhaval Maunishkumar Shah (orcid.org/0000-0001-5425-8312). Public Health Asharej, Jawarneh MPH UAEU, Al Ain, United Arab Emirates: Dima Ibrahim (orcid.org/0000-0001-9394-6572). Faculty of Medicine, Dental Medicine and Pharmacy of Fez, Sidi Mohammed Ben Abdellah University, Fez, Morocco: Diyae Khadri. College of Medicine, National University for Science and Technology, Seeb, Oman: Eman Younis Al-Fahdi (orcid.org/0000-0001-5533-5914). Ambulatory Healthcare Services, Abu Dhabi, United Arab Emirates: Fatema Al Mazrouei. Department of Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Abu Dhabi, United Arab Emirates: Fatema Al Mazrouei. Dubai Medical College, Dubai, United Arab Emirates: Fatema Muneer Radhi (orcid.org/0000-0001-5972-3543). Dubai medical college, Dubai, United Arab Emirates: Fatema Yusuf Aljanabi (orcid.org/0000-0002-6619-1829). Ambulatory Healthcare Services, Abu Dhabi, United Arab Emirates: Fatima Al sheriff Al Zaabi. Department of Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Abu Dhabi, United Arab Emirates: Fatima Al sheriff Al Zaabi. Department of Endocrinology Syria, Faculty of Medicine, Aleppo University Hospital, University of Aleppo, Aleppo, Syria: Fatima Alzhra Mohamed Hanifa (orcid.org/0000-0003-1320-1795). Department of surgery, Jaber alahmed hospital, Kuwait City, Kuwait: Fatma Mustafa Ridha (orcid.org/0000-0002-3152-2560). Spinghar Thoracic Surgery Kabul, Kabul, Afghanistan: Fayaz Ahmad Momand (orcid.org/0000-0003-2964-8882). College of Food and Agriculture, United Arab Emirates University, Dubai, United Arab Emirates: Fayeza Hasan (orcid.org/0000-0002-8349-1057). Alexandria faculty of medicine, General practitioner, Alexandria, Egypt: Filopater Mar Gerges (orcid.org/0000-0002-3945-7417). Department of Nutrition Science, Universitas Muhammadiyah Surakarta, Sukoharjo, Indonesia: Firmansyah Firmansyah Firmansyah (orcid.org/0000-0002-9764-3461). Tanjungpura University, Pontianak, Indonesia: Frederick Putra Wijaya (orcid.org/0000-0002-4084-5134). MidHudson Family Medicine residency, Institute for family Health Family Medicine, Centerville, United States: Hassan B Nagy (orcid.org/0000-0001-9589-1333). OnDokuz Mayis University, Samsun, Turkey: Hussam Kiwan (orcid.org/0000-0003-1464-0511). Faculty of Medical Sciences, Lebanese university, Beirut, Lebanon: Ibrahim Khaled Salah El Din (orcid.org/0000-0002-3420-1279). An-Najah National University, Nablus, Palestine: Israa Hasan Hasan (orcid.org/0000-0002-4289-4802). University of Jordan, Amman, Jordan: Jehad Firas Samhouri (orcid.org/0000-0002-4878-7362). Ondokuz Mayis University, Samsun, Turkey: Kamil Sannah (orcid.org/0000-0002-8428-5191). MPH, North South University, Dhaka, Bangladesh: Lamisa Rahman (orcid.org/0000-0002-6100-6276). Aljabili Saglik Bilimleri Üniversitesi, Istanbul, Turkey: M. Munir (orcid.org/0000-0001-8104-7078). Monash University, Fawkner, Australia: Malik Bendak (orcid.org/0000-0003-3769-3974). Ain Shams General Hospital House, Khartoum, Sudan: Maram sirelkhatim elsayed (orcid.org/0000-0003-3560-9900). Tripoli central hospital, Tripoli, Libya: Marwa Mohammed morgom. Aleppo University Hospital, Aleppo, Syria: Maya Shahadeh Alassadi (orcid.org/0000-0001-9803-3422). Faculté de Médecine et de Pharmacie de Rabat, Temara, Morocco: Meryem Gounni (orcid.org/0000-0002-0360-2176). NGHA, KAMC, Riyadh, Saudi Arabia: Moath Ahmed Aldafas (orcid.org/0000-0002-9816-0672). Surgery department (Intern doctor), Princess Basma teaching hospital, Irbid, Jordan: Mohammad Mahmoud Jarrah (orcid.org/0000-0002-3339-9295). University of Aleppo, Aleppo, Syria: Mohammad / Shahrour (orcid.org/0000-0001-7506-9924). Medical Facuilty, Paktia University, Kabul, Afghanistan: Mohammad Elyas Wardak (orcid.org/0000-0002-7584-991X). Student Research Committee, Iran Clinical Research Development Center of Imam Khomeini Hospital, Jiroft University of Medical Sciences, Jiroft, Iran: Mohammad Pourfridoni (orcid.org/0000-0002-0510-3194). College of Medicine, Qatar University, Doha, Qatar: Mohammad Zulqurnain Haider (orcid.org/0000-0003-0598-5171). Faculté de Médecine, de Pharmacie et de Médecine Dentaire de Fès, Fez, Morocco: Mohammed Chakir (orcid.org/0000-0001-5232-4435). Kas al aini Clinic, Cairo, Egypt: Mohammed Al-Rsheed mostafa Omar Abueissa (orcid.org/0000-0002-8908-9026). Department of Physiology, Alzaiem Alazhari University, Khartoum, North Sudan: Mohannad Abdalfdeeel Almahie Shaban (orcid.org/0000-0002-2670-4802). Department of Human Physiology, Alzaiem Alazhari University, Khartoum, North Sudan: Mohannad Abdalfdeel Almahie Shaban (orcid.org/0000-0002-2670-4802). Ministry of Health Internship, Khartoum, Sudan: Monzir Musa Hamdan Mohammed (orcid.org/0000-0001-6385-8117). Ministry of Health, Khartoum, Sudan: Mosab Salah elmahi Ahmed (orcid.org/0000-0003-1056-1402). The Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan: Muhammad Daniyal Khan (orcid.org/0000-0002-1415-4970). College of Medicine, Menoufia University, Al Minufiyah, Egypt: Muhammad Sameh Amer (orcid.org/0000-0002-6562-3319). Psychology Department of Behavioral Science, Rehman College of Dentistry, Peshawar, Pakistan: Muttahid Shah (orcid.org/0000-0003-3423-9549). CMHS Family Medicine, UAEU, Al Ain, United Arab Emirates: Nadirah Ghenimi Ghenimi (orcid.org/0000-0003-0897-2587). Healthcare Psychology, Abu Dhabi, United Arab Emirates: Nailah Mahmood (orcid.org/0000-0001-5834-9422). ICU department, Alexandria Main University Hospital, Alexandria, Egypt: Nermeen Mohammed Afifi (orcid.org/0000-0001-5614-3340). Mosul medical collage, Mosul, Iraq: Noran Omar Mahmood. Ondokuz Mayis University, Samsun, Turkey: Noura Ahmad Kanjo (orcid.org/0000-0002-2348-1156). Emirates Health Services, Dubai, United Arab Emirates: Rahaf Ziad Abughosh (orcid.org/0000-0003-3225-6927). Faculty of Medicine, Ain Shams University, Cairo, Egypt: Ramy Rafaat Yassa (orcid.org/0000-0002-0675-9320). Department of Nutrition, Pertamedika College of Health Sciences, Jakarta, Indonesia: Rani Rahmasari Tanuwijaya M. Gizi (orcid.org/0000-0003-3438-0614). School of Nursing and Midwifery, Tehran University of Medical Sciences, Tarbiat Modares University, Tehran, Iran: Reza Heidari-Soureshjani (orcid.org/0000-0002-1212-1171). Shadan Institute of Medical Sciences and Research Centre, Peeramcheru, India: Romana Riyaz (orcid.org/0000-0003-0113-9824). CMH Institute of Medical Sciences, Multan, Pakistan: Rutab Tareen (orcid.org/0000-0002-1215-7991). Birat Medical College and Teaching Hospital Sukhrampur, Krishnanagar, Nepal: Sajjad Ahmed Khan (orcid.org/0000-0002-5315-9934). Kuwait University, Kuwait City, Kuwait: Sana Kalim Qureshi (orcid.org/0000-0001-7640-8334). Jordan University of Science and Technology, Irbid, Jordan: Sara Mohammed Ahmed Musleh Al-Badani (orcid.org/0000-0002-6986-8647). Alexandria Faculty of Medicine, Alexandria, Egypt: Sara Nazmy Ataallah (orcid.org/0000-0002-5900-1361). College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia: Saud Mohammed Alwatban (orcid.org/0000-0003-4787-7486). King Abdulaziz Medical City, Ministry National Guard Health Affairs, Riyadh, Saudi Arabia: Saud Mohammed Alwatban (orcid.org/0000-0003-4787-7486). King Abdullah International Medical Research Center, Riyadh, Saudi Arabia: Saud Mohammed Alwatban (orcid.org/0000-0003-4787-7486). Nangarhar Medical Faculty, Timergara, Pakistan: Sayed Mustafa Kamal (orcid.org/0000-0002-7098-8443). Khatam-Al-Nabieen University, Kabul, Afghanistan: Shams Ul Haq n/a Noori (orcid.org/0000-0002-8249-2023). University of Debrecen, Debrecen, Hungary: Somto Judith Okafor (orcid.org/0000-0001-9455-2348). Faculté de médecine d’Alger, Algiers, Algeria: Tadjadit Lydia (orcid.org/0000-0002-0294-3438). Sultan Qaboos University (Oman), Seeb, Oman: Tariq Ali Al Habsi (orcid.org/0000-0001-9010-8856). JSS Medical College, Mysore General Medicine, Kannur, India: Tejaswini Ashok (orcid.org/0000-0002-5888-0106). Jimma University, Jimma, Ethiopia: Tujuba Diribsa Benti (orcid.org/0000-0001-8453-5755). DNB GEM Hospital General Medicine, Chennai, India: Waseem N Ahmed (orcid.org/0000-0002-1687-922X). An-Najah National University, Bethlehem, Palestine: Yazan William Giacaman (orcid.org/0000-0002-3277-2481). MWACP West African College of Physicians, Federal Neuropsychiatric Hospital, Maiduguri, Nigeria: Yesiru Adeyemi KAREEM (orcid.org/0000-0001-5569-6592). MBBCh Dubai Medical College, Manama, Bahrain: Zainab Sadeq AlRabeea (orcid.org/0000-0002-8656-4557). ## Author contributions All authors provide substantial contributions to the conception or design of the work or the acquisition, analysis, or interpretation of data for the work, drafting the work or revising it critically for important intellectual content. The collaborators were involved in collecting the data, revising the manuscript critically, and finalizing and approving the final version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1040355/full#supplementary-material ## References 1. 1.World Population Review. Muslim Population by Country. (2022). 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Washif J, Pyne DB, Sandbakk Ø, Trabelsi K, Aziz AR, Beaven C. Ramadan intermittent fasting induced poorer training practices during the COVID-19 lockdown: a global cross-sectional study with 5529 athletes from 110 countries.. (2022.0) 39 1103-15. DOI: 10.5114/biolsport.2022.117576
--- title: Reliability and validity of the Chinese version of the Sakata Eating Behavior Scale short form and preliminary analysis of the factors related to the score of the scale authors: - Pu Ge - Xinpei Wang - Siying Gao - Jiaxin Liu - Yuyao Niu - Mengyao Yan - Siyuan Fan - Qiyu Li - Jinzi Zhang - Xiaonan Sun - Fei Wang - Yike Sun - Wenli Yu - Xinying Sun - Lian Yu - Yibo Wu journal: Frontiers in Nutrition year: 2023 pmcid: PMC10031001 doi: 10.3389/fnut.2023.1076209 license: CC BY 4.0 --- # Reliability and validity of the Chinese version of the Sakata Eating Behavior Scale short form and preliminary analysis of the factors related to the score of the scale ## Abstract ### Background The obesity rate in the Chinese population is increasing and there is a lack of short and reliable scales for measuring obesity-related eating behavior in China. The EBS-SF (Sakata Eating Behavior Scale short form) has only 7 entries and has shown good reliability in studies such as those in Japan. ### Objective To translate the EBS-SF into Chinese, check its reliability, validity and explore the related factors. ### Method The EBS-SF was translated into Chinese. 3,440 residents were investigated and 34 respondents were retested. Item analysis and reliability and validity tests were carried out. Personality characteristics, family health status and depression were investigated using the BFI-10, FHS-SF and PHQ-9 to investigate the factors associated with EBS-SF. The t-test, ANOVA and Pearson correlation was used to explore the related factors of its scores. ### Result Among 3,440 residents, 1,748 ($50.81\%$) were male and 1,692 ($49.19\%$) were female; 1,373 ($39.91\%$) were aged 36–50 years. All 7 items were qualified in the item analysis. As for reliability, the Cronbach's α was 0.870, the split-half reliability was 0.830, the test-retest correlation coefficient was 0.868. As for the structural validity, the standardized factor loadings were above 0.50, χ2 / df = 2.081,GFI = 0.999; NFI = 0.999; RFI = 0.996; RMSEA = 0.018, all qualified. The characteristics, personality, family health and depression were correlated with the score of the Chinese version of EBS short form. ### Conclusion The structural validity and reliability of the Chinese version of the EBS-SF are good and it can be used as a measurement tool to evaluate the eating behavior of Chinese. The scores of the EBS-SF may be related to the sociological characteristics, personality, family health, and depression status. ## 1. Introduction Over the past four decades, the dietary patterns of Chinese residents have undergone significant changes, with a rapid increase in the consumption of high-sugar and high-calorie foods and a rapid increase in the rates of overweight and obesity in the population. Obesity is increasingly becoming an important public health issue in China. As of 2019 statistics, the national prevalence of obesity is estimated at $6.8\%$, with obesity rates of $16.4\%$ in adults [1]. The development of obesity is closely related to uncontrolled eating behavior [2]. Meanwhile, obesity is associated with the onset and progression of various chronic diseases, such as hypertension [3] and diabetes [4], which is very worrying. The prevalence of these chronic diseases rises with the obesity rate, thus significantly increasing the burden on the medical and public health systems of China. The World Health Organization defines cut-off values for obesity based on the physical assessment such as body mass index (BMI): weight/height squared (kg/m2) [5, 6]. Internationally, assessment methods for the obesity also involved such as nutrition assessment, exercise assesssment [4] and also the use of obesity genes. Obviously, it is not realistic to use any of these single indexes to describe the cause of obesity. And the above tools are more appropriate as therapeutic aids to identify obesity rather than exploring the causes of obesity. A valid assessment in terms of food intake as well as eating behavior seems to contribute to a better understanding of obesity and to give an active life management program. In studies of the eating behavior of Chinese adults, it has been found that certain specific eating habits may constitute risk factors to obesity, such as the absence of a particular meal in a day. However, this may not be the only eating behavior that has an impact on obesity. There are complex interactions between eating behaviors and psychological, social and other factors. Related research confirms that body fat levels are closely related to eating behavior and that obesity may be driven by diet-related behavioral factors as well as pre-existing environmental and genetic factors [7]. Therefore, a comprehensive assessment of eating behaviors is needed to identify specific potential risks for obesity. Many studies exploring eating behaviors have used validated and reliable questionnaires that provide data tested in populations. For example, the Adult Eating Behavior Questionnaire (AEBQ) has been used all over the world. AEBQ is currently validated in Saudi, Poland, Portugal and China (8–11). The AEBQ has 35 items, using a five-point Likert scale involving eight subscales, which can be further divided into two dimensions of food approach and food avoidance behavior. Although this tool has been widely used and validated, the 35-item scale is very costly in terms of effort and time for the subjects and experimenters. The thirty-item Sakata Eating Behavior Scale widely used in Japan is divided into seven dimensions: concern cognition of constitution, motivation for eating, substitute eating and drinking, feeling of satiety, eating style, meal contents, and eating rhythm abnormalities [12]. Higher scores indicate poorer eating behaviors, which exacerbate obesity. The scale is used in some hospitals in Japan to assess eating behaviors to help patients change their eating behaviors and carry out obesity treatment. The EBS short form was simplified from the 30-item EBS based on item response theory [13]. Among the instruments measuring eating behavior associated with obesity, this is a much shorter scale, containing only seven items from the seven original dimensions. This means that the scale can be used in practice with less time and effort on the part of the user. The total score of the short scale is strongly correlated with the original scale ($r = 0.93$, $$P \leq 0.001$$). The EBS short form was validated in 1,032 Japanese adults aged 20–59 years and confirmed its validity. In China, there is a lack of short scales that can be used in large cross-sectional surveys and identify more potential obesity problems. The EBS short form could be a more useful tool to measure Chinese eating behaviors and help Chinese obese patients to control their eating behavior. Considering the interoperability of Asian food cultures, the use of a simplified Chinese version of the EBS short form in China would be more culturally advantageous than the original scale developed and validated in countries outside of Asia. The objective of this study was to translate the simplified Chinese version of the EBS short form, check its reliability and validity in China and explore the possible influences on its scores. ## 2. Methods This study is derived from a large cross-sectional study that callled “2021 China Family Health Index Investigation” [14]. The data used in this study are a subset of this national study. The survey is based on multi-stage sampling across the country. When selecting cities, all the provincial capital cities of provinces and autonomous regions, as well as municipalities in China were firstly included. Later, the random-number table was applied to randomly select the non-provincial capital cities of all provinces and autonomous regions in the country. Finally, 120 cities were selected within China. During the second phase of sampling, the population of each city was stratified according to gender, age, and urban-rural distribution, and the sample size of each stratum was 100 people, which was determined according to the demographic characteristics of the “Seventh National Census in 2021”. Convenience sampling was carried out on the premise of meeting quota requirements. After the completion of the sampling, with the favor of the investigator recruited in each city, the investigation was conducted from 10th July 2021 to 15th September 2021. In detail, investigators of each city used the online questionnaire star platform (https://www.wjx.cn/) to distribute questionnaires one-on-one and face-to-face with people in their cities. Then, after the investigator entered the questionnaire number, respondents would complete the questionnaire by clicking on the link. If the respondents held the ability to think but were not able to act to answer the questionnaire, the investigator would help finish the questionnaire based on the offered answers by the participants. After unified training, investigators recruited from various provinces and cities distributed questionnaires to respondents meeting the inclusion and exclusion criteria through field investigation. Prior to the investigation, the investigator would use consistent instructions to explain the research purpose to the respondents, emphasizing the anonymity of the research and obtaining the informed consent of the respondents. During the investigation, the respondents filled out the questionnaire by themselves and then handed it over to the investigator for inspection. If there were omissions or multiple elections, the investigator would communicate with the respondents on the spot whether a by-election or re-election is possible. After the questionnaires were collected, the questionnaires whose filling time was <2 min, incomplete filling and inconsistent filling content were excluded. ## 2.1.1. The EBS short form The EBS short form was developed in 2017 by Tayama and Ogawa et al., using item response theory (IRT) based on the Sakata Eating Behavior Scale (EBS) [13]. The scale consists of 7 items, including eating rhythm abnormalities, feeling of satiety, eating habits, cognition of constitution, meal content, substitute eating and drinking, and motivation for eating, and each item is scored on a 4-point scale (1 = strongly disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = strongly agree). The scores of the 7 items were summed up as the total score of the scale, and the higher the respondent's score on this scale, the worse the eating behavior of the respondent. ## 2.1.2.1. The 10-Item short version of the Big Five Inventory (BFI-10) The 10-Item short version of the Big Five Inventory (BFI-10) was applied to measure the personality characteristics of the respondents. The scale consists of 5 dimensions with 10 items, and each dimension contains 2 items, including Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness, on a 5-point Likert-type scale ranging from 1 (totally disagree) to 5 (totally agree). The full score of each dimension is 10 points, and the higher the score of a personality trait of the respondents, the more significant the personality trait of the respondents is. Several studies have shown that the BFI-10 has good reliability and validity (15–17). ## 2.1.2.2. The Short-Form of the Family Health Scale (FHS-SF) The family health level of the respondents was measured by the Short-Form of the Family Health Scale (FHS-SF) [18]. The scale was developed by Crandall and Weiss-Laxer et al., and the simplified Chinese version has been validated in the Chinese population by Wang et al. [ 19]. The FHS-SF is derived from the Family Health Scale-Long Form (FHS-LF), and it includes 4 dimensions, which are family social and emotional health process, family healthy lifestyle, family health resources, and family external social support, with a total of 10 items. Each item is scored on a 5-point Likert-type scale, among which items 6, 9 and 10 were scored in reverse. The higher the respondents' score on this scale, the higher the family health of the respondents. In the study, the Cronbach's coefficient of the scale was 0.851. ## 2.1.2.3. The Patient Health Questionnaire-9 (PHQ-9) The Patient Health Questionnaire-9 (PHQ-9) was used to measure the depression level of the respondents [20]. There are 9 items on the scale, and each item is scored on a 4-point Likert-type scale ranging from 0 (never) to 3 (nearly every day). A total score between 0 and 4 indicates no depression; a total score between 5 and 9 indicates possible mild depression; a total score between 10 and 14 indicates likely moderate depression; a score between 15 and 19 indicates that there may be moderate to severe depression; a total score between 20 and 27 indicates that there may be severe depression. The higher the respondents' score on this scale, the higher the level of depression the respondents may have. In the study, the Cronbach's coefficient of the scale was 0.940. ## 2.2. Research process Figure 1 shows the entire process of this study. Figure 1: Research process of the study. ## 2.2.1.1. Translation and back-translation of the scale Figure 2 shows the process of the scale translation stage. Figure 2: The process of the EBS-SF scale translation stage. Translation stage. Expert consultation was used to qualitatively evaluate the content validity of the Chinese version of the scale [21]. Authorization for translation and use was obtained from the developers of the EBS short form, and the scale was translated independently by two masters (1 master in public health and 1 master in English-Chinese translation). Afterward, they compared and discussed the similarities and differences between the 2 translations to form the first draft (called: T1) of the Chinese version of the scale. Back translation stage. The other 2 masters in English-Chinese translation were invited to back-translate the first draft T1 of the Chinese version of the scale separately without knowing the original English version of the EBS short form. The inconsistencies between the 2 back-translation manuscripts were revised, and a back-translation manuscript was finally formed. Audit stage. A master in medicine compared and analyzed the original scale and the back-translated scale, and adjusted and revised the differences between the two scales to form the second draft (called: T2) of the Chinese version of the EBS short form. ## 2.2.1.2. Cultural adjustment of the scale The cultural adjustment group was composed of 16 experts (two in each field) from eight fields of psychology, sociology, social medicine, humanistic medicine, nursing, health education, health service management and behavioral epidemiology. All the members of the group are familiar with the process and methods of scale localization. According to the Guidelines for the process of cross-cultural adaptation of self-report measures [22], experts made judgments on the cultural adaptability of each item in the second draft T2 of the Chinese Version of the EBS short form, and made certain modifications based on Chinese expression habits without changing the original meaning. According to the suggestions of experts, the third draft (called: T3) of the Chinese Version of the EBS short form used in the pre-investigation stage was formed. ## 2.2.2. Pre-investigation The purpose of the pre-investigation was to finalize the wording of the Chinese version of the questionnaire. In May 2021, a convenient sampling of 25 respondents who met the inclusion criteria was used for a pre-investigation using the general data questionnaire and the third draft T3 of the Chinese version of the EBS short form. For the pre-investigation, we collected only general characteristics of the respondents and tested the respondents with the Chinese version of EBS-SF. Respondents were asked about the clarity and intelligibility of each item after completion and they all indicated that the items were easy to understand. Therefore, the third draft T3 of the Chinese Version of the EBS short form was used for the formal investigation. ## 2.2.3.1.1. Inclusion/exclusion criteria The questionnaire selected for this research was selected from the questionnaires in “2021 China Family Health Index Investigation” which met the requirements of this research. The inclusion criteria of the participants were as follows: [1] Based on the original scale population the age ranged from 20 to 59 [13]; [2] Had the nationality of the People's Republic of China; [3] China's permanent resident population with an annual abroad time ≤ 1 month; [4] Participate in the study voluntarily and fill in the informed consent form; [5] Participants can complete the questionnaire by themselves or with the help of investigators; [6] Participants can understand the meaning of each item in the questionnaire; The exclusion criteria were as follows: [1] Persons with unconsciousness, eating disorders, or mental disorders; [2] Those who are participating in other similar research projects. ## 2.2.3.1.2. Investigation method The surveyors were recruited from online, and the surveyors conducted face-to-face interviews with the respondents and completed the online questionnaire on the spot for submission. ## 2.2.3.1.3. The characteristics of the respondents The characteristics of the participants that researchers collected comprised gender, age, marital status, educational level, Per capita monthly household income, current residence (urban / rural), region, occupational status, smoking status, drinking status. ## 2.2.4. Quality control The study conducted two rounds of pre-investigation before the formal investigation. Trained investigators distributed questionnaires to respondents and registered their codes one-on-one and face-to-face. Every Sunday evening during the investigation process, members of the research group communicated with the investigators to summarize, evaluate and give feedback on the questionnaires they collected. After the questionnaires were collected, two people conducted back-to-back logic checks and data screening. If singular values were found during data analysis, the original questionnaire must be found and checked with the investigator before proceeding to the next step of the analysis. ## 2.3. Statistical analysis Data analysis was performed using SPSS 22.0 and AMOS 21.0. Statistical description of the sociological characteristics of the respondents was carried out using percentage, mean, etc. The correlation coefficient method, CITC method and extreme group method were used for item analysis The correlation coefficient method required those correlations of items with coefficients r < 0.35 or $P \leq 0.50$ associated with the total scale score be dropped; the extreme group method required that items with t-values obtained using independent sample t-tests in the high (highest $27\%$) and low (lowest $27\%$) subgroups be dropped if the differences were not significant [23]. In addition, the CITC method requires that if the Cronbach's α of an item increases significantly after deletion, the item will be less internally relevant and should be deleted. Cronbach's α of internal consistency, split-half coefficient and test-retest reliability (intraclass correlation coefficient, ICC) were used for reliability analysis [24], and values ≥ 0.70 were considered to be good reliability. In addition, confirmatory factor analysis was performed using AMOS 21.0 to test the structural validity of the scale. χ2 / DF < 3, GFI > 0.9, NFI > 0.9, RFI > 0.9 and RMSEA < 0.08 were used as the criteria for good structural validity of the model (25–27). The mean and standard deviation were used to describe the central tendency and dispersion degree of continuous variables. What's more, t-test or ANOVA was used for comparison between groups. After ANOVA, Bonferroni method was used for multiple comparisons. The pearson correlation method was used to analyze the correlation between the scores of other scales and the EBS short form. All data were tested with a two-sided test, and $P \leq 0.05$ was considered statistically significant unless otherwise stated. ## 3.1. Characteristics of the participants and the score of the EBS short form The 2021 “China Family Health Index Investigation” started from July 10, 2021 to September 9, 2021. A total of 11,688 questionnaires were distributed, 11,031 valid questionnaires were recovered, and a total of 3,440 cases were sampled based on the data from the 2021 “China Family Health Index Investigation”. Among 3,440 participants, 1,748 ($50.81\%$) were male and 1,692 ($49.19\%$) were female; 1,373 ($39.91\%$) were people aged 36–50 years and the marital status of married people was the largest, with 2,278 ($66.22\%$). There were 1,662 cases ($48.31\%$) with a bachelor's degree or above and 1,667 cases ($48.46\%$) with household per capita monthly income below 4,500 yuan. Nearly three-quarters of them live in urban areas, with 2,551 cases ($74.16\%$). More than half of the cases were from the eastern part of the Chinese mainland, with 1,729 cases ($50.26\%$) (Table 1). Table 1 \| Item \| Item.1 \| N (%) \| Cronbach's αcoefficient \| The EBS-SF scores \| The EBS-SF scores.1 \| The EBS-SF scores.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| Mean ±SD \| t / F \| P \| \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| \| \| Male \| 1,748 (50.81) \| 0.879 \| 16.57 ± 4.618 \| 0.749 \| 0.454 \| \| \| Female \| 1,692 (49.19) \| 0.862 \| 16.46 ± 4.591 \| \| \| \| Age \| Age \| Age \| Age \| Age \| Age \| Age \| \| \| 20–25 \| 679 (19.74) \| 0.868 \| 17.47 ± 4.649 \| 30.227 \| < 0.001 \| \| \| 26–35 \| 802 (23.31) \| 0.865 \| 17.14 ± 4.524 \| \| \| \| \| 36–50 \| 1,373 (39.91) \| 0.866 \| 16.17 ± 4.523 \| \| \| \| \| 51–59 \| 586 (17.03) \| 0.870 \| 15.36 ± 4.514 \| \| \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| \| Unmarried \| 1,052 (30.58) \| 0.869 \| 17.48 ± 4.609 \| 36.073 \| < 0.001 \| \| \| Married \| 2,278 (66.22) \| 0.868 \| 16.05 ± 4.534 \| \| \| \| \| Else (divorced or widowed) \| 110 (3.20) \| 0.845 \| 16.98 ± 4.557 \| \| \| \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| \| \| Junior school and below \| 649 (18.87) \| 0.866 \| 16.46 ± 4.475 \| 0.201 \| 0.896 \| \| \| Senior school and middle vocational school \| 596 (17.33) \| 0.859 \| 16.42 ± 4.394 \| \| \| \| \| Junior college \| 533 (15.49) \| 0.884 \| 16.54 ± 4.727 \| \| \| \| \| Bachelor and above \| 1,662 (48.31) \| 0.871 \| 16.57 ± 4.690 \| \| \| \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| Per capita monthly household income, yuan \| \| \| ≤ 4,500 (663 dollars) \| 1,667 (48.46) \| 0.861 \| 16.66 ± 4.402 \| 2.197 \| 0.111 \| \| \| 4,501–9,000 (663-1326 dollars) \| 1,216 (35.35) \| 0.873 \| 16.30 ± 4.673 \| \| \| \| \| >9,000 (1,326 dollars) \| 557 (16.19) \| 0.885 \| 16.55 ± 5.017 \| \| \| \| Place of residence \| Place of residence \| Place of residence \| Place of residence \| Place of residence \| Place of residence \| Place of residence \| \| \| Urban \| 2,551 (74.16) \| 0.868 \| 16.51 ± 4.619 \| −0.176 \| 0.860 \| \| \| Rural \| 889 (25.84) \| 0.877 \| 16.54 ± 4.565 \| \| \| \| Region \| Region \| Region \| Region \| Region \| Region \| Region \| \| \| Eastern \| 1,729 (50.26) \| 0.875 \| 16.65 ± 4.702 \| 6.173 \| 0.002 \| \| \| Central \| 979 (28.46) \| 0.866 \| 16.68 ± 4.529 \| \| \| \| \| Western \| 732 (21.28) \| 0.861 \| 15.99 ± 4.434 \| \| \| \| Occupational status \| Occupational status \| Occupational status \| Occupational status \| Occupational status \| Occupational status \| Occupational status \| \| \| Unoccupied \| 724 (21.05) \| 0.842 \| 16.69 ± 4.249 \| 16.895 \| < 0.001 \| \| \| Employed \| 1,875 (54.51) \| 0.873 \| 16.20 ± 4.614 \| \| \| \| \| Student \| 701 (20.38) \| 0.869 \| 17.44 ± 4.648 \| \| \| \| \| Retired \| 140 (4.07) \| 0.910 \| 15.19 ± 5.213 \| \| \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| \| Never smoking \| 2,651 (77.06) \| 0.872 \| 16.39 ± 4.630 \| 4.253 \| 0.014 \| \| \| Smoker \| 538 (15.64) \| 0.864 \| 16.95 ± 4.529 \| \| \| \| \| Ex-smoker \| 251 (7.30) \| 0.864 \| 16.90 ± 4.430 \| \| \| \| Drinking frequency \| Drinking frequency \| Drinking frequency \| Drinking frequency \| Drinking frequency \| Drinking frequency \| Drinking frequency \| \| \| Never drinking \| 1,846 (53.66) \| 0.882 \| 16.12 ± 4.710 \| 15.273 \| < 0.001 \| \| \| Drinking, but not every week \| 974 (28.31) \| 0.842 \| 16.88 ± 4.397 \| \| \| \| \| Drinking weekly \| 620 (18.02) \| 0.866 \| 17.12 ± 4.496 \| \| \| The Chinese and English versions of EBS-SF is shown in Supplementary Table 1. The t-test or ANOVA was used to test for differences in the EBS short form scores at each level of sociological variables. The results showed that there were significant differences in the scores of the EBS short form in participants of different ages, marital status, regions, occupational status, smoking status and drinking frequency ($P \leq 0.05$) (Table 1). The Bonferroni method was further used for the post hoc test of the results of ANOVA (see Supplementary Tables 2–7 for details). The scores of the older age group on this scale are significantly lower than that of the younger age group and the unmarried group were significantly higher than those of the married group; the scores of the western regions were significantly lower than those of the eastern and central regions and student's scores above other occupational status. As for smoking status and drinking frequency, never smoking scored lower than the smokers, and never drinking scored lower than drinking. The mean score of the total score of the EBS short form was 16.52 ± 4.604 (Mean ± SD). See Table 2 for the score of each item in the EBS short form. The item with the highest mean score was “Item 3: eating fast” (2.52 ± 0.869), while the item with the lowest mean score was “item 7: when I buy food, I am satisfied when I buy more than I need” (2.21 ± 0.878). In each item, the choice with the largest number of people was “somewhat disagree” (items 2, 6, 7) or “somewhat agree” (items 1, 3, 4, 5), while the choice with the smallest number of people was “strongly agree”. Table 2 \| Item \| Scores (mea ±SD) \| Median (lower quartile, upper quartile) \| A N (%) \| B N (%) \| C N (%) \| D N (%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1. Eat at all different times \| 2.31 ± 0.886 \| 2 (2,3) \| 744 (21.63%) \| 1,121 (32.59%) \| 1,336 (38.84%) \| 239 (6.95%) \| \| 2. Do not feel satisfied unless I eat until full \| 2.25 ± 0.865 \| 2 (2,3) \| 714 (20.76%) \| 139 (40.41%) \| 108 (31.66%) \| 247 (7.18%) \| \| 3. Eat fast \| 2.52 ± 0.869 \| 3 (2,3) \| 497 (14.45%) \| 103 (30.17%) \| 153 (44.53%) \| 37 (10.84%) \| \| 4. Tend to gain weight more easily than others \| 2.51 ± 0.934 \| 3 (2,3) \| 579 (16.83%) \| 102 (29.85%) \| 134 (39.04%) \| 49 (14.27%) \| \| 5. Like oily foods \| 2.36 ± 0.874 \| 2 (2,3) \| 649 (18.87%) \| 1,191 (34.62%) \| 132 (38.63%) \| 271 (7.88%) \| \| 6.Eat if others around me are eating \| 2.36 ± 0.833 \| 2 (2,3) \| 542 (15.76%) \| 135 (39.45%) \| 128 (37.44%) \| 253 (7.35%) \| \| 7.When buying food, I am not content unless I buy more than necessary \| 2.21 ± 0.878 \| 2 (2,3) \| 805 (23.40%) \| 1,356 (39.42%) \| 1,040 (30.23%) \| 239 (6.95%) \| \| Total score \| 16.52 ± 4.604 \| 17 (14,20) \| \| \| \| \| ## 3.2.1.1. Correlation coefficient method The correlation analysis between each item and the total score of the questionnaire showed that each item score of the Chinese version of the EBS short form was significantly correlated with the total score of the scale, with correlation coefficients ranging from 0.694 to 0.794 ($P \leq 0.001$), above 0.35. ## 3.2.1.2. CITC method The Corrected item-total correlation (CITC) of the Chinese version of the EBS short form was all above 0.571, and the combination of the deleted Cronbach's α coefficient showed that the internal consistency coefficients did not change much after the deletion of the items (Table 3). Table 3 \| Item \| Cronbach's α after item deletion \| \| --- \| --- \| \| 1. Eat at all different times \| 0.861 \| \| 2. Do not feel satisfied unless I eat until full \| 0.844 \| \| 3. Eat fast \| 0.858 \| \| 4.Tend to gain weight more easily than others \| 0.861 \| \| 5. Like oily foods \| 0.843 \| \| 6. Eat if others around me are eating \| 0.846 \| \| 7. When buying food, I am not content unless I buy more than necessary \| 0.845 \| ## 3.2.1.3. Extreme group method The participants were ranked according to the total score of the scale, with $27\%$ of the participants at both ends of the scale falling into the two extreme groups. The CR values of the high score group (≥ 19 points) and the low score group (≤ 14 points) were all above 3.0 ($P \leq 0.001$). ## 3.2.2.1. Content validity The content validity of the EBS short form was qualitatively evaluated by the expert consultation method. Experts made a qualitative evaluation of the relevance of each item of the Chinese version of the EBS short form to its measured content. 16 experts (two experts in each field of psychology, sociology, social medicine, humanistic medicine, nursing, health education, health service management, and behavioral epidemiology, all with master's or doctoral degrees). All experts agreed that each item in the scale could reflect the content to be measured, indicating that the Chinese version of the EBS short form had good content validity. ## 3.2.2.2. Structural validity The EBS short form consisted of only one dimension, so only validation factor analysis was used to test the structural validity of the scale, and the scale was validated according to the single factor structural model of the original scale, and the model was revised 8 times according to the Modified Index (MI). After the modification, the standardized factor loadings of the validation factor analysis were between 0.55 and 0.80, and the residuals were positive and significant. The model fit indexes were χ2 / df = 2.081 < 3, GFI = 0.999 > 0.9, NFI = 0.999 > 0.9, RFI = 0.996 > 0.9, and RMSEA = 0.018 < 0.08, which is known from the fit indexes that the model structural validity is good and met the requirements. The results of the validation factor analysis are shown in Figure 3. Figure 3: The validation factor analysis model for the Chinese version of the EBS short form. ## 3.2.2.3. Reliability analysis The Cronbach's α coefficient of the Chinese version of the EBS short form was 0.870, and the split-half reliability was 0.830, which were all above 0.8. A total of 34 participants were selected by convenience sampling and retested at an interval of 2 weeks. We conducted a convenience sampling and retest reliability test based on respondents' willingness. The test-retest correlation coefficient (ICC) of the scale was 0.868, which was above 0.7. ## 3.3. The scores of the BFI-10, FHS-SF, and PHQ-9 and their correlation with the scores of the EBS short form The specific scores of each dimension of BFI-10, FHS-SF and PHQ-9 scale of the participants are shown in Table 4. In BFI-10, the two dimensions with higher scores were agreeableness (7.02 ± 1.532) and conscientiousness (6.92 ± 1.621), and the two dimensions with lower scores were extraversion (6.27 ± 1.591) and neuroticism (5.77 ± 1.478). The overall family health status was good (37.99 ± 6.670). According to the assessment of the depression level of the participants according to their PHQ-9 scores, it was found that of the 3,440 participants, 1,572 had no depression (PHQ-9 score of 0 to 4), 1,171 had possible mild depression (PHQ-9 score of 5 to 9) There were 360 likely to have moderate depression (PHQ-9 score of 10–14), 250 likely to have moderate depression (PHQ-9 score of 15–19), and 87 likely to have severe depression (PHQ-9 score of 20–27). Table 4 \| Scale \| Number of entries \| Score ranges \| Mean ±SD \| The scores of the EBS short form \| \| --- \| --- \| --- \| --- \| --- \| \| BFI-10 \| \| \| \| \| \| Extraversion \| 2.0 \| 2–10 \| 6.27 ± 1.591 \| −0.015 \| \| Agreeableness \| 2.0 \| 2–10 \| 7.02 ± 1.532 \| −0.220 \| \| Conscientiousness \| 2.0 \| 2–10 \| 6.92 ± 1.621 \| −0.264 \| \| Neuroticism \| 2.0 \| 2–10 \| 5.77 ± 1.478 \| 0.139 \| \| Openness \| 2.0 \| 2–10 \| 6.42 ± 1.504 \| −0.031 \| \| FHS-SF \| 10.0 \| 10–50 \| 37.99 ± 6.670 \| −0.328 \| \| PHQ-9 \| 9.0 \| 0–27 \| 6.21 ± 5.700 \| 0.396 \| The scores of the EBS short form were significantly positively correlated with the scores of “neuroticism” ($r = 0.139$, $P \leq 0.001$) and the PHQ-9 ($r = 0.396$, $P \leq 0.001$). The scores of “agreeableness” (r = −0.220, $P \leq 0.001$), “conscientiousness” (r = −0.264, $P \leq 0.001$) and the FHS-SF(r = −0.328, $P \leq 0.001$) were significantly negatively correlated with the scores of the EBS short form, as shown in Table 4. ## 4.1. The Chinese version of the simplified EBS short form has good reliability and validity In the field of eating behavior research, this study obtained preliminary results in the context of providing a national sample of the Chinese population, and the psychometric characteristics and factor structure of the simplified Chinese version of the EBS short form. The equivalence between the Chinese version of the scale and the original scale was fully ensured through a rigorous scale introduction process, including translation, back translation, expert consultation, and prediction. The good results of item analysis and the good correlation among the items and the total score of the scale illustrate the good reliability of the Chinese version of the EBS short form, the good representation of the items of this scale and the ability to measure eating behavior effectively. After validation factor analysis, sufficient structural validity represented that the original single factor structural model agreed well with the Chinese version of the EBS short form data. The factor loadings of all items in each dimension of the English version of the EBS short form were above 0.6, and the standardized factor loadings of the Chinese version of the factor model were between 0.55 and 0.80, which are more consistent with the original scale. It is generally considered that the Cronbach's α of the total scale is above 0.80, and the test-retest reliability is above 0.7, which means that the reliability of the scale is good. Cronbach's α coefficient and test-retest reliability of the total scale met the measurement requirements, indicating that the Chinese version of the EBS short form has good internal consistency, high reliability, and temporal stability. EBS short form in English with Cronbach's α was 0.830, and Cronbach's α for this study was 0.870, which is close to that of the previous study [13]. ## 4.2. Factors associated with the Chinese version of the EBS short form scores The EBS short form looks at the differences in eating habits between obese and healthy individuals, with higher scores reflecting worse eating habits. Four aspects were analyzed: personal characteristics, interpersonal networks, personal behavior and social factors. ## 4.2.1.1. Personality traits The Big Five Personality Inventory, a powerful model for measuring human personality traits, helps us to analyze differences in eating behavior in the population. Our study exploratively found that conscientiousness in the Big Five personality traits may have a significant negative impact on eating behavior, which is similar to the findings of Keller et al. [ 28]. Conscientiousness can lead to more consumption of recommended foods and less consumption of non-recommended foods. In addition, agreeableness is negatively associated with poor eating behavior and relevant to, low emotional under-eating and low emotional overeating [29, 30]. What's more, the present study showed a positive association between neuroticism and poor eating behavior, similar to previous studies [31]. It could be that neuroticism is associated with emotional eating [32]. Emotional instability, impulsiveness and poor self-control are not conducive to good eating habits. ## 4.2.1.2. Age In this study, age was viewed as a categorical variable, the significantly higher scores on eating behavior among those under 35 years of age in this study compared to those over 35 years of age may be related to the fact that emotional eating is more prevalent in younger age groups [33]. Younger populations have a stronger tendency to be more impulsive to attractive food stimuli, have lower self-regulation and seek higher pleasure, thus increasing the likelihood of undesirable eating behavior [30, 34]. ## 4.2.2.1. Marital status The results showed that married residents scored lower on the EBS short form than unmarried, which is consistent with a previous study [35]. This may be because people are encouraged and supervised by their spouses after entering marriage, which promotes healthy eating behavior [36]. ## 4.2.2.2. Family Health This study showed a significant negative correlation between the EBS short form scores and FHS-SF scores, which is consistent with previous studies [35]. A good family health function not only provides sufficient family health resources to help families better meet their daily needs and perform their functions but also promotes emotional communication between family members, therefore, it is helpful to develop good eating habits [18, 37]. ## 4.2.3.1. Lifestyle Lifestyles such as smoking and drinking were associated with high EBS short-form scores. This may be related to the fact that alcohol consumption stimulates appetite and even leads to binge eating [38, 39]. Quitting smoking may lead to uncontrolled eating as a result of quitting and thus enhancing the stimulatory response to food. Nicotine, on the other hand, has a suppressive effect on one's appetite, which could explain the relationship between smoking and disordered eating habits, for example, adolescents may smoke in the hope of losing weight [40]. ## 4.2.3.2. Emotional processing We also explored whether depression was associated with eating behavior, with PHQ-9 scale scores showing a significant positive correlation with eating behavior scores. It may be because depression affects a person's motivation to make food choices, thus reducing the likelihood of choosing healthy meals [41]. Although this result is consistent with the findings of a larger number of studies on eating behavior, more research is needed to confirm whether depression is associated with eating behavior in the broad sense (42–44). ## 4.2.4.1. Occupational status Poor eating behavior was more pronounced in the student group in this study compared to other occupational states. This is similar to the conclusion of Stok [45] that eating behavior usually becomes unhealthy during the transition from adolescence to young adulthood. When students start college, they are faced with new pressures and a lack of time for activity and financial ability, which can have a strong impact on their eating habits and willingness to engage in healthy behaviors [46]. ## 4.2.4.2. Region of residence Those in the central and eastern regions of the country performed less well in terms of eating behavior than those in the western regions, and some studies have found that residents living in developed regions are more likely to have eating behavior disorders, which is consistent with the results of this study [47]. The pace of life in economically developed areas is fast, and there are more diets available for people to choose. In addition, food-related takeaway and express delivery services are more convenient, and poorer eating behavior may be related to these factors. ## 4.3. Limitations In this study, we did not set a scalar scale, so we could not give the scalar validity of this scale and other validated scales, which is one of the limitations of this study. The simplified scale has only seven items, and the answer format with fewer items allows us to complete the test on a larger population. Due to practical difficulties in secondary data collection, the retest reliability of this study was based on a convenience sampling method without using the original study sample, which may be a source of bias. ## 5. Conclusion In conclusion, this study demonstrates that the simplified Chinese version of the EBS short form has good psychometric properties and is a valid and reliable tool for assessing eating behavior in Chinese adults. This tool is easy to use in population-based studies because of its self-reported nature and brevity. This study also explored the relationship between personal characteristics such as personality traits or depression status and eating behavior. Although there are some limitations, this study preliminary validated the reliability and validity of the simplified Chinese version of the EBS short form in a national Chinese sample. Future research should focus on the mechanism by which various related factors affect eating behavior, and should also focus on the relationship between the EBS short form score and obesity-related indicators such as BMI, also with the differences in eating behavior between obese and normal people. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Institutional Review Committee of Jinan University, Guangzhou, China (JNUKY-2021-018). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions LY and YW: directed and supervised the project. PG, XW, LY, and YW: designed research and had primary responsibility for the final content. PG, XW, and JL: drafted the first manuscript. PG, SG, XiaS, FW, and YW: scale translation. PG, SG, WY, YS, and YW: collected the data. PG, WY, YS, and YW: performed the statistical analysis. PG and XW: interpreted the results and wrote the manuscript. PG, XW, XiaS, FW, YN, MY, JZ, SF, QL, XinS, LY, and YW: provided critical revision for important intellectual content of the manuscript. All authors read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1076209/full#supplementary-material ## References 1. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019*. 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--- title: Effect of “maccog” TCM tea on improving glucolipid metabolism and gut microbiota in patients with type 2 diabetes in community authors: - Biyue Hu - Tongtong Yin - Jiajia Zhang - Minjing Liu - Hang Yun - Jian Wang - Renmei Guo - Jie Huang - Yixia Zhou - Hongyan Meng - Li Wang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031008 doi: 10.3389/fendo.2023.1134877 license: CC BY 4.0 --- # Effect of “maccog” TCM tea on improving glucolipid metabolism and gut microbiota in patients with type 2 diabetes in community ## Abstract ### Objectives This work aimed to observe the effect of consuming Chinese herb tea on glucolipid metabolism and gut microbiota in patients with type 2 diabetes mellitus (T2DM). ### Methods Ninety patients with T2DM were recruited from a community and randomly divided into the control group (CG) and intervention group (IG). CG maintained conventional treatment and lifestyle, and IG accepted additional “maccog” traditional Chinese medicine (TCM) tea (mulberry leaf, radix astragali, corn stigma, cortex lycii, radix ophiopogonis, and gynostemma) for 12 weeks. Glucolipid metabolism, hepatorenal function, and gut microbiota were then measured. ### Results After the intervention, the decreases in fasting plasma glucose (FPG) and total cholesterol (TC) were greater ($P \leq 0.05$) in IG than in CG, and those in glycosylated serum protein (GSP) were almost significantly greater ($$P \leq 0.066$$) in IG than in CG. The total protein (TP), albumin (ALB), and creatinine (CREA) levels in IG were significantly lower and their decreases were larger in IG than in CG ($P \leq 0.05$) after the intervention. The Ace and Chao1 indices in IG were slightly higher after the intervention ($$P \leq 0.056$$ and 0.052, respectively) than at baselines. The abundance of Actinobacteria, Lachnospiraceae, Bifidobacteriaceae, and Phascolarctobacterium increased significantly after the intervention in IG ($P \leq 0.05$), and the abundance was higher in IG than in CG ($P \leq 0.05$ or $P \leq 0.1$). The abundance of Clostridiales and Lactobacillales was negatively correlated with FPG ($P \leq 0.05$), Clostridiales and Lachnospiraceae was negatively correlated with GSP ($P \leq 0.05$), and Bacteroides/Firmicutes was positively correlated with both ($P \leq 0.05$). No adverse event was observed during the intervention. ### Conclusions Administration of “maccog” TCM tea for 12 weeks slightly improved glucolipid metabolism and significantly increased the abundance of beneficial gut microbiota in community patients with T2DM. The increase in beneficial bacteria abundance may be involved in the improvement of glucose metabolism indicators. In addition, this intervention is safe and feasible. ### Clinical trial registration https://www.chictr.org.cn/showproj.aspx?proj=31281, identifier ChiCTR1800018566. ## Introduction Type 2 diabetes mellitus (T2DM) is a metabolic disorder disease characterized by chronic hyperglycemia. Its main pathogenesis includes insulin resistance and insulin secretion disorders leading to absolute insulin deficiency [1]. The continued growth of patients with T2DM has led to a serious public health problem [2]. Its clinical treatment mainly depends on the long-term use of medicine to control the disease. Although treatment can have certain positive effects, a decrease in drug effect, an increase in adverse reaction, and potential complications may occur over time [3]. Compared with conventional hypoglycemic drugs, traditional Chinese medicine (TCM) may have lower cost and fewer side effects and delay the occurrence and development of diabetes complications through the overall regulation of the human body [4]. Many animal studies have shown that mulberry leaves, gynostemma, radix astragalus, and radix ophiopogonis can improve insulin sensitivity, stimulate insulin secretion, protect pancreatic islet function, inhibit intestinal carbohydrate intake, promote lipid metabolism, and inhibit peroxidation to achieve hypoglycemic, hypolipidemic, and anti-oxidative effects (5–8). These effects may play an important role as effective supplements and alternatives to conventional antidiabetic drugs. However, such studies in human subjects are lacking. In addition, medicine food homology (MFH) is provided as decoction in most studies [9, 10]. Although it may have certain effects on controlling blood glucose, the method is relatively complicated and inconvenient to carry out, which is not convenient for long-term auxiliary hypoglycemic use. Comparatively speaking, taking Chinese herbs as tea fits with the daily tea drinking habits of the Chinese. It enables the integration of nutritional health care into daily drinking, which causes minimal mental burden and time cost. As a result of fewer ingredients extracted from tea and consumed by human beings compared with decoction, TCM tea should be much safer (but also may be less effective, and this point may be compensated by adopted a longer period). Given their simplicity, convenience, acceptability, and wide applicability, TCM teas have received increasing attention in health care for chronic diseases. In recent years, studies have pointed out that the gut microbiota are closely related to the occurrence and development of T2DM. Intestinal flora participates in human metabolism, affects the energy balance, and regulates blood sugar levels and chronic inflammatory reaction [11]. If dysbacteriosis appears, it will produce immunotoxins, reduce the intestinal barrier function, increase intestinal permeability, promote metabolic endotoxemia, and trigger chronic inflammation, leading to obesity, insulin resistance, diabetes, and other diseases [12]. According to research findings, there are significant differences in gut microbiota between diabetics and nondiabetics. Compared with normal people, the number of Bifidobacterium, Prevotella, Clostridium coccoides, and Atopobium cluster in the gut microbiota of diabetics decreases significantly, whereas the number of Lactobacillus increases significantly [13, 14]. Moderate alterations in intestinal flora have been observed in patients with T2DM [15]. In addition, the abundance of Bacteroides in the T2DM group is only half that of the normal glucose tolerance (NGT) and prediabetes (pre-DM) groups, and Verrucomicrobiae has a significantly lower abundance in both the pre-DM and T2DM groups [16]. Furthermore, a correlation was found between different gut microbes and metabolic parameters. Others have shown that the amount of Faecalibacterium in the intestine of patients with T2DM is negatively correlated with levels of FPG, HbA1c, and 2h-postprandial blood glucose but is positively correlated with homeostatic model assessment of β-cell function [17]. In summary, changes in the structure of the intestinal flora are likely to be an important factor affecting T2DM, or even a target to control the development of the disease. Previous studies suggested that food formulas based on whole grains, TCM foods, and prebiotics can help patients with obesity improve blood pressure, lipid profile, and insulin sensitivity. Meanwhile, they can significantly reduce opportunistic pathogens, such as Enterobacteriaceae and Desulfovibrionaceae, and increase gut barrier-protecting Bifidobacterium spp [9]. The combination of metformin and traditional Chinese herbal formulas can significantly ameliorate hyperglycemia and hyperlipidemia in T2DM patients with hyperlipidemia, as well as change the structure of intestinal flora [18]. In addition, Gegen Qinlian Decoction (GQD) can significantly reduce FPG and HbA1c levels and enrich the amounts of Faecalibacterium, Bifidobacterium, and Gemmiger [10]. All of these results suggest that the changes in structure of gut microbiota may be a potentially effective adjunctive treatment for diabetes. Therefore, the purpose of this study was to explore the effect of “maccog” TCM tea (mulberry leaf, radix astragali, corn stigma, cortex lycii, radix ophiopogonis, and gynostemma) on improving blood glucose and gut microbiota of patients with T2DM. A 12-week randomized controlled intervention trial was conducted, and the internal relationship between blood glucose and gut microbiota was analyzed. The results are expected to provide reference for the adjuvant treatment of patients with T2DM and research about gut microbiota. ## Sample size estimate The sample size was estimated with an estimation formula for a two-group comparison with a random design: Zα: the corresponding Z value for type I error α; Zβ: the corresponding Z value for type II error β; σ: standard deviation; and δ: permissible error The unilateral test was adopted with α=0.05, β=0.10, Zα=1.645, and Zβ=1.282. HbA1c was considered the primary outcome indicator. We concluded X1 = $7.4\%$, X2 = $8.1\%$, δ=0.7, and σ=1.0 by reviewing the related literature [19]. The calculated result was n1=n2 = 36. Assuming an attrition rate of $20\%$, each group needed 44 patients, and a minimum of 88 patients was required. ## Participants All the participants were recruited from the Canglangting Healthcare Community of Suzhou. Patients with T2DM were included in the study (aged 40–80 years; receiving diet control or medication for more than 4 weeks). The exclusion criteria were as follows: had severely diseased heart, liver, kidney, brain, tumor, and acute diabetic complications; receiving other forms of dietary therapy; and allergic to medicine and food homologous substances or other allergic constitutions. This study was approved by the Ethics Committee of Soochow University (ECSU-201800076). This study conformed to the provisions of the Declaration of Helsinki and was registered with the Chinese Clinical Trial Registry (ChiCTR1800018566). Informed consent was obtained from all participants. ## Study procedure The study was a 12-week randomized controlled clinical trial. We randomly divided participants into two groups. First, a random number for each participant was made through SPSS software (IBM, Armonk, NY, USA), and the numbers of participants were sorted and included in the control group (CG, $$n = 45$$) and the intervention group (IG, $$n = 45$$). CG maintained the conventional treatment and the original lifestyle, and IG was administered with “maccog” TCM tea. The formula of “maccog” TCM tea included 2 g of mulberry leaf, 2 g of radix astragali, 2 g of corn stigma, 2 g of cortex lycii, 2 g of gynostemma, and 3 g of radix ophiopogonis, totaling 13 g. A procedure for the formation of the “maccog” TCM tea formula was added in the supplementary material. The “maccog” TCM tea was soaked in hot water for about 10 min, and participants drank the water when it became warm. The water could be refilled, and the recommended total volume was 1000–1500 mL every day. The prescribed total days were 6–7 days a week successively for 12 weeks. All the herbs were purchased from Suzhou Tianling Traditional Chinese Medicine Pieces Co., Ltd. (brand Li Liangji). During the intervention period, we required all participants to maintain their usual diet and lifestyles and provided no other health guidance. ## Measurements The primary outcomes included changes in fasting plasma glucose (FPG), glycosylated serum protein (GSP), glycated hemoglobin (HbA1c), blood lipids, and gut microbiota. The secondary outcomes included changes in hepatorenal function indicators, abnormal complaints, and adverse reactions. The FPG and GSP were measured before the intervention (baseline) and at 6 and 12 weeks after the intervention. The HbA1c, blood lipids, hepatorenal function, and gut microbiota were measured at baseline and week 12. The changes in medication, any adverse reactions, and abnormal complaints were recorded during the intervention. FPG was determined by using a Contour TS blood glucose meter and supporting test strips (BAYER, Germany). GSP was measured with a microplate reader (Multiskan™ FC, Finland). HbA1c was tested with an automatic special protein dry immunochromatographic analyzer (AS100, Alere Technologies AS, Oslo, Norway). Blood lipids, including total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL), and hepatorenal function indicators, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), total bilirubin (TBIL), alkaline phosphatase (ALP), blood urea nitrogen (BUN), creatinine (CREA), and uric acid (UA), were detected with an automatic chemistry analyzer (7100, Hitachi, Tokyo, Japan). Stool samples were kept frozen at −80°C until testing. DNA was extracted using the QIA amp Power Fecal DNA Kit (REF: 12830-50, QIAGEN, Germany). Fecal DNA was amplified by PCR using 16S amplicon PCR forward primer and 16S amplicon PCR reverse primer. The amplicons were pooled and sequenced by the HiSeq 2000 platform. Sequences were clustered into operational taxonomic units (OTUs) based on the SILVA128 database, at a similarity level of $97\%$. Alpha and beta diversities were calculated using Quantitative Insights Into Microbial Ecology (QIIME) and R (version 3.2.0). Meanwhile, the participants completed a self-designed questionnaire on basic information. Among the questions investigated, regular exercise referred to the performance of exercise of no less than three times a week, with each instance lasting for no less than 30 min [20]. Physical activity referred to occupational, household, transportation, and other daily activities [21]. Patients chose the appropriate options according to their actual situations. ## Statistical analysis Statistical analyses were carried out by IBM SPSS Statistic 26.0. Origin 2018 was used to draw diagrams. The Shapiro–Wilk test was used to verify the normal distribution of data. The data are expressed as the mean ± SD or M (P25, P75) unless specifically noted. Two-way repeated measures ANOVA was used to compare the data in CG and IG from different time points. The independent sample t-test, Mann–Whitney U test, and chi-square test were used to compare the changes (Δ, equals to week 12–week 0) after the intervention between the two groups. Correlations were assessed by Pearson or Spearman rank correlation analysis. $P \leq 0.05$ was set as statistically significant. Alpha and beta diversities were generated in QIIME and calculated based on weighted unifrac distance matrices. ## Basic characteristics of participants A total of 112 patients with T2DM were screened, and 90 of them who met the inclusion criteria were randomly divided into IG and CG (45 cases in each). Finally, 83 participants (41 in IG, aged 70.7 ± 5.5 years and 42 in CG, aged 71.6 ± 4.7 years) were included in the statistical analysis (Figure 1). No significant differences in baseline characteristics and clinical parameters (Table 1) were found between IG and CG. Figure 1: Participant flowchart. CG, control group; IG, intervention group. TABLE_PLACEHOLDER:Table 1 ## Compliance The results showed that $73.2\%$ of the participants had an average daily consumption dose of 1000–1500 mL, and $85.4\%$ of the participants consumed the “maccog” TCM tea for 6–7 days per week, which met the recommended volume and frequency (Table 2). Table 2 \| Unnamed: 0 \| Unnamed: 1 \| Number of participants \| Percentage (%) \| \| --- \| --- \| --- \| --- \| \| Average daily consumption dose (mL) \| >1500 \| 7 \| 17.1 \| \| Average daily consumption dose (mL) \| 1000–1500 \| 30 \| 73.2 \| \| Average daily consumption dose (mL) \| 500–1000 \| 4 \| 9.7 \| \| Average daily consumption dose (mL) \| Total \| 41 \| 100.0 \| \| Average weekly consumption day (days/week) \| 6–7 \| 35 \| 85.4 \| \| Average weekly consumption day (days/week) \| 4–5 \| 6 \| 14.6 \| \| Average weekly consumption day (days/week) \| 1–3 \| 0 \| 0.0 \| \| Average weekly consumption day (days/week) \| Total \| 41 \| 100.0 \| ## Efficacy of “maccog” TCM tea for glycemic control At baseline, there was no group difference in FPG and HbA1c, but GSP was higher in IG than in CG. Post-intervention levels of FPG and GSP showed no group difference ($P \leq 0.05$). The decrease in FPG at week 12 in IG was larger than that in CG ($$P \leq 0.033$$), and the decrease in GSP at week 12 in IG was almost significantly greater than that in CG ($$P \leq 0.066$$). GSP in IG was significantly lower after 6 weeks and 12 weeks of intervention compared with that pre-intervention ($P \leq 0.01$). Neither the pre- and post-intervention levels nor the changes in HbA1c showed group differences ($P \leq 0.05$) (Table 3). Table 3 \| Indicators \| Group \| Time points \| Time points.1 \| Time points.2 \| Z/P b \| Ft/Pt \| Fg/Pg \| Fi/Pi \| △ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Indicators \| Group \| Pre \| Week 6 \| Week 12 \| Z/P b \| Ft/Pt \| Fg/Pg \| Fi/Pi \| △ \| \| FPG \| CG \| 7.55 ± 2.09 \| 7.74 ± 1.85 \| 7.70 ± 1.75 \| \| 0.471/0.598 \| 1.004/0.322 \| 2.338/0.103 \| 0.30 (-0.40,1.03) \| \| FPG \| IG \| 8.32 ± 2.67 \| 7.99 ± 2.39 \| 7.60 ± 1.95 \| \| 0.471/0.598 \| 1.004/0.322 \| 2.338/0.103 \| -0.40 (-1.65, -0.80) \| \| FPG \| Z \| \| \| \| \| \| \| \| -2.133 \| \| FPG \| Pa \| \| \| \| \| \| \| \| 0.033 \| \| GSP \| CG \| 3.22 ± 0.35 \| 2.94 ± 0.29† \| 2.81 ± 0.28†† \| \| 86.420/<0.01 \| 2.191/0.147 \| 4.672/0.016 \| -0.35 (-0.67, -0.18) \| \| GSP \| IG \| 3.43 ± 0.56 \| 3.03 ± 0.38† \| 2.83 ± 0.37†† \| \| 86.420/<0.01 \| 2.191/0.147 \| 4.672/0.016 \| -0.47 (-0.92, -0.25) \| \| GSP \| F \| 4.220 \| 1.662 \| 0.056 \| \| \| \| \| -1.840 \| \| GSP \| P \| 0.047 \| 0.210 \| 0.815 \| \| \| \| \| 0.066 \| \| HbA1c \| CG \| 7.0 (6.4, 7.7) \| / \| 6.9 (6.2, 7.8) \| -1.031/0.303 \| \| \| \| -0.2 (-0.2, 0.3) \| \| HbA1c \| IG \| 7.0 (6.4, 8.6) \| / \| 6.9 (6.5, 8.7) \| -0.598/0.550 \| \| \| \| 0.0 (-0.4, 0.3) \| \| HbA1c \| Z \| -0.515 \| \| -0.647 \| \| \| \| \| -0.394 \| \| HbA1c \| P a \| 0.606 \| \| 0.517 \| \| \| \| \| 0.694 \| ## Efficacy of “maccog” TCM tea for blood lipid profile No significant differences were found in blood lipid levels between the two groups at baseline. TC and LDL in IG were significantly lower after 12 weeks of the intervention compared with those before the intervention ($P \leq 0.05$). TC was significantly lower in IG than in CG at week 12 ($P \leq 0.05$). The decrease of TC in IG was larger than that in CG. HDL/LDL in both groups significantly increased after the intervention ($P \leq 0.05$) (Table 4). Table 4 \| Unnamed: 0 \| Unnamed: 1 \| CG \| IG \| Ft/Pt \| Fg/Pg \| Fi/Pi \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| TC (mmol/L) \| Pre \| 4.60 ± 0.93 \| 4.49 ± 1.04 \| 29.181/<0.01 \| 5.060/0.030 \| 20.053/<0.01 \| \| TC (mmol/L) \| Week 12 \| 4.49 ± 0.93 \| 3.84 ± 0.92†* \| 29.181/<0.01 \| 5.060/0.030 \| 20.053/<0.01 \| \| TC (mmol/L) \| F \| 1.510 \| 48.484 \| \| \| \| \| TC (mmol/L) \| P \| 0.226 \| <0.01 \| \| \| \| \| TC (mmol/L) \| △ \| -0.11 ± 0.58 \| -0.65 ± 0.60† \| 4.167/<0.01 \| 4.167/<0.01 \| 4.167/<0.01 \| \| TG (mmol/L) \| Pre \| 1.78 ± 1.33 \| 1.52 ± 1.18 \| 2.569/0.117 \| 1.429/0.239 \| 0.413/0.524 \| \| TG (mmol/L) \| Week 12 \| 1.71 ± 1.11 \| 1.34 ± 0.76 \| 2.569/0.117 \| 1.429/0.239 \| 0.413/0.524 \| \| TG (mmol/L) \| △ \| -0.07 ± 0.91 \| -0.18 ± 0.80 \| 0.573/0.568 \| 0.573/0.568 \| 0.573/0.568 \| \| HDL (mmol/L) \| Pre \| 1.25 ± 0.32 \| 1.32 ± 0.35 \| 0.295/0.590 \| 0.063/0.802 \| 3.634/0.064 \| \| HDL (mmol/L) \| Week 12 \| 1.32 ± 0.37 \| 1.29 ± 0.38 \| 0.295/0.590 \| 0.063/0.802 \| 3.634/0.064 \| \| HDL (mmol/L) \| △ \| 0.07 ± 0.17 \| -0.04 ± 0.35 \| 1.752/0.085 \| 1.752/0.085 \| 1.752/0.085 \| \| LDL (mmol/L) \| Pre \| 2.69 ± 0.85 \| 2.75 ± 0.68 \| 11.403/<0.001 \| 0.085/0.772 \| 4.351/0.043 \| \| LDL (mmol/L) \| Week 12 \| 2.55 ± 0.81 \| 2.38 ± 0.78* \| 11.403/<0.001 \| 0.085/0.772 \| 4.351/0.043 \| \| LDL (mmol/L) \| F \| 3.995 \| 11.655 \| \| \| \| \| LDL (mmol/L) \| P \| 0.052 \| <0.01 \| \| \| \| \| LDL (mmol/L) \| △ \| -0.14 ± 0.47 \| -0.36 ± 0.68 \| 1.702/0.093 \| 1.702/0.093 \| 1.702/0.093 \| \| HDL/LDL \| Pre \| 0.52 ± 0.23 \| 0.51 ± 0.20 \| 13.706/<0.001 \| 0.115/0.736 \| 1.154/0.289 \| \| HDL/LDL \| Week 12 \| 0.57 ± 0.25 * \| 0.62 ± 0.32* \| 13.706/<0.001 \| 0.115/0.736 \| 1.154/0.289 \| \| HDL/LDL \| F \| 18.561 \| 6.515 \| \| \| \| \| HDL/LDL \| P \| <0.01 \| 0.015 \| \| \| \| \| HDL/LDL \| △ \| 0.06 ± 0.09 \| 0.11 ± 0.26 \| -1.059/0.295 \| -1.059/0.295 \| -1.059/0.295 \| ## Efficacy of “maccog” TCM tea for hepatorenal function parameters There was no significant difference between the two groups at baseline. The TP, ALB, and CREA levels at week 12 were significantly lower in IG than in CG ($P \leq 0.01$). The decrease in AST, TP, ALB, and CREA was larger in IG than in CG at week 12 ($P \leq 0.05$). The AST, TP, ALB, ALP, and CREA levels in IG were significantly lower after 12 weeks of the intervention compared with those before the intervention ($P \leq 0.05$). The levels of all indicators were within normal range (Table 5). Table 5 \| Unnamed: 0 \| Unnamed: 1 \| CG \| IG \| Ft/Pt \| Fg/Pg \| Fi/P i \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ALT (U/L) \| Pre \| 21.07 ± 10.17 \| 20.50 ± 5.70 \| 0.601/0.443 \| 0.666/0.419 \| 0.827/0.369 \| \| ALT (U/L) \| Week 12 \| 21.26 ± 10.62 \| 19.03 ± 8.28 \| 0.601/0.443 \| 0.666/0.419 \| 0.827/0.369 \| \| ALT (U/L) \| △ \| 0.19 ± 7.59 \| -1.47 ± 8.18 \| 0.958/0.341 \| 0.958/0.341 \| 0.958/0.341 \| \| AST (U/L) \| Pre \| 18.50 ± 7.03 \| 20.27 ± 6.90 \| 10.674/<0.01 \| 0.131/0.719 \| 3.716/0.061 \| \| AST (U/L) \| Week 12 \| 18.10 ± 6.77 \| 17.47 ± 4.25 * \| 10.674/<0.01 \| 0.131/0.719 \| 3.716/0.061 \| \| AST (U/L) \| F \| 0.341 \| 10.421 \| \| \| \| \| AST (U/L) \| P \| 0.563 \| <0.01 \| \| \| \| \| AST (U/L) \| △ \| -0.40 ± 4.49 \| -2.80 ± 5.55† \| 2.161/0.034 \| 2.161/0.034 \| 2.161/0.034 \| \| TP (g/L) \| Pre \| 71.33 ± 5.06 \| 69.66 ± 4.44 \| 1.331/0.256 \| 10.502/<0.01 \| 6.180/0.017 \| \| TP (g/L) \| Week 12 \| 72.24 ± 4.29 \| 67.35 ± 5.42† * \| 1.331/0.256 \| 10.502/<0.01 \| 6.180/0.017 \| \| TP (g/L) \| F \| 1.110 \| 6.718 \| \| \| \| \| TP (g/L) \| P \| 0.298 \| 0.013 \| \| \| \| \| TP (g/L) \| △ \| 0.91 ± 5.60 \| -2.31 ± 5.70† \| 2.595/0.011 \| 2.595/0.011 \| 2.595/0.011 \| \| ALB (g/L) \| Pre \| 44.80 ± 3.26 \| 43.72 ± 3.17 \| 5.138/0.029 \| 14.507/<0.01 \| 19.719/<0.01 \| \| ALB (g/L) \| Week 12 \| 45.31 ± 3.61 \| 41.91 ± 3.47† * \| 5.138/0.029 \| 14.507/<0.01 \| 19.719/<0.01 \| \| ALB (g/L) \| F \| 2.604 \| 16.283 \| \| \| \| \| ALB (g/L) \| P \| 0.114 \| <0.01 \| \| \| \| \| ALB (g/L) \| △ \| 0.50 ± 2.02 \| -1.81 ± 2.88† \| 4.236/<0.01 \| 4.236/<0.01 \| 4.236/<0.01 \| \| TBIL (µmol/L) \| Pre \| 9.69 ± 4.09 \| 10.29 ± 2.28 \| 2.754/0.105 \| 0.033/0.857 \| 1.608/0.212 \| \| TBIL (µmol/L) \| Week 12 \| 10.82 ± 5.03 \| 10.48 ± 2.75 \| 2.754/0.105 \| 0.033/0.857 \| 1.608/0.212 \| \| TBIL (µmol/L) \| △ \| 1.12 ± 4.50 \| 0.19 ± 2.34 \| 1.186/0.240 \| 1.186/0.240 \| 1.186/0.240 \| \| ALP (U/L) \| Pre \| 100.12 ± 21.14 \| 101.10 ± 18.53 \| 26.841/<0.01 \| 0.705/0.406 \| 1.765/0.192 \| \| ALP (U/L) \| Week 12 \| 94.76 ± 21.80 \| 88.02 ± 17.41 * \| 26.841/<0.01 \| 0.705/0.406 \| 1.765/0.192 \| \| ALP (U/L) \| F \| 2.552 \| 14.696 \| \| \| \| \| ALP (U/L) \| P \| 0.118 \| <0.01 \| \| \| \| \| ALP (U/L) \| △ \| -5.36 ± 21.73 \| -13.07 ± 21.84 \| 1.613/0.111 \| 1.613/0.111 \| 1.613/0.111 \| \| BUN (mmol/L) \| Pre \| 5.75 ± 1.23 \| 5.53 ± 0.82 \| 0.038/0.846 \| <0.01/0.995 \| 2.053/0.160 \| \| BUN (mmol/L) \| Week 12 \| 5.61 ± 1.42 \| 5.76 ± 1.01 \| 0.038/0.846 \| <0.01/0.995 \| 2.053/0.160 \| \| BUN (mmol/L) \| △ \| -0.15 ± 1.23 \| 0.22 ± 1.24 \| -1.357/0.179 \| -1.357/0.179 \| -1.357/0.179 \| \| CREA (µmol/L) \| Pre \| 74.06 ± 15.77 \| 75.58 ± 15.23 \| 24.203/<0.01 \| 2.408/0.129 \| 19.456/<0.01 \| \| CREA (µmol/L) \| Week 12 \| 73.98 ± 16.06 \| 63.41 ± 13.60† * \| 24.203/<0.01 \| 2.408/0.129 \| 19.456/<0.01 \| \| CREA (µmol/L) \| F \| 0.006 \| 26.043 \| \| \| \| \| CREA (µmol/L) \| P \| 0.940 \| <0.01 \| \| \| \| \| CREA (µmol/L) \| △ \| -0.08 ± 6.92 \| -12.17 ± 15.26† \| 4.626/<0.01 \| 4.626/<0.01 \| 4.626/<0.01 \| \| UA (µmol/L) \| Pre \| 328.08 ± 70.56 \| 323.93 ± 84.13 \| 0.012/0.913 \| 0.041/0.840 \| 2.777/0.103 \| \| UA (µmol/L) \| Week 12 \| 320.91 ± 69.92 \| 330.83 ± 66.91 \| 0.012/0.913 \| 0.041/0.840 \| 2.777/0.103 \| \| UA (µmol/L) \| △ \| -7.16 ± 39.83 \| 6.89 ± 40.35 \| -1.597/0.114 \| -1.597/0.114 \| -1.597/0.114 \| ## Efficacy of “maccog” TCM tea for gut microbiota The overall microbiota structure at the phylum level in IG and CG before and after the intervention is presented in Figure S1-A in supplementary material. The main phyla of IG and CG were Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria, with Bacteroidetes being the most abundant (Figure S1-A). To compare the overall gut microbiota structure in participants, principal coordinate analysis (PCoA) according to OTUs of each sample was implemented to provide a glimpse of gut microbial dynamics between CG and IG. The weighted results of PCoA were PC1 = $38.66\%$ and PC2 = $18.51\%$ of total variations (Figure S1-B). No significant difference was noted between the two groups at baseline. The Ace and Chao1 indices in IG were slightly higher after the intervention, and the values were close to significant differences ($$P \leq 0.056$$, $$P \leq 0.052$$; Table 6). The abundance of Actinobacteria, Lachnospiraceae, Bifidobacteriaceae, Phascolarctobacterium, and Bifidobacterium after the intervention was significantly higher in IG than that at baseline ($P \leq 0.05$). The abundance of Lachnospiraceae and Phascolarctobacterium after the intervention was significantly higher in IG than in CG ($P \leq 0.05$) (Figure 2). ## Correlation between gut microbiota abundance and glucose metabolism indicators The abundance of Clostridiales and Lactobacillales was negatively correlated with FPG levels ($P \leq 0.05$), and the abundance of Clostridiales and Lachnospiraceae was negatively correlated with GSP levels ($P \leq 0.05$). The abundance of Bacteroides/Firmicutes was positively correlated with FPG and GSP levels ($P \leq 0.05$) (Table 7). Table 7 \| Unnamed: 0 \| FPG \| FPG.1 \| GSP \| GSP.1 \| HbA1c \| HbA1c.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| r \| P \| r \| P \| r \| P \| \| Clostridiales \| -.197* \| .034 \| -.221* \| .017 \| -.154 \| .099 \| \| Lachnospiraceae \| -.117 \| .212 \| -.188* \| .043 \| -.076 \| .419 \| \| Lactobacillales \| -.204* \| .028 \| -.178 \| .056 \| -.136 \| .145 \| \| Bacteroides/Firmicutes \| .200* \| .031 \| .237* \| .010 \| .156 \| .094 \| ## Changes in diet and medication during the intervention One participant in CG had changes in the diet during the study, which was more frequent eating outside and the resulting increased intake of greasy food. In IG, there were no significant changes in diet or other lifestyle behaviors. The medication (for diabetes) doses of two patients decreased and that of one increased in IG, and those of two patients decreased in CG. Medication doses were adjusted either by the patients (on the basis of self-monitored blood glucose levels) or their doctors (Table 8). Table 8 \| Group \| Change \| n \| Details \| \| --- \| --- \| --- \| --- \| \| IG \| Increase \| 1 \| (1) Laideshi insulin 14U/day→15U/day (doctor’s prescription) \| \| IG \| Decrease \| 2 \| (1) Youmile insulin 19U/day→17U/day (self-adjustment) \| \| IG \| Decrease \| 2 \| (2) Yamoli 1 mg/day→0.5 mg/day (doctor’s prescription) \| \| CG \| Increase \| 0 \| \| \| CG \| Decrease \| 2 \| (1) Novoline 25U →20/25U in the morning, and 15U→10U in the evening (self-adjustment) \| \| CG \| Decrease \| 2 \| (2) Metformin 0.25 g/day → withdrawal (doctor’s prescription) \| ## Adverse reactions and physical changes During the intervention, no hypoglycemia was reported. In IG, one participant reported skin itching, three reported xerostomia, five reported improvement in vigor, six reported defecation increased, five reported stool form improvement, five reported sleep quality improvement, and two reported remarkable improvement with blurred vision. In CG, two participants reported vigor improvement and three reported an increase in defecation. ## Discussion By providing “maccog” TCM tea to patients with T2DM, our research found some minor positive effects of such an intervention on glucolipid metabolism and gut microbiota and proved its safety and feasibility. The minor positive effects in glycemic control were demonstrated by the significantly larger decrease in FPG and the slightly larger reduction of GSP in IG compared with CG. This finding was similar to the results of Huyen and Chatterji’s clinical studies [19, 22]. After 12 weeks of gynostemma pentaphyllum tea intervention in patients with T2DM, Huyen et al. found that the FPG levels in the experimental group significantly decreased compared with those in the placebo group [19]. After 12 weeks of compound capsule (containing Morus alba, Artemisia dracunculus, Urtica dioica, Cinnamomum zeylanicum, and Taraxacum officinale) intervention in patients with T2DM, Chatterji et al. found that the FPG levels of participants significantly decreased compared with those at baseline [22]. In the mechanism by which TCM affects glucose metabolism, previous studies have shown that mulberry leaf polysaccharide effectively normalizes hepatic glucose metabolism and insulin signaling by inhibiting the expression of protein–tyrosine phosphatase 1B and mitigating oxidative stress in the livers of rats with type 2 diabetes [23]. A polysaccharide extracted from gynostemma pentaphyllum revealed excellent capacity in inhibiting α-glucosidase activity and glucose absorption and curing diabetic mice [5]. Astragalus membranaceus improves glucose metabolism and insulin sensitivity in T2DM by directly enhancing insulin-stimulated glucose uptake in insulin-resistant myotubes with improved insulin signalling and inflammatory response and oxidative stress [24]. Polysaccharides from corn silk exert appreciable hypoglycemic activity by inhibiting α-amylase and α-glucosidase and enhancing glucose uptake in rat L6 skeletal muscle cells [25]. Ophiopogon japonicus extract can significantly lower blood glucose levels on experimental type 2 diabetic rats by improving insulin sensitivity and increasing glycogen contents in liver and skeletal muscle [26]. Taurine in the cortex lycii can reduce the lipid peroxidation level under hyperglycemia and protect β-cells, and the polysaccharides from cortex lycii can inhibit α-glucosidase and lower the glucose absorption rate [27]. In this study, the combined use of these substances for 12 weeks significantly decreased FPG levels and slightly decreased GSP levels in patients with T2DM. Although the change in GSP at week 12 was only close to a significant difference, the result suggested a potential possibility of significant changes in glucose metabolism in the case of a longer intervention period or larger doses. However, as the primary outcome of our study, HbA1c showed no significant difference or change. The lack of change in HbA1c may be related to the relatively low HbA1c baseline levels and the intervention method (period, dosage, and administration method). First, in the meta-analysis, the reduction in HbA1c was found to be positively correlated with its baseline level [28]. However, most of the participants recruited in this study had relatively low baseline HbA1c levels compared with those trials in the meta-analysis, and this factor may limit the effect of “maccog” TCM tea. Second, HbA1c reflects glucose metabolism in the past 2–3 months, but our intervention lasted for only 12 weeks [29]. Considering that “maccog” TCM tea may take quite long to produce a marked effect, a prolonged intervention period may yield significant changes in HbA1c. Third, for better safety and acceptability, the substances applied in this study had a relatively small dosage and were consumed by drinking tea, which may affect the changes in HbA1c. Different effects on FPG and HbA1c were found in patients with T2DM who accepted different dosages of traditional Chinese herbal formula intervention with GQD (high dosage at 240 g and medium dosage at 144 g demonstrated significant reductions in changes in FPG and HbA1c levels, whereas placebo at 2.16 g and low dosage at 48 g did not) [10]. Asai et al. conducted a mulberry leaf extract (DNJ) intervention of 12 weeks via tea drinking mode in patients with T2DM and found that 6 mg of DNJ did not significantly reduce the FPG and HbA1c levels compared with placebo [30]; Hu et al. used a capsule (4.3 g) containing MFH such as corn stigma, mulberry leaves, and hawthorn intervention in patients with T2DM and found no statistical difference in FPG and HbA1c levels between IG and the placebo after 12 weeks of intervention [31]. In our study, the total amount of “maccog” TCM tea was only 13 g. Whether a large dosage can produce more significant change in glucose metabolism indices in patients with T2DM should be explored in future work. Apart from the effect on glycemic control, several substances of “maccog” TCM tea used in this study, including mulberry leaves, radix ophiopogonis, and gynostemma, have been shown to reduce blood lipid by reducing endogenous cholesterol synthesis, inhibiting the production of lipid peroxides, and clearing free radicals in the body (32–34). Dyslipidemia is one of the common problems in patients with T2DM, and the improvement in lipid profile will lead to remarkable benefits [35]. The significantly lowered TC level after the intervention found in our study demonstrated a certain effect on blood lipid metabolism in patients with T2DM by “maccog” TCM tea. The improvement in blood lipid profile was also found in the research of Peng and Aramwit et al. [ 36, 37]. Aramwit found that 12 weeks of mulberry leaf powder intervention can significantly reduce TG and LDL levels in participants with mild dyslipidemia [37]. The changes in various parameters may be related to different participants. In our study, the decrease in TG in IG was slightly greater than that in CG, which was close to a statistical difference at week 12. A significant difference is likely with prolonged intervention period or increased dosage. Xu et al. showed that MFH intervention for 12 weeks can significantly increase HDL [10], but other studies found a decrease in HDL after MFH intervention for 12 weeks compared with placebo [18, 38]. In our study, although the changes in HDL in IG were not statistically different from those in CG at week 12, we noted a decrease trend in IG and an upward trend in CG. Thus, the specific effects and reasons for the differences in human HDL changes of “maccog” TCM tea need to be further investigated. Among the parameters of hepatorenal function, significantly lower TP, ALB, and CREA were found in IG than in CG. TP and ALB are closely related to protein metabolism, and their decrease generally indicates a malnourished status or reduced protein synthesis in the liver. The decrease in CREA is also a manifestation of body protein reduction. Whether the unexpected lower TP, ALB, and CREA after the intervention is related to the bitter taste of “maccog” TCM tea, which can affect the participants’ appetite, or due to some of the ingredients that affect protein metabolism is unknown and awaits further research. However, all hepatorenal indicators in our study were within the normal range, suggesting that taking these substances in tea drinking mode in short term did not cause abnormal human hepatorenal function. In addition, all participants did not have any significant adverse reactions during the study. Only a few participants had xerostomia or skin itching, and many reported beneficial changes in bowel movements, sleep, and vision. These results combined with good compliance confirmed the safety and feasibility of the intervention. The gut microbiota and its metabolic products interact with the host in many different ways, influencing gut homoeostasis and health outcomes. The species composition of the gut microbiota has been shown to respond to dietary changes and then alter metabolic status [39]. The results showed that after 12 weeks of “maccog” TCM tea consumption, the gut microbiota diversity index of patients with T2DM did not increase significantly, but the Ace and Chao1 indices in IG were slightly higher after the intervention, and the values were close to significant differences. We can speculate that the flora diversity index of patients with T2DM is very likely to increase significantly after the intervention with enlarged sample size. The human gut microbiota contains more than a thousand species of which about $95\%$ belongs to Firmicutes and Bacteroidetes, followed by Actinobacteria and Proteobacteria [40]. Both IG and CG have similar relative abundances at the phylum level before and after the intervention, including Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. The results showed that Proteobacteria, Firmicutes, and Bacteroidetes in IG showed no significant difference compared with those in CG and the baseline, but Actinobacteria was significantly higher. In a study of a 23-week dietary intervention in 123 patients with central obesity, the participants’ abundance of Actinobacteria was significantly higher and that of Proteobacteria was significantly lower at 9 weeks and 23 weeks of intervention, respectively. The intervention was accompanied with improvements in inflammatory and metabolic indices [41]. Although the participants and intervention project differed from our study, we noted similarities. Central obesity and T2DM are metabolic syndromes; the intervention of both studies included Chinese herbal medicines, and changes in the intestinal flora at the phylum level were basically consistent with our study, which suggested that Actinobacteria may be involved in the improvement of the metabolic syndrome. The abundance of Actinobacteria, Lachnospiraceae, Bifidobacteriaceae, Phascolarctobacterium, and Bifidobacterium after the intervention were significantly higher in IG than those at baseline. The abundance of Lachnospiraceae and Phascolarctobacterium after the intervention was significantly higher in IG than in CG. Lachnospiraceae can produce butyrate through the butyryl-CoA, acetate CoA-transferase, or butyrate kinase pathway [42]. Butyrate can improve insulin production and sensitivity by stimulating glucagon-like peptide 1 (GLP-1) secretion and reducing inflammation of fat cells. Rising butyrate inhibits the development of low levels of chronic inflammation [43]. Peng et al. found that Lachnospiraceae and Clostridiaceae increase significantly after 3 weeks by transferring gut microbes to non-obese diabetic mice, and this change was found to improve the insulin sensitivity of diabetic mice to some extent [44]. Bifidobacteriaceae are a member of Gram-positive bacteria with intestinal barrier protection. They have been found to improve gut barrier function or reduce gut endotoxin level by stimulating the discharge of secretory immunoglobulin A [45]. Le et al. found that oral administration of Bifidobacterium can reduce the expression levels of inflammatory adipocytokines and improve insulin resistance and glucose tolerance in obese mice [46]. Cani et al. found that Bifidobacterium can improve the state of metabolism disorders in diabetic mice by reducing endotoxemia and intestinal permeability induced by a high-fat diet [47]. Naderpoor et al. found that Phascolarctobacterium is positively related to insulin production and sensitivity through a survey of gut microbiome and insulin sensitivity in 38 obese patients [48]. Our study concluded that the abundance of Clostridiales and Lactobacillales was negatively correlated with FPG levels, and the abundance of Clostridiales and Lachnospiraceae was negatively correlated with GSP levels; these results indicated that the higher the amount of beneficial bacteria, the more obvious the improvement in the glucose metabolism index. Furthermore, Pugliese et al. reported that obesity is associated with a change in gut microbiota composition characterized by a reduction in the abundance of Bacteroidetes and a proportional increase in Firmicutes [49]. Obesity and T2DM are metabolic syndromes, which suggest that Bacteroides/Firmicutes may be involved in T2DM. In our study, the abundance of Bacteroides/Firmicutes was positively correlated with FPG and GSP levels. In summary, “maccog” TCM tea can significantly improve the abundance of gut microbiota in patients with T2DM. These changes may promote the beneficial transformation of the intestinal environment through different modes of action, thereby improving the glucose metabolism of patients with T2DM. This study had some limitations. First, the intervention used in this study was a combination of six substances, so we could not infer whether a single substance has a significant effect on improving glucolipid metabolism in patients with T2DM. Second, considering that the MFH materials used in this study are relatively common edible substances in daily life without special contraindications, syndrome differentiation and classification were not performed on the participants for the convenience of application. However, the improvements in patients with T2DM of different constitution types may vary. Third, due to practical constraint, this study was only carried out for 12 weeks and had no follow-up visits. Therefore, the long-term effect and safety of this formula on patients with T2DM need to be investigated in future studies for clarity. In general, fewer effective ingredients are extracted from tea than from decoction. Therefore, “maccog” TCM tea may not be as effective as decoction; with decoction, more positive therapeutic effects may be achieved with shorter intervention time under equal conditions. ## Conclusions The results of this study showed that “maccog” TCM tea for 12 weeks, which is safe and feasible, had some minor effects on improving glucolipid metabolism and significantly improved the abundance of gut microbiota in community patients with T2DM. The increase of beneficial bacteria abundance may be involved in the improvement in glucose metabolism indicators. More research is needed to confirm the positive effects and clarify its long-term effects and potential mechanisms. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee of Soochow University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions LW, HM, and YZ designed the study. BH, JZ, ML, HY, JW, RG and JH conducted the study. BH and TY analyzed the data and wrote the first draft of manuscript. LW, HM and YZ supervised the study and reviewed and edited the manuscript. All authors have read and approved the final version of the manuscript. ## Conflict of interest Authors JW, RG, and JH were employed by Soochow Setek Biotechnology Co, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: “Drying effect” of fructus aurantii components and the mechanism of action based on network pharmacology and in vitro pharmacodynamic validation authors: - Jing Zhu - Yi Luo - Hengli Tong - Lingyun Zhong - Qianfeng Gong - Yaqi Wang - Ming Yang - Qing Song journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10031011 doi: 10.3389/fphar.2023.1114010 license: CC BY 4.0 --- # “Drying effect” of fructus aurantii components and the mechanism of action based on network pharmacology and in vitro pharmacodynamic validation ## Abstract Background: Fructus aurantii (FA) is the dried, unripe fruit of the plant Citrus aurantium L. and its cultivated varieties. We investigated the drying effect of FA components and how this drying affect is achieved. Methods: We employed systems pharmacology to predict the components and targets of FA that produce its drying effect. These predictions were verified by computer simulation and animal experiments. In the latter, we measured the bodyweight, water consumption, urine output, fecal water content, rate of salivary secretion, and cross-sectional area of the long axis of the submandibular gland of mice. Immunohistochemistry was used to measure expression of aquaporin (AQP)5 in the submandibular gland, AQP2 in the kidney, and AQP3 in the colon. ELISA kits were used to measure the horizontal variation of cyclic adenosine monsophosphate (cAMP), cyclic guanosine monophosphate (cGMP) and interferon-γ. Results: Sixty-seven potentially active components of FA were screened out. FA could produce a drying effect after regulating 214 targets through 66 active components. A total of 870 gene ontology (GO) terms and 153 signaling pathways were identified. The hypoxia inducible factor-1 signaling pathway, phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway, calcium signaling pathway, and Ras signaling pathway may have important roles in the drying effect of FA. Four components of FA were identified: sinensetin, tangeretin, 5-demethylnobiletin and chrysin. These four components could increase the serum level of interferon-γ and ratio of cyclic adenosine monophosphate:cyclic guanosine monophosphate in mice, and affect their water consumption, urine output, fecal water content and rate of salivary secretion. Conclusion: Four components of FA (tangeretin, sinensetin, chrysin, 5-Demethylmobiletin) were closely related to the Janus kinase-signal transducer and activator of transcription-3 (JAK-STAT3), PI3K-AKT, and the other signaling pathways. They can regulate the protein expression of JAK2, STAT3, PI3K, lymphocyte cell-specific protein-tyrosine kinase, vascular endothelial growth factor A, and protein kinase B1, affect water metabolism in the body and, finally, result in a drying effect. ## 1 Introduction The quality of extracts of traditional Chinese medicine (TCM) formulations is important to determine if such formulations can have a clinical effect. Many Chinese botanical drugs have been reported to have pharmacological and biological (e.g., antioxidant, anti-inflammatory, antibacterial, antiviral, anti-tumor) activities. ( Nikzad-Langerodi et al., 2017; Uysal et al., 2017). The side effect of dryness affects the efficacy of TCM formulations. On the one hand, physicians can use the dryness of a TCM formulation to dispel dampness and treat dampness syndrome. On the other hand, the dryness of a TCM formulation damages body-fluid metabolism, resulting in a dry mouth and throat (Wang et al., 2014). The properties or toxicity of a TCM formulation can be altered by processing, such as frying, stir-frying, calcining, steaming, boiling, blanching, or water milling (Ye et al., 2020; Cao et al., 2022). Fructus aurantii (FA) is the dry immature fruit of Citrus aurantium L. and its cultivated varieties. It can promote Qi circulation to alleviate the middle energizer. The dryness of FA is usually regarded as its adverse effect in the clinical setting. With regard to the drying effect of raw FA, Shennong’s Herbal Classic says that “those who cough with Yin deficiency and inflammation will be in danger after taking it”. Modern experimental research shows that raw FA will destroy the metabolism of body fluids (Zhu et al., 2020). Sjögren’s syndrome (SS; also known as “autoimmune exocrine adenosis” or “autoimmune exocrine supraglandular dermatitis”) is characterized mainly by histology and functional changing in exocrine glands (e.g., lacrimal glands, submandibular gland, parotid glands, pancreas) (Chatzis et al., 2021). The clinical manifestations of SS are dry mouthand dry eyes, etc. “Systems pharmacology” can be used to screen out the effective substances of a TCM formulation and analyze its mechanism of action by constructing a multi-level “component–target–pathway” network (Hopkins, 2008; Li et al., 2014; Zhang et al., 2019). This strategy enables systematic determination of the effect and mechanism of the drug action for treating complex diseases at molecular, cellular, tissue, and biological levels (Yang et al., 2019). In this situation, Systems Pharmacology was used to analyze the active compounds, drug targets, and key signaling pathways of FA to ascertain how the drying effect worked (Figure 1). We conducted ultrasound examination at the submandibular glands of mice. Combining the traditional evaluation indicators (e.g., salivary flow rate, water consumption, urine volume), we sought to establish an evaluation method of the drying effect of FA. In this way, we wished to lay a foundation for the study of the drying effect of FA and reveal its mechanism of action. FIGURE 1: Systems pharmacology-based strategy to study the drying effect of components of Fructus Aurantii and the mechanism of action. ## 2.1 Ethical approval of the study protocol The study protocol was approved (JZLLSC20220813) by the Research Ethics Committee of Jiangxi University of Traditional Chinese Medicine (Nanchang, China). Potentially identifiable human images or data are not presented in this study. ## 2.2 Chemicals, agents, and materials A decolorization shaker (ZD-9560) was purchased from Jiangsu Shenglan Instrument Manufacturing (Jiangsu, China). An electrophoresis instrument (JY300HC), vertical electrophoresis tank (JY-SCZ2+), and western blotting system transfer electrophoresis tank (JY-ZY5) were obtained from Beijing Junyi Huaxin Technology (Beijing, China). A desktop high-speed freezing centrifuge (TGL-16M) and chemiluminescence imaging device (chemiscope Mini 3300) were sourced from Shanghai Qinxiang Scientific Instruments (Shanghai, China). A bicinchoninic-acid protein quantification kit was obtained from Beijing Solarbio Technology (Beijing, China). Polyvinylidene-fluoride membranes and an electrochemiluminescence kit were purchased from Millipore (Burlington, MA, United States). Antibodies against glyceraldehyde 3-phosphate dehydrogenase, signal transducer and activator of transcription (STAT)3, and protein kinase B (AKT) were obtained from Abcam (Cambridge, United Kingdom). Phosphoinositide 3-kinase (PI3 kinase) p85 rabbit monoclonal antibody was purchased from Cell Signaling Technology (Danvers, MA, United States). Preserved protein ladders were from Fermentas (Burlington, ON, United States). Lymphocyte cell-specific protein-tyrosine kinase (Lck) polyclonal antibody was sourced from Proteintech (Chicago, IL, United States). Rabbit anti-vascular endothelial growth factor (VEGF) antibody was obtained from Beijing Boarsen Biotechnology (Beijing, China). Antibody against Janus kinase (JAK)2 was purchased from Hangzhou Hua’an Biotechnology (Hangzhou, China). Antibodies against horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin-G (H + L) and goat anti-mouse immunoglobulin-G (H + L) were sourced from Beijing TDY Biotechnology (Beijing, China). As reference standards, four compounds with purity ≥$98\%$ were used. They were sinensetin (batch number: wkq21060106), tangeretin (wkq21042202), 5-demethylnobiletin (wkq21052610), and chrysin (wkq21042612), which were purchased from Sichuan Vicky Biotechnology (Sichuan, China). ## 2.3 Collection of FA components and prediction of candidate compounds Using “Fructus aurantii” as the keyword, we searched the China National Knowledge Infrastructure, VIP, and Web of Science databases to discover the ingredients contained in raw pieces of FA. Then, by comparison with the ingredients included in Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Traditional Chinese Medicine Integrated Database (TCMID), the information of each ingredient was summarized after removing duplicate items (Ji et al., 2006; Ru et al., 2014). Next, we used the Lipinski’s Rule of Five (Lipinski et al., 2012). That is, if a FA component was subject to certain provisions of certain parameters, it could be identified as a potentially effective component. These provisions werehydrogen-bond donor (number of hydrogen atoms attached to oxygen and nitrogen atoms) ≤5; Relative molecular mass ≤500; Partition coefficient of fat:water miLogP ≤5; Hydrogen-bond acceptor (number of oxygen and nitrogen atoms) ≤10; Meeting at least two of four filters (Ghose (Amgen), Veber (GSK), Egan (Pharmacia) and Muegge (Bayer); Gastrointestinal Absorption Index was “high” (Sravika et al., 2021; Zheng et al., 2022). ## 2.4 Identification of the related targets of FA components Collected ingredient was searched for in PubChem (www.ncbi.nlm.nih.gov/pubmed) and ChemicalBook (www.chemicalbook.com/). These platforms can be used to distinguish the molecular structure of ingredients (Dashti et al., 2019). The structure data file (SDF) of ingredients was downloaded from PubChem. All the targets of the compounds in FA were collected from SwissTargetPredition (www.swisstargetprediction.ch/). After removing redundant information, 66 components in FA and 652 known targets associated with them were obtained. Then, through the UniProt database (www.uniprot.org/), we undertook conversion to the gene name (The uniProt consortium, 2021) for future use. ## 2.5 Acquisition of targets for SS GeneCards (www.genecards.org/), DisGeNET (www.disgenet.org/) and Online Mendelian Inheritance in Man (www.omim.org/) were searched using the keywords “Sjögren syndrome”, “Sjogren’s syndrome”, “Sjogren’s disease”, and “sicca syndrome”. We deleted duplicates to obtain relevant targets (Stelzer et al., 2016; Yu et al., 2020). ## 2.6 Construction of protein-protein interaction (PPI) and compound-target networks First, we intersected the obtained drug targets with the disease related target, then we obtained a Venn diagram of the intersecting targets. After that, we inputted the intersected targets into Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.5 (https://string-db.org/). STRING can be used to predict the interaction relationship between targets. We selected “multiple proteins” in the left functional area and set the species as Homo sapiens. We analyzed the interaction relationship between common targets, then imported the results into Cytoscape 3.7.1 (www.cytoscape.org/) to build the PPI network “compound-target” network. We used the “network analyzer” function to analyze the network parameters and visualize the results (Zhu and Hou, 2020; Li et al., 2021). ## 2.7 Enrichment analyses To explore the biological process (BP), molecular function (MF), cellular component (CC), and related pathways associated with the drying effect of FA, the intersection targets obtained previously were enriched and analyzed using Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8 (https://david.ncifcrf.gov/home.jsp) (Cui et al., 2020; Zhang et al., 2020; Gegen et al., 2022). We selected $p \leq 0.05$ as the screening criterion. Analysis of enrichment of function and signaling pathways was conducted using the Gene Ontology (http://geneontology.org/) database and Kyoto Encyclopedia of Genes and Genomes (www.genome.jp/) database, respectively. The top-20 enriched genes were visualized using ImageGP (www.ehbio.com/ImageGP/). We studied the relationship between components, common targets, and pathways, and predicted how FA elicits its drying effect. ## 2.8 Expression of targets in organs TCM theory emphasizes the holistic nature of treatment. Usually, TCM preparations treat diseases by regulating multiple organs to achieve a “peaceful” state (Li, 2016). We wished to characterize the relationship between the efficacy of FA and organs. Consequently, we imported the targets in the top-15 nodes of the PPI network into the biogps database (http://biogps.org/). The species we chose was “human”. We obtained protein-expression datasets of related targets in different organs, and generated organ-expression heatmaps of targets. ## 2.9 Computational validation of compound-target interactions We used computer simulations to study active compounds and their targets. We selected the top-10 potentially active components in FA for molecular docking with the top-15 node targets in the PPI network. CB-dock (http://cao.labshare.cn/cb-dock/) is a docking tool for docking protein-ligand complexes. CB-dock can identify binding sites, calculate the location of docking centers, and automatically customize the size of docking-box according to ligands. After that, we ran AutoDock Vina (https://vina.scripps.edu/) (Liu et al., 2022a; Yang et al., 2022). We obtained and downloaded the SDF of FA potential components from PubChem. The entry codes corresponding to the top-15 targets in the PPI network can be found in the Research Collaboration for Structural Bioinformatics in Protein Database (PDB; www.rcsb.org/). Then, we downloaded their PDB format. CB-dock uses curvature based cavity detection (curpocket) to predict the binding sites of target proteins. Then, the binding posture of the query ligand can be predicted using AutoDock Vina, which provides a guarantee for the accuracy of molecular docking. The more negative the docking score, the more complex is the interaction between the target and compound molecule. ## 2.10.1 Screening of the best ingredients and dose Dosing for mice was calculated based on the doses of drugs used in humans and converted accordingly (Reagan-Shaw et al., 2008; Chinese Pharmacopoeia Commission, 2020). In pharmacological experiments, drugs are often prepared in a solution of a certain concentration and then administered in mL/kg body-weight. As long as the difference between the animal’s body-weight and standard body-weight is less than ±$20\%$, the drug can be administered at the same dose. The range in content of four components of FA from 10 locations was determined using raw pieces of FA from these 10 places. The content range (in μg/g) of the four components in 10 batches of raw FA was 9.27–283.84 for sinensetin, 36.01–610.83 for chrysin, 164.31–2994.21 for tangeretin, and 6.60–301.78 for 5-Demethylmobiletin (see the Supplementary Table for details). Then, according to the maximum content of each component, we calculated the amount of the four components in the maximum dose of FA prescribed in the Chinese Pharmacopoeia (10 g). We performed equivalent dose switching in mice. That is, we multiplied the dose for a human (60 kg) per kilogram of body-weight by the conversion coefficient of a human and a mouse under standard weight (12.33) to obtain the dose of a mouse per kilogram of body-weight (Db) of standard body-weight (0.02 kg). The body-weight of an experimental mouse changes dynamically. Hence, the dose for a mouse with a non-standard body-weight (Db1) = Db·Sb (where Sb is the correction coefficient of a mouse with a non-standard body-weight) (Table 1). TABLE 1 \| B = W/W标 \| 0.6 \| 0.7 \| 0.8 \| 0.9 \| 1.0 \| 1.1 \| 1.2 \| 1.3 \| 1.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Sb = 1/B1/3 \| 1.186 \| 1.126 \| 1.077 \| 1.036 \| 1.0 \| 0.969 \| 0.941 \| 0.916 \| 0.894 \| ## 2.10.2 Preparation of drugs The FA used for the administration of the extract was purchased from Jiangxi Guhan Refined Chinese Herbal Pieces (Jiangxi, China). It was identified by Professor Zhang Jinlian of the Chinese Herbal Processing Discipline Group of Jiangxi University of Traditional Chinese Medicine (Jiangxi, China) as the dried immature fruit of the Rutaceae plant Citrus aurantium. First, we weighed an appropriate amount of raw FA. Then, we added 10-times the amount of water and boiled for 30 min. This action was repeated twice. Next, we combined the filtrate, decompressed and concentrated to weigh 1 g/ml of the drug solution. Lastly, we gavaged the drug according to the body-weight of mice. In addition, considering the low efficiency and insufficient purity of the monomer components purified by our research team, the monomer compounds used in the monomer-component administration group were purchased from professional companies specializing in extracting single compounds from plant components. The four components were flavonoids and had poor solubility in water. Hence, $0.1\%$ dimethyl sulfoxide (DMSO) was added when preparing the solution for administration (Gilfanova et al., 2021; Hoyberghs et al., 2021). To ensure consistency, the same dose of DMSO solution was also added in physiologic ($0.9\%$) saline.2.10.3Validation of pharmacodynamics sixty healthy, specific pathogen-free (SPF), female BALB/C mice (license number: SCXK(Yu) 2020-0005) were purchased from Henan Sikebasi Biotechnology (Henan, China). Ten female SPF non-obese mice suffering from diabetes mellitus (NOD/LtJ) were purchased from SBF (Beijing) Biotechnology (license number: SCXK(Beijing) 2019-0010) in Beijing, China. After 1 week of adaptive feeding, 60 BALB/C mice were divided into six groups of ten: control; extract administration; sinensetin; tangeretin; 5-demethylnobiletin; chrysin. In addition, 10 female NOD/Ltj mice were set as the model (NOD) group. Mice in each administration group were administered the agent by gavage at 10-times the equivalent dose for 5 weeks. The control group and NOD group were given an identical volume of physiologic ($0.9\%$) saline. The body-weight and water consumption of mice were recorded every day. At days 0, 7, 14, 21, 28, and 35 of administration, mice were placed in the corresponding metabolic cages in groups. The total urine output of mice in each group was measured at 12 h. Feces were collected within 12 h, and foreign matter and other impurities removed. The moisture of the dejecta in each group was measured. The rate of salivary secretion of mice in each group was measured 0, 15, 18, 21, 24, and 27 days after administration. Thirty-two days after 32 administration, the submandibular glands of mice were examined using a high-resolution small-animal ultrasound imaging system (VEVO 2100; VisualSonics, Toronto, Canada). After 35 days of administration, mice in each group were fasted for 12 h without water. Blood was taken from the retro-orbital sinus. Serum was collected by centrifugation (Relative centrifugal force (RCF) = 684.775 g, 10 min, 4°C). The level of interferon (IFN)-γ, cyclic adenosine monophosphate (cAMP), and cyclic guanosine monophosphate (cGMP) was determined by enzyme-linked immunosorbent assays (ELISAs). Expression of VEGF, JAK2, phosphoinositide 3-kinase-protein kinase B (PI3K), STAT3, Lck, and AKT in mouse colons was determined by immunoblotting. ## 2.11 Statistical analyses Measurement data are expressed as the mean ± standard deviation. Prism 8.0.2 (GraphPad, San Diego, CA, United States) was used for one-way ANOVA to determine the significance of results. $p \leq 0.05$ was considered significant. All experiments were repeated at least three times. ## 3.1 Collection and screening of the raw ingredients of FA According to the screening criteria described in Section 2.3 (Lipinski’s Rule of Five and four filters), 43 compounds met the screening requirements, and could be used as candidates for screening the active components of FA. Even though some components do not meet the screening criteria and their pharmacokinetic values are relatively low, several studies have reported their biological activities (Dagur et al., 2022). They also need to be included in a library of candidate components for screening the active compounds of FA to investigate this issue more fully. Twenty-four components (e.g., naringin, neohesperidin, naringin, rutin, hesperidin, limonene) did not meet the screening criteria stated above, but they were retained as active components. Finally, 67 compounds were selected as candidate active ingredients of FA (Table 2). TABLE 2 \| NO \| Name of molecular \| Encoded \| MW \| CAS \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 2-acetyloxyethyl (trimethyl)azanium \| M1 \| C7H16NO2 + \| 51-84-3 \| \| 2 \| 2-hydroxybenzoic acid \| M2 \| C7H6O3 \| 69-72-7 \| \| 3 \| 5,7-dihydroxy-2-(4-hydroxyphenyl)-2,3-dihydrochromen-4-one \| M5 \| C15H12O5 \| 67604-48-2 \| \| 4 \| Palmitic Acid \| M7 \| C16H32O2 \| 57-10-3 \| \| 5 \| 4-(2-amino-1-hydroxyethyl)phenol \| M11 \| C8H11NO2 \| 104-14-3 \| \| 6 \| Terbutaline \| M13 \| C12H19NO3 \| 23031-25-6 \| \| 7 \| 4-[1-hydroxy-2-(methylamino)ethyl]phenol \| M18 \| C9H13NO2 \| 94-07-5 \| \| 8 \| 7-methoxy-8-(3-methylbut-2-enyl)chromen-2-one \| M25 \| C15H16O3 \| 484-12-8 \| \| 9 \| 2-methyl-5-propan-2-ylphenol \| M26 \| C10H14O \| 499-75-2 \| \| 10 \| 3-hydroxy-2-phenylchromen-4-one \| M28 \| C15H10O3 \| 577-85-5 \| \| 11 \| Tangeretin \| M32 \| C20H20O7 \| 481-53-8 \| \| 12 \| Hesperetin \| M35 \| C16H14O6 \| 520-33-2 \| \| 13 \| Nobiletin \| M36 \| C21H22O8 \| 478-01-3 \| \| 14 \| Tetramethylscutellarein \| M37 \| C19H18O6 \| 1168-42-9 \| \| 15 \| Sinensetin \| M39 \| C20H20O7 \| 2306-27-6 \| \| 16 \| 5-Demethylnobiletin \| M41 \| C26H30O8 \| 2174-59-6 \| \| 17 \| Naringenin \| M42 \| C15H12O5 \| 480-41-1 \| \| 18 \| 4-[(1R)-2-amino-1-hydroxyethyl]phenol \| M43 \| C8H11NO2 \| 876-04-0 \| \| 19 \| Fumaric Acid \| M47 \| C4H4O4 \| 110-17-8 \| \| 20 \| Ferulic Acid \| M49 \| C10H10O4 \| 537-98-4 \| \| 21 \| Coumaric Acid \| M50 \| C9H8O3 \| 501-98-4 \| \| 22 \| (2R)-5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)-2,3-dihydrochromen-4-one \| M55 \| C16H14O6 \| 69097-99-0 \| \| 23 \| 4-[(1R)-1-hydroxy-2-(methylamino)ethyl]phenol \| M57 \| C9H13NO2 \| 614-35-7 \| \| 24 \| Auraptene \| M61 \| C19H22O3 \| 495-02-3 \| \| 25 \| N-[2-(4-methoxyphenyl)ethyl]benzamide \| M63 \| C16H17NO2 \| 3278-19-1 \| \| 26 \| 4-azaniumylbenzenesulfonate \| M64 \| C6H7NO3S \| 121-57-3 \| \| 27 \| 7-hydroxychromen-2-one \| M68 \| C9H6O3 \| 93-35-6 \| \| 28 \| Kaempferide \| M69 \| C16H12O6 \| 491-54-3 \| \| 29 \| 5-methyl-2-propan-2-ylphenol \| M83 \| C10H14O \| 89-83-8 \| \| 30 \| pentadecanoic acid \| M138 \| C15H30O2 \| 1002-84-2 \| \| 31 \| 3,3′,4′,5,6,7,8-Heptamethoxyflavone \| M147 \| C22H24O9 \| 1178-24-1 \| \| 32 \| Isosakuranetin \| M148 \| C16H14O5 \| 480-43-3 \| \| 33 \| 5,6,7,3′,4′,5′-Hexamethoxyflavone \| M150 \| C21H22O8 \| 29043-07-0 \| \| 34 \| Eriodictyol \| M151 \| C15H12O6 \| 552-58-9 \| \| 35 \| Acacetin \| M155 \| C16H12O5 \| 480-44-4 \| \| 36 \| Scopoletin \| M156 \| C10H8O4 \| 92-61-5 \| \| 37 \| Chrysin \| M157 \| C15H10O4 \| 480-40-0 \| \| 38 \| Diosmetin \| M158 \| C16H12O6 \| 520-34-3 \| \| 39 \| (113C)dodecanoic acid \| M159 \| C12H24O2 \| 93639-08-8 \| \| 40 \| Quercetin \| M65 \| C15H10O7 \| 117-39-5 \| \| 41 \| Apigenin \| M66 \| C15H10O5 \| 520-36-5 \| \| 42 \| Kaempferol \| M67 \| C15H10O6 \| 520-18-3 \| \| 43 \| Robinetin \| M70 \| C15H10O7 \| 490-31-3 \| \| 44 \| Naringin \| M162 \| C27H32O14 \| 10236-47-2 \| \| 45 \| Neohesperidin \| M163 \| C28H34O15 \| 13241-33-3 \| \| 46 \| Narirutin \| M165 \| C27H32O14 \| 14259-46-2 \| \| 47 \| (-)-Limonene \| M164 \| C10H16 \| 5989-54-8 \| \| 48 \| Limonene \| M161 \| C10H16 \| 5989-27-5 \| \| 49 \| Hesperidin \| M160 \| C28H34O15 \| 520-26-3 \| \| 50 \| alpha-Farnesene \| M166 \| C15H24 \| 502-61-4 \| \| 51 \| beta-Elemene \| M167 \| C15H24 \| 515-13-9 \| \| 52 \| gamma-Terpinene \| M168 \| C10H16 \| 99-85-4 \| \| 53 \| O-Cymene \| M169 \| C10H14 \| 527-84-4 \| \| 54 \| M-Cymene \| M170 \| C10H14 \| 535-77-3 \| \| 55 \| (-)-alpha-Cubebene \| M171 \| C15H24 \| 17699-14-8 \| \| 56 \| Neoeriocitrin \| M172 \| C27H32O15 \| 13241-32-2 \| \| 57 \| (-)-alpha-Pinene \| M173 \| C10H16 \| 7785-26-4 \| \| 58 \| (+)-alpha-Pinene \| M174 \| C10H16 \| 7785-70-8 \| \| 59 \| alpha-Terpinene \| M175 \| C10H16 \| 99-86-5 \| \| 60 \| Naringin 4′-glucoside \| M176 \| C33H42O19 \| 17257-21-5 \| \| 61 \| Hesperetin 5-O-glucoside \| M177 \| C22H24O11 \| 69651-80-5 \| \| 62 \| Merancin hydrate \| M178 \| C15H18O5 \| 5875-49-0 \| \| 63 \| Meranzin \| M180 \| C15H16O4 \| 23971-42-8 \| \| 64 \| Quercimeritrin \| M181 \| C21H20O12 \| 491-50-9 \| \| 65 \| Naringenin chalcone \| M182 \| C15H12O5 \| 73692-50-9 \| \| 66 \| Isomeranzin \| M183 \| C15H16O4 \| 1088-17-1 \| \| 67 \| Xanthotoxol \| M141 \| C11H6O4 \| 2009-24-7 \| ## 3.2 Identification of the related targets of FA constituents All the targets of the compounds in FA were collected from SwissTargetPredition and TCMSP databases. After removing redundant information, 67 components in FA and 652 known targets associated with these components were obtained (Supplementary Table). ## 3.3 Acquisition of the known therapeutic targets for SS After collecting and removing redundant items from GeneCards, DisGeNET, and OMIM databases, 3126 known targets of SS treatment were collected (Supplementary Table). ## 3.4 Analyses of the compound–target network There were 214 overlaps of 3126 targets for disease and 652 targets for the drug (Figure 2A). That is, there could be 214 key targets for FA during SS treatment. The 214 overlapping targets are detailed in Supplementary Table. FIGURE 2: (A) There are 214 overlapping targets between disease and drug. (B) C-T network. The 86 warm yellow nodes indicate the potential active ingredients of dryness effect. The 15 light blue nodes indicate coincident targets between disease and drug. The edges denote that nodes can interact with each other. (C) Details of the PPI Network. According to the degree value of each target, the larger the degree value, the redder the color, and the larger the circle. (D) Bar plot of the PPI network. The x-axis represents the number of neighboring proteins of the target protein. The y-axis represents the target protein. To elucidate how FA elicits a dryness effect symptoms, we constructed a Compound-Target network. Figure 2B shows the 15 potential active components which may act on 86 targets to exert a dryness effect. The 15 light blue nodes represent active components in FA; The 86 warm yellow nodes represent potential targets which acted by the components in FA; Edges indicate that nodes could influence each other. ## 3.5 Analyses of the PPI network We constructed a PPI network consisting of 65 nodes and 443 edges. This PPI network was based on the premise that proteins interact with each other more than would be expected for a random proteome of similar size extracted from the genome. This enrichment suggests that the proteins are at least partially linked biologically as a group (Figure 2C). Based on the PPI network’s “Betweenness unDir” and other analysis parameters, we screened 32 potential core targets related to dryness (Supplementary Table). We identified the top-15 proteins in the PPI network. STAT3 and heat shock protein (HSP)90AA1 may be associated with 41 other proteins (Figure 2D). Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) was related to 39 other proteins. PIK3CA was associated with 36 other proteins. These results suggested that these four proteins would be the focus of our study on PPIs. Fucosyltransferase (FUT)7 and serine/threonine-protein kinase TNNI3K were not associated with other proteins in this PPI network, implying that they were less important. ## 3.6 Enrichment analyses First, we carried out enrichment analyses using DAVID using GO terms to elucidate relevant BP. The y-axis represents GO terms. The x-axis indicates the number of targets enriched in this term. The redder the color, the smaller the p-value, the higher is the credibility and importance. In contrast, the greener the color, the greater is the value of padjusted. The size of the circle represents the number of genes included in the corresponding enrichment entry. The larger the circle area, the more genes that are included. The top-10 terms of BP, CC, and MF were screened (Figures 3A–C). FIGURE 3: (A–C) Analyses of 214 targets associated with Sjögren’s syndrome using the GO database. The x-axis represents significant enrichment of these term counts. The y-axis represents the category of “biological process”, “cellular component” and “molecular function” in the GO of target genes (p < 0.05, top10). (D) Pathway-enrichment analyses using the KEGG database. The x-axis represents the count of targets in each pathway; the y-axis represents the primary pathway (p < 0.01, top20). Details of pathway-enrichment analyses using DAVID are provided in Supplementary Table. The main BP were “response to a drug” (GO:0042493), “response to a xenobiotic stimulus” (GO: 0009410), “positive regulation of gene expression” (GO:0010628), “aging” (GO:0007568), and “peptidyl-tyrosine phosphorylation” (GO:0018108). The main CC were “membrane raft” (GO:0045121), “plasma membrane” (GO:0005886), “receptor complex” (GO:0043235), “integral component of plasma membrane” (GO:0005887), and “macromolecular complex” (GO:0032991). The main MF were “RNA polymerase II transcription factor activity (GO:0004879)”, “ligand-activated sequence-specific DNA binding” (GO:0004879), “enzyme binding” (GO:0019899), “protein tyrosine kinase activity” (GO:0004713), “transmembrane receptor protein tyrosine kinase activity” (GO:0004714), and “identical protein binding” (GO:0042802). The y-axis of Figure 3D represents a signaling pathway. The x-axis indicates the enrichment multiple of this signaling pathway. A total of 214 overlapping targets were mapped to 154 signaling pathways. For a more intuitive presentation, a bubble diagram can be drawn by intercepting the position of top-20 signaling pathways from small to large according to the p-value. The details of signaling-pathway enrichment based on the KEGG database are shown in Supplementary Table. Analyses of signaling-pathway enrichment using DAVID indicated that the raw ingredients of FA leading to a drying effect were based on “immune response”, “inflammation”, and “tumor or cancer”. Two-hundred and fourteen overlapping targets were closely related to “pathways in cancer (hsa05200), “HIF-1 signaling pathway (hsa04066)”, “PI3K-AKT signaling pathway” (hsa04151), “Th17 cell differentiation” (hsa04659), “calcium signaling pathway” (hsa04020), and “Ras signaling pathway” (hsa04014) (Figure 3D). These signaling pathways may be the key ones leading to the drying effect of FA. This type of analysis provides a new method to elucidate how the various components of FA elicit a drying effect. ## 3.7 Expression of targets in different organs Expression of the top-15 targets in the PPI-network according to the node degree in different organs is shown in Figure 4B (see the Supplementary Table for specific values). The redder the color, the higher is the expression of that target. Conversely, the bluer the color, the lower is the expression of that target. Most of the targets had high expression in the liver, kidneys, small intestine, lungs, and thyroid gland. According to TCM theory, pathogenesis is a complex process involving a group of interrelated factors. Therefore, in the treatment of local lesions, the mutual influence of different organs on physiology and disease should also be based on the holistic principles (Xu et al., 2021). FIGURE 4: (A) Heatmap of the top 15 targets docking with the top 10 components. The x-axis indicates the target name. The y-axis indicates the component name; from top to bottom, acacetin, 3,3′,4′,5,6,7,8-Heptamethoxyflavone, xanthotoxol, tetramethylscutellarein, apigenin, 5,6,7,3′,4′,5′-Hexamethoxyflavone, tangeretin, 5-Demethylnobiletin, chrysin, sinensetin. (B) Heatmap of organ expression of 20 targets. The x-axis indicates the organ name. The y-axis indicates the target name; from left to right, heart, liver, lung, kidney, small intestine, colon, whole brain, whole blood, thyroid, salivarygland,smoothmuscle, and hypothalamus. (C–F) Binding studies of selected compound–target interactions. (C) Tangeretin with LCK. (D) Chrysin with LCK. (E) Sinensetin with LCK. (F) 5-demethylmobiletin with LCK. Molecules are represented by a ball-and-stick model. Hydrogen bonds are represented by a dotted line, and numbers indicate hydrogen-bond distances in angstroms. Study found that TCM can alleviate dry mouth in patients with head-and-neck cancer after radiotherapy. This effect is achieved by promoting the flow and secretion of salivary amylase, and protecting the salivary glands of patients (Wang et al., 1998). Some scholars analyzed the pathological characteristics of “dryness” in patients living in Hunan Province, China. They concluded that dryness and dampness were due mainly to the “child disease mother” effect in the disease process (Fan et al., 2020). According to TCM theory, drynessinjuresthe lung. The lung and spleen belong to “gold” and “Earth” in the “Five Elements Theory” of TCM. Dampness tends to trap the spleen. Wet soil is the “mother of dryness”. In addition, excessive lung dryness will hurt the spleen “soil”. Hence, the organs of the human body are interrelated and also interact with each other. We showed that therapeutic targets could exist in two or more organs simultaneously, and that multiple targets could be distributed in the same organ. This finding suggests that human organs are related to each other during the occurrence and development of diseases, and FA may have a therapeutic role in SS-like symptoms through multiple targets and multiple pathways. ## 3.8 Computational assessment of selected compound–target interactions In general, the lower the binding energy, the more stable the binding between the ligand and receptor. Binding energy <0 indicates that a ligand molecule can bind spontaneously to a receptor molecule (Namasivayam and Günther, 2007). Therefore, we explored the interaction and binding modes between the top-15 targets in the PPI network and their potential active components by AutoDock Vina. Through molecular docking, we can determine the possibility of potential components acting on the target and the specific binding sites. The results of molecular docking are shown in Figure 4A. Previous analyses of compound-target networks have revealed close associations of the components sinensetin, tangeretin, 5-demethylnobiletin, and chrysin with FA targets. Therefore, we first undertook molecular docking of LCK with them. The binding energy between sinensetin and LCK was −8.4 kcal/mol, which indicated the possibility of strong binding between them. Sinensetin and LCK formed a hydrogen bond at arginine (Arg)-250 (Figure 4E). The molecular-docking results of tangeretin with LCK showed that the binding energy between them was −7.7 kcal/mol, which suggested good binding activity. Figure 4C shows the formation of hydrogen bonds at leucine (L)-247 between tangeretin and LCK. The binding energy between 5-demethylnobiletin and LCK was −8.2 kcal/mol, and the binding energy between chrysin and LCK was −10.2 kcal/mol, respectively, which indicated the possibility of strong binding between them. LCK could form hydrogen bonds with 5-demethylnobiletin at glutamic acid (Glu)-237, lysine (Lys)-293, and glutamine (Gln)-296 (Figure 4F). Moreover, LCK could form hydrogen bonds with chrysin at tryptophan (Trp)-234 (Figure 4D). Supplementary Table shows the specific values. Based on these data, we considered that: 1) The interactions between these active ingredients and targets underlie their biological activity; 2) The drying effect of FA is reliant on multiple components and multiple targets. ## 3.9.1 Determination of dose In the early stage of this experiment, different dose groups (1-, 10-, 20-times of the equivalent dose) were set. After 4 weeks of administration, we found that the administration effect of the four components could all reach the same level as that of FA decoction with the same dose, and the effects of some components were more significant. Comprehensive analysis of various indicators (water consumption, urine volume, rates of salivary secretion, fecal water content, aquaporin (AQP)5 expression in the kidneys, area of the long axis of the submandibular gland) revealed that a 10-times equivalent dose of each component could better reflect the administration effect than that of other dose groups. Therefore, this dose was selected for the subsequent experiment. The four initially selected validation components were not screened. ## 3.9.2 In Vivo experiment Mice in each administration group drank more water (to varying degrees) than mice in the control group. The fecal water content, total volume of urine, and salivary flow rate of mice in each administration group were reduced (to varying degrees) compared with those in the control group, and reduced further with prolongation of the administration period (see the Supplementary Table for details). The area of the long axis of the submandibular gland in mice in each administration group was significantly greater than that of mice in the control group (Figure 5I). FIGURE 5: (A) Expression of PI3K protein. (B) Expression of STAT3 protein (C) Expression of AKT protein. (D) Expression of JAK2 protein. (E) Expression of LCK protein. (F) Expression of VEGF protein. (G) Level of cAmp/cGmp expression in mice serum. (H) Expression of inflammatory factors IFN-γin mice serum. (I) Longitudinal sectional area of submandibular gland long axis in each group. (J) Western blotting.p < 0.05, *p < 0.01 vs. control group (KB). Compared with the control group, the serum level of interferon-γ and the ratio of the serum level of cAMP:cGMP in each administration group was increased (to varying degrees), among which there were significant differences in the administration groups of sinensetin, tangeretin, and 5-demethylnobiletin (Figure 5H). The serum interferon-γ level in the extract-administration group (containing orange flavones) was significantly different from that in the control group. Also, the ratio of the serum level of cAMP:cGMP in the tangeretin group was significantly different from that in the control group. These data suggested that the ingredients stated above could cause varying degrees of body-fluid damage to healthy mice (especially tangeretin) (Figure 5G). Western blotting showed that, compared with the control group, the extract-administration group and NOD group could increase expression of each protein to varying degrees, and each administration group showed the same upward trend. Tangeretin could upregulate protein expression of PI3K, AKT, JAK2, and VEGF significantly. Chrysin could upregulate protein expression of LCK, VEGF, STAT3, and AKT significantly. Sinensetin could upregulate protein expression of PI3K, AKT, LCK, VEGF, and STAT3 significantly. 5-Demethylnobiletin could upregulate protein expression of AKT, LCK, VEGF, and STAT3 significantly (Figures 5A–F, J). Immunohistochemical analysis showed that, compared with the control group, each administration group could reduce AQP5 expression in the submandibular gland significantly. Tangeretin, sinensetin, and 5-demethylnobiletin could significantly reduce AQP2 expression in the kidneys yet enhance AQP3 expression in the colon. Compared with the model (NOD) group and extract-administration group, each administration group had the same expression trend. ( Figures 6A–C). The immunohistochemical sections of each administration group are shown in Figure 6D. FIGURE 6: (A) Expression of AQP3 protein in colon. (B) Expression of AQP5 protein in submandibular gland. (C) Expression of AQP2 protein in kidney. (D) Immunohistochemical example diagram of each group. The y-axis indicates the protein name. The x-axis indicates the group name; from left to right, control; NOD model; tangeretin; chrysin; sinensetin; 5-demethylnobiletin; extract administration. Submandibular-gland tissues were stained with H&E and observed under light microscopy (Figure 7). In the control group, the acinus of the submandibular-gland was regular in size and closely arranged, without duct expansion or lymphocyte infiltration. In the model group, there was no obvious lymphocyte infiltration, vascular dilation,catheter-related edema in the submandibular gland, and the size of acini was different. Compared with other groups, the tangeretin group showed more lymphocyte infiltration, accompanied by acinar atrophy and deformation. FIGURE 7: Pathological section of submandibular gland (100×). Letters in the figure indicates the group name; From left to right, control; NOD model; Tangeretin; Chrysin; Sinensetin; 5-demethylnobiletin; Extract administration. ## 4 Discussion Raw FA has a strong drying effect. If taken for a long time or inappropriately, it can cause yin-blood deficiency or yin deficiency, leading to an unmoistened nose or unmoistened throat. Initially, it manifests as xerostomia of the mouth and eyes, which accumulates over time and causes body-fluid exhaustion and blood dryness. According to TCM theory, dryness is an inherent feature of TCM formula that can lead to thirst, dry mouth, dry nose, dry skin, dry stools, and low urine output. Therefore, in the present study, body-fluid indices of mice, such as the water intake, urine output, rate of salivary secretion, and water content of feces were selected to investigate the influence of the drying effect of FA on body fluids. Compared with the control group, the amount of drinking water of mice in each treatment group increased, and the indicators of urination, saliva secretion rate and fecal water content decreased; In addition, each test index in mice showed the same trend as that in the extract-administration group to different degrees. This result indicated that each component could have an impact on the body’s water metabolism, which preliminarily confirmed that the dryness component predicted by our model was reliable. AQPs are proteins that can control the transmembrane transport of water with high selectivity. AQPs play an important part in the distribution, transportation, metabolism, and digestion of body fluids (Liao et al., 2021). It has been reported that different subtypes of AQP have different functions, and that the tissues they reside in are also different. AQP2 is present in the main cells of renal collecting ducts, is sensitive to antidiuretic hormone (ADH), which can regulate urine excretion (Jung and Kwon, 2016; Yu et al., 2022). Studies have shown that an increase in urine volume may be related to decreased expression of AQP2, and that protein expression of AQP2 can reflect water metabolism. Therefore, determination of AQP2 content can be used as an indicator to measure the drying effect of a TCM formulation. AQP3 is the most highly expressed AQP subtype in the colonic mucosa and has a leading role in mediating free water transport (Ikarashi, 2013). AQP5 is a commonly used marker to evaluate the drying effect of FA (Ren et al., 2020). Therefore, the contents of AQP2 in the kidneys, AQP3 in the colon, and AQP5 in the submandibular gland were selected as indicators of the drying effect of FA in our study. The metabolism of cAMP and cGMP in normal cells is in dynamic balance, and the two maintain a certain level. TCM theory holds that dryness damages yin, and patients with yin deficiency syndrome often show an increase in the cAMP level and a decrease in the cGMP level (i.e., an increase in the cAMP:cGMP ratio) (Qu et al., 2018). Studies have shown that the essence of yin deficiency and yang deficiency is closely related to the cyclic-nucleotide system. Scholars have postulated that a change in the plasma level of cyclic nucleotides in patients with yin deficiency is characterized by an increase in the cAMP level and cAMP:cGMP ratio: our results confirm this view. The early symptoms and pathologic process of SS are very similar to the drying effect of FA (Killedar et al., 2006; Scuron et al., 2019). To further clarify the main active ingredients and molecular mechanisms of the drying effect of raw FA, we integrated network pharmacology, molecular docking, and in vivo experiments. A review showed that flavonoids can be used to treat various types of tumors in clinical trials (Bisol et al., 2020). Maleki and colleagues showed that flavonoids also could inhibit the development and progression of inflammatory diseases by inhibiting the enzymes and transcription factors involved in inflammatory reactions and by acting on immune cells (Maleki et al., 2019). Recent studies have found that sinensetin can attenuate interleukin-1β-induced cartilage damage ameliorate osteoarthritis by regulating expression of serpin family A member 3, and possesses strong anticancer activities and a wide range of pharmacological (anti-inflammatory, anti-obesity, anti-dementia, vasorelaxant) activities. ( Han et al., 2021; Liu et al., 2022b). Sinensetin has been shown to inhibit activation of STAT3 phosphorylation in a phorbol 12-myristate 13-acetate plus A23187-stimulated human mast cell line in a dose-dependent manner. Activated STAT3 upregulates expression of proinflammatory mediators and induces expression of inflammation-related genes. These findings further indicate that sinensetin is involved in mast cell-mediated inflammation (Chae et al., 2017). Wang and colleagues found that 5-demethylmobiletin regulates the JAK2-STAT3 pathway, thereby inhibiting JAK2 expression and STAT3 phosphorylation (Wang et al., 2019). Qi and collaborators discovered that chrysin can inhibit lipopolysaccharide-induced phosphorylation of JAK-STATs, nuclear translocation of STAT1 and STAT3, and release of tumor necrosis factor-α, monocyte chemoattractant protein-1, interleukin-6, and production of reactive oxygen speciesin RAW264.7 cells (Qi et al., 2018). Mantawy and coworkers suggested that chrysin can reduce lipid peroxidation, enhance secretion of antioxidant enzymes, reduce expression of p53, Bax, Puma, Noxa, cytochrome c, caspase-3, increase expression of B-cell lymphoma-2, and inactivate mitogen-activated protein kinase, p38, and c-Jun N-terminal kinases. Chrysin can also reduce expression of nuclear factor-kappa B and phosphatase and tensin homolog, and augment the VEGF/AKT pathway. ( Mantawy et al., 2014; Liang et al., 2022). In vitro and in vivo, chrysin may reduce the proliferation and induce the apoptosis and death of cells, reduce inflammation, and inhibit tumor growth by activating the Notch1 signaling pathway (Yu et al., 2012; Xue et al., 2016). Xu et al. demonstrated that tangeretin could improve allergic rhinitis by mediating inhibition of the Notch-1 signaling pathway and promoting the differentiation of regulatory T cells (Xu et al., 2019). To sum up, we selected the four components stated above to verify the dryness effect of FA. Analyses of a PPI network revealed that the target of dryness effect was mainly due to proteases, including PIK3R1, PIK3CA, AKT1, and LCK. Enrichment analyses using the GO database showed that the drying effect of FA was mainly through acting on the endomembrane system, regulating cellular response to an endogenous stimulus, and positively regulating apoptosis, cell proliferation, and macromolecular metabolism. To a certain extent, these data also suggest that FA has multiple components, multiple targets, and multiple pathways. ## 5 Conclusion Four components of FA (tangeretin, sinensetin, chrysin, 5-Demethylmobiletin) are closely related to the JAK-STAT3, PI3K-AKT, and the other signaling pathways. These components will be important in the drying effect of FA if it is used in a TCM formulation. They can regulate the protein expression of JAK2, STAT3, PI3K, LCK, VEGFA and AKT1, affect water metabolism in the body and, finally, result in a drying effect. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. ## Ethics statement The animal study was reviewed and approved by Experimental Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine. ## Author contributions YL integrated the data and wrote the manuscript. JZ and YL, and QS accomplished the pharmacological study. MY and QG executed the literature search. HT and QS directed the data processing. JZ and LZ conceptualized and designed the experimental plan. All authors participated in drafting of the manuscript and revising it before final submission. YL and JZ contributed equally to this work and share first authorship. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1114010/full#supplementary-material ## References 1. Bisol Â., de Campos P. S., Lamers M. L.. Flavonoids as anticancer therapies: A systematic review of clinical trials. Phytotherapy Res. (2020) 34 568-582. 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--- title: Association between serum manganese and serum klotho in a 40–80-year-old American population from NHANES 2011–2016 authors: - Guoyu Guan - Jiasheng Cai - Songbai Zheng - Yanzhen Xiang - Shijin Xia - Yixuan Zhang - Jiaqiang Shi - Jun Wang journal: Frontiers in Aging year: 2023 pmcid: PMC10031017 doi: 10.3389/fragi.2023.1120823 license: CC BY 4.0 --- # Association between serum manganese and serum klotho in a 40–80-year-old American population from NHANES 2011–2016 ## Abstract Objectives: Manganese is one of the essential trace elements that are required by the human body. Klotho protein is a classic anti-aging marker. The association between the levels of serum manganese and serum klotho in individuals between the ages of 40–80 in the United States remains unclear. Methods: Data for this cross-sectional study was obtained from the National Health and Nutrition Examination Survey (NHANES 2011–2016) in the United States. We performed multiple linear regression analyses to investigate the association between the levels of serum manganese and serum klotho. Furthermore, we performed a fitted smoothing curve according to a restricted cubic spline (RCS). Stratification and subgroup analyses were performed for further verification of the results. Results: Weighted multivariate linear regression analysis showed that serum manganese levels were independently and positively associated with serum klotho levels (β = 6.30, $95\%$ confidence interval: 3.30–9.40). Kruskal–Wallis test showed that participants with higher manganese quartiles had higher serum klotho levels (Q1: 808.54 ± 256.39 pg/mL; Q2: 854.56 ± 266.13 pg/mL; Q3: 865.13 ± 300.60 pg/mL; and Q4: 871.72 ± 338.85 pg/mL, $p \leq 0.001$). The RCS curve indicated that the association between the levels of serum manganese and serum klotho was non-linear. Furthermore, a significantly positive association was found between serum manganese and serum klotho levels in the majority of subgroups. Conclusion: A non-linear and positive association was found between the levels of serum manganese and serum klotho in individuals aged 40–80 in the United States according to the NHANES (2011–2016). ## 1 Introduction Aging is involved in the pathological alterations of nearly all tissues or organs of the body, leading eventually to debilitating or chronic diseases (Prud’homme et al., 2022). In the past 30 years, klotho has been one of the classic anti-aging biomarkers. This protein performs a considerable role in regulating the activity of fibroblast growth factor and maintaining phosphate equilibrium in the body (Kuro-o et al., 1997; Buchanan et al., 2020). The core members of the klotho family proteins are a-klotho, ß-klotho, and γ-klotho (Hu et al., 2013; Kuro and Moe, 2017; Kuro, 2019), which are isomers of each other and are single-pass transmembrane proteins. Soluble a-klotho is found in the cerebrospinal fluid, blood, and urine (Imura et al., 2004; Kurosu et al., 2005; Akimoto et al., 2012), which is hereby referred to as “klotho” in this study. In vivo experiments and clinical studies show that low serum klotho levels accelerate senescence (Xiao et al., 2004; Dërmaku-Sopjani et al., 2013) and death (Kresovich and Bulka, 2022), and are also associated with an increased risk of age-related diseases such as atherosclerosis (Pan et al., 2018; Chen et al., 2021), chronic kidney disease (Manya et al., 2010; Drew et al., 2017), type 2 diabetes mellitus (T2DM) (Nie et al., 2017), metabolic syndrome (Kim et al., 2019), and pulmonary emphysema (Suga et al., 2000). A previous study showed that klotho increased resistance to oxidative stress by upregulating superoxide dismutase (SOD) (Kurosu et al., 2005) and slowed aging by inhibiting insulin and insulin-like growth factor-1 (IGF-1) signaling, which was affected by nutritional status (Partridge and Gems, 2002). Manganese is an essential micronutrient without adequate levels in virtually all types of diets (Parmalee and Aschner, 2016). Previous studies showed that manganese was involved in many crucial physiological activities of cells (Malecki et al., 1994; Aschner and Aschner, 2005; Aschner and Erikson, 2017) such as regulating immune functions, stabilizing blood sugar levels, maintaining cellular energy, and resisting oxidative stress. Previous clinical trials have shown that low serum manganese levels were associated with a higher risk of hypertension, renal dysfunction, T2DM, and impaired longevity (Koh et al., 2014; Lv et al., 2021; Zhang et al., 2022). The increase in antioxidant levels is closely related to the longevity of the body (Finkel and Holbrook, 2000). Manganese can regulate the expression and activity of manganese superoxide dismutase (MnSOD) (Smith et al., 2017; Li and Yang, 2018) and then decrease the oxidative stress of the body, to slow down aging (Malecki et al., 1994). Based on the circumstantial evidence that both serum klotho and serum manganese levels decrease with older age (Oulhote et al., 2014) and can decrease oxidative stress by regulating the activity of SOD to promote longevity (Kurosu et al., 2005; Malecki et al., 1994). Furthermore, nutritional intake can affect insulin and IGF-1 signaling, which is related to the anti-aging properties of klotho (Partridge and Gems, 2002). Therefore, we reasonably speculated that serum manganese, which is an essential micronutrient, might be associated with serum klotho. If this correlation is confirmed through statistical and pathophysiological analyses, we propose that serum manganese levels can be a potential biomarker of klotho. As a proper index, serum manganese levels might rightly reflect malnutrition in the process of aging. Therefore, a large-scale cross-sectional study was performed to investigate the association between serum manganese and serum klotho levels in individuals between the ages of 40–80 in the United States according to the NHANES (2011–2016). ## 2.1 Demographics of the study participants The National Health and Nutrition Examination Survey (NHANES) is an accessible database from the United States that contains questionnaire data on national health and nutritional status (Yang et al., 2014) as well as the results of laboratory and imaging tests (Xiao et al., 2021). Continuous information on the non-institutionalized US population was included in the NHANES, with every 2 years representing 1 cycle. This retrospective study analyzed health information collected from 29,902 subjects from NHANES during 2011–2016 (2011–2012, 2013–2014, and 2015–2016). Information about serum manganese and serum klotho was completely provided only in these years. This survey was conducted periodically under the approval of the Institutional Review Board (ERB) of the National Center for Health Statistics (NCHS), and each individual provided signed informed consent (Yang et al., 2014). The participants who lacked information on manganese ($$n = 13$$,394) or klotho ($$n = 11$$,826) were excluded. Pregnant women ($$n = 125$$) and cancer patients ($$n = 511$$) were also excluded from the study. In addition, subjects without covariates ($$n = 1032$$) were removed, which included educational attainment ($$n = 120$$), income-to-poverty ratio (PIR; $$n = 329$$), alcohol use ($$n = 285$$), body mass index (BMI, $$n = 27$$), diabetes ($$n = 119$$), 24-h total energy intake ($$n = 151$$), and smoking habit ($$n = 1$$). Eventually, we enrolled a total of 3014 participants in this study. A flowchart depicting the subject screening process is shown in Figure 1. FIGURE 1: Flowchart depicting the process of participant recruitment in this study. ## 2.2 Manganese measurements The whole blood of participants was collected and dispatched to the Centers for Disease Control and Prevention, Division of Laboratory Sciences, and National Center for Environmental Health (Atlanta, GA, United States) for analyses. The blood samples were stored at 30°C and then diluted (1 part sample +48 parts diluent1 + part water). Inductively coupled plasma-mass spectrometer (ICP-MS) with dynamic reaction cell technology) separates Mn under oxygen pressurization. For internal quality control, spiked pools was used, and external calibration utilized standard reference materials to meet the quality control standard (Gbemavo and Bouchard, 2021). After the detection, the mass spectrometer was cleaned in an aqueous solution of $0.01\%$ ammonium pyrrolidinedithiocarbamate (APDC) for the next usage. ## 2.3 Klotho measurements Northwest Lipid Metabolism and Diabetes Research Laboratories affiliated with the University of Washington used an ELISA kit (Fujioka Immunobiology Laboratory, Japan) for detecting the serum klotho levels of the whole blood samples (Alkalbani et al., 2022). All samples were stored under appropriate freezing (−80°C) conditions before conducting the assay. Two parallel holes were created in the ELISA plate to measure the klotho concentration of quality control samples, and the average value was considered as the final concentration. The serum klotho levels among healthy people fluctuated from 285.8 to 1638.6 pg/mL. The actual minimum measured concentration of this ELISA kit was 4.33 pg/mL, which is higher than the low limit value reported by the producer (6.15 pg/mL) (Cai et al., 2023). In addition, all procedures were conducted under laboratory-specified criteria. ## 2.4 Covariate information We explored the association between serum manganese and serum klotho levels after adjusting for covariates selected in accordance with the literature (Xiao et al., 2021; Kim et al., 2022; Kresovich and Bulka, 2022). Sociodemographic characteristics were obtained using computer-assisted questionnaires, which included age, gender, race, educational attainment, marital status, and PIR. The health-related characteristics were also considered, which included the smoking habit (people who have or have never smoked >100 cigarettes in their lifetime), alcohol use (people who have or have never drunk >12 alcoholic beverages in a year), physical activity, BMI, and the 24-h total energy intake. In addition, two variables (i.e., the presence of diabetes and hypertension) were considered as medical comorbidities based on the response to the following question: “Has a doctor or other health professional ever told you that you suffered from diabetes/hypertension?” Most studies focusing on the two variables of blood manganese and blood klotho collected information on these two diseases (Wang et al., 2016; Nie et al., 2017; Gbemavo and Bouchard, 2021; Alkalbani et al., 2022; Chen et al., 2022; Cai et al., 2023). ## 2.5 Statistical analyses In this investigation, the data on normal distribution were displayed as the mean ± standard deviation (SD), while the data on the skewed distribution were presented as the median (interquartile range: IQR). The categorical variables were demonstrated as a percentage (%). A weighted multiple linear regression analysis was performed to estimate the association of the serum manganese levels with the serum klotho concentrations in three different models. In Model 1, there was no adjustment for any variables. In Model 2, there was an adjustment for only 3 variables, that is, age, gender, and race. Building on Model 2, in Model 3, there was a further adjustment for the following variables: educational attainment, marital status, PIR, smoking habit, alcohol use, physical activity, BMI, 24-h total energy intake, diabetes, and hypertension. To illustrate the stability of the present results, the association between the serum manganese and serum klotho level was obtained with due consideration of the manganese concentration as a continuous variable and a categorical variable, respectively. We also transformed the raw data of these two variables by the lg function and then performed a fitted smoothing curve of the correlation between the serum manganese and serum klotho levels based on the restricted cubic spline (RCS). To determine the threshold, the non-segmented and segmented regression models were compared by the log-likelihood ratio test. Moreover, stratified and subgroup analyses were conducted considering age, gender, race, diabetes, and hypertension as stratified variables, respectively. The statistical analysis software used in this study included EmpowerStats and R version 4.2.0. Two-sided $p \leq 0.05$ was considered to indicate statistical significance. ## 3.1 The baseline characteristics of the participants In this study, a total of 3014 participants were included from the NHANES (2011–2016) in the United States. The specific screening of the participants is shown in Figure 1. According to the measured distribution of serum manganese levels (Q1: ≤7.27 μg/L; Q2: 7.27–9.10 μg/L; Q3: 9.10–11.55 μg/L; and Q4: >11.55 μg/L), participants were divided into quartiles based on the intuitively describing weighted demographic and medical characteristics (Table 1). Overall, the average age of participants was 56.83 ± 10.50, $50.13\%$ were female, $38.79\%$ belonged to the non-Hispanic white race, $55.01\%$ had educational qualifications beyond high school level, $63.67\%$ had a partner, and the mean BMI was 29.92 ± 6.89 kg/m2. In the diverse quartile of serum manganese levels (Q1–Q4), age, gender, race, educational attainment, marital status, smoking habit, alcohol use, physical activity, BMI, and 24-h total energy intake were significantly different ($p \leq 0.05$). Compared with the other quartiles, participants in the Q1 group were more likely to be old, male, people who consume more energy in 24 h, and had lower serum klotho levels. Participants in the Q1 group had the highest proportions of smokers, alcohol consumers, and hypertension. Notably, participants with higher manganese quartiles had higher serum klotho levels (Q1: 808.54 ± 256.39 pg/mL; Q2: 854.56 ± 266.13 pg/mL; Q3: 865.13 ± 300.60 pg/mL; and Q4: 871.72 ± 338.85, $p \leq 0.001$) and lower age (Q1: 58.26 ± 10.56; Q2: 57.34 ± 10.30; Q3: 56.54 ± 10.37; and Q4: 55.17 ± 10.53, $p \leq 0.001$). TABLE 1 \| Variable \| Serum manganese concentration, μg/L \| Serum manganese concentration, μg/L.1 \| Serum manganese concentration, μg/L.2 \| Serum manganese concentration, μg/L.3 \| Serum manganese concentration, μg/L.4 \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| Overall \| Quartile 1 \| Quartile 2 \| Quartile 3 \| Quartile 4 \| p-value \| \| Variable \| Overall \| (≤7.27) \| (7.27–9.10) \| (9.10–11.55) \| (>11.55) \| p-value \| \| N* \| 3014 \| 751 \| 754 \| 755 \| 754 \| \| \| Age, % \| 56.83 ± 10.50 \| 58.26 ± 10.56 \| 57.34 ± 10.30 \| 56.54 ± 10.37 \| 55.17 ± 10.53 \| <0.001 \| \| Gender, % \| \| \| \| \| \| <0.001 \| \| Male \| 49.87 \| 60.11 \| 55.29 \| 43.57 \| 34.62 \| \| \| Female \| 50.13 \| 39.89 \| 44.71 \| 56.43 \| 65.38 \| \| \| Race, % \| \| \| \| \| \| <0.001 \| \| Non-Hispanic white \| 38.79 \| 76.02 \| 76.39 \| 72.02 \| 63.61 \| \| \| Non-Hispanic black \| 22.53 \| 13.24 \| 8.90 \| 8.28 \| 6.05 \| \| \| Mexican American \| 13.70 \| 4.25 \| 4.73 \| 5.86 \| 11.36 \| \| \| Other \| 24.98 \| 6.49 \| 9.99 \| 13.84 \| 18.98 \| \| \| Educational attainment, % \| \| \| \| \| \| 0.018 \| \| Less than high school \| 23.16 \| 10.84 \| 13.59 \| 14.26 \| 16.83 \| \| \| High school \| 21.83 \| 23.47 \| 22.02 \| 19.11 \| 18.91 \| \| \| College or higher \| 55.01 \| 65.69 \| 64.40 \| 66.64 \| 64.26 \| \| \| Marital status, % \| \| \| \| \| \| 0.012 \| \| Have a partner \| 63.67 \| 69.65 \| 73.82 \| 68.33 \| 65.94 \| \| \| No partner \| 26.48 \| 22.90 \| 18.65 \| 21.34 \| 25.28 \| \| \| Unmarried \| 9.85 \| 7.45 \| 7.53 \| 10.33 \| 8.79 \| \| \| PIR \| 2.64 ± 1.65 \| 3.32 ± 1.60 \| 3.34 ± 1.58 \| 3.23 ± 1.58 \| 3.13 ± 1.68 \| 0.054 \| \| Smoking habit, % \| \| \| \| \| \| 0.001 \| \| Yes \| 45.62 \| 52.37 \| 47.34 \| 42.52 \| 45.45 \| \| \| No \| 54.38 \| 47.63 \| 52.66 \| 57.48 \| 54.55 \| \| \| Alcohol use, % \| \| \| \| \| \| <0.001 \| \| Yes \| 72.33 \| 86.27 \| 84.86 \| 77.01 \| 72.49 \| \| \| No \| 27.67 \| 13.73 \| 15.14 \| 23.00 \| 27.51 \| \| \| Diabetes, % \| \| \| \| \| \| 0.121 \| \| Yes \| 18.81 \| 14.70 \| 11.35 \| 14.92 \| 14.67 \| \| \| No \| 81.19 \| 85.30 \| 88.65 \| 85.08 \| 85.33 \| \| \| Hypertension, % \| \| \| \| \| \| 0.056 \| \| Yes \| 45.55 \| 42.79 \| 36.86 \| 42.53 \| 39.81 \| \| \| No \| 54.45 \| 57.21 \| 63.14 \| 57.47 \| 60.19 \| \| \| Physical activity, % \| \| \| \| \| \| 0.035 \| \| Vigorous \| 17.98 \| 21.16 \| 24.84 \| 18.31 \| 19.08 \| \| \| Moderate \| 23.89 \| 25.77 \| 24.67 \| 24.56 \| 25.16 \| \| \| Never \| 58.13 \| 53.07 \| 50.49 \| 57.13 \| 55.76 \| \| \| BMI, kg/m2 \| 29.92 ± 6.89 \| 29.33 ± 6.37 \| 29.08 ± 6.00 \| 30.73 ± 6.61 \| 30.41 ± 7.18 \| <0.001 \| \| 24-h total energy intake, kcal \| 2030.34 ± \| 2216.48 ± 894.19 \| 2190.13 ± 915.31 \| 2025.17 ± 805.35 \| 1970.57 ± 790.71 \| <0.001 \| \| 24-h total energy intake, kcal \| 877.34 \| 2216.48 ± 894.19 \| 2190.13 ± 915.31 \| 2025.17 ± 805.35 \| 1970.57 ± 790.71 \| <0.001 \| \| klotho (pg/mL) \| 860.80 ± 312.58 \| 808.54 ± 256.39 \| 854.56 ± 266.13 \| 865.13 ± 300.60 \| 871.72 ± 338.85 \| <0.001 \| ## 3.2 Association between serum manganese and serum klotho levels Table 2 shows the association between serum manganese and serum klotho in the three models. In the unadjusted model, serum manganese was markedly positively associated with serum klotho (β = 7.30, CI: 4.40–10.20, $p \leq 0.001$). This association was observed even after partial adjustment (β = 6.50, CI: 3.50–9.50, $p \leq 0.001$) and full adjustment (β = 6.30, CI: 3.30–9.40, $p \leq 0.001$). These three models showed a significantly positive association between serum manganese and serum klotho levels after considering manganese levels as a categorical variable. Taking Q1 as a reference, serum klotho levels increased with increasing serum manganese level quartile (P for trend <0.001). Moreover, the restricted cubic spline curve Supplementary Figure S1 showed the non-linear association of serum manganese with serum klotho (P for non-linearity < 0.05). As shown in Supplementary Table S1, this positive association was significant when lg (manganese) was lower than 0.9 ($p \leq 0.05$), whereas the association was insignificant when lg (manganese) was higher than 0.9 ($p \leq 0.05$). TABLE 2 \| Unnamed: 0 \| Model 1 \| Model 2 \| Model 3 \| \| --- \| --- \| --- \| --- \| \| \| β (95% CI, P) \| β (95% CI, P) \| β (95% CI, P) \| \| Manganese \| 7.30 (4.40, 10.20) \| 6.50 (3.50, 9.50) \| 6.30 (3.30, 9.40) \| \| Manganese \| <0.001 \| <0.001 \| <0.001 \| \| Manganese (quartiles) \| \| \| \| \| Q1 \| Ref \| Ref \| Ref \| \| Q2 \| 46.01 (17.38, 74.64) \| 46.11 (17.54, 74.69) \| 44.85 (16.30, 73.40) \| \| Q2 \| <0.010 \| <0.010 \| <0.010 \| \| Q3 \| 56.58 (27.58, 85.59) \| 53.32 (24.07, 82.57) \| 50.90 (21.52, 80.28) \| \| Q3 \| <0.001 \| <0.001 \| <0.001 \| \| Q4 \| 63.18 (33.04, 93.33) \| 55.42 (24.34, 86.50) \| 52.25 (21.11, 83.40) \| \| Q4 \| <0.001 \| <0.001 \| <0.010 \| \| P for trend \| <0.001 \| <0.001 \| <0.001 \| ## 3.3 Stratified subgroup analysis Serum manganese levels were positively associated to serum klotho levels after they were stratified based on variates such as age, gender, race, hypertension, and diabetes (P for interaction >0.05) (Figure 2). The following subgroups showed significant positive association between serum manganese levels and serum klotho levels: those aged 40–44 years (β = 10.89, CI: 3.16–18.63) or 45–64 years (β = 4.69, CI: 0.80–8.59), females (β = 7.53, CI: 3.63–11.42), non-Hispanic whites (β = 6.14, CI: 1.45–10.83) or other ethnicities (β = 10.54, CI: 3.47–17.61), participants with hypertension (β = 8.29, CI: 3.74–12.84) or without hypertension (β = 4.83, CI: 0.77–8.89), and participants with diabetes (β = 11.09, CI: 4.13–18.05) or without diabetes (β = 5.28, CI: 1.97–8.60). FIGURE 2: Subgroup analyses of the association between serum manganese and serum klotho levels. Each stratification was adjusted for all factors (i.e., age, gender, race, educational attainment, marital status, PIR, smoking habit, alcohol use, physical activity, BMI, 24-h total energy intake, diabetes, and hypertension), except for the stratification factor itself. ## 4 Discussion In this study, we observed a non-linear positive association between serum manganese levels and serum klotho levels among middle-aged and elderly in the United States, according to the NHANES conducted from 2011–2016. After adjusting for all covariates, each 1.00 μg/L increase in manganese levels corresponded to the 6.30 pg/mL increase in klotho levels. This significantly positive association was also observed in most subgroups. The global population is accelerating into the aging stage. According to statistics (Beard and Bloom, 2015), the elderly population will account for $11\%$–$22\%$ by 2050. Therefore, fostering the healthy aging of the elderly is crucial. Nutrients have been found to alleviate aging and age-related diseases among humans. The present study showed a positive association between manganese and the long-lived protein klotho for the first time. As one of the micronutrients, manganese possesses a positive effect of delaying aging (Lv et al., 2021). Lv et al. [ 2021] found that serum manganese levels found in centenarians (11.41 μg/L) were higher than those found in younger elderly (10.23 μg/L), which indirectly confirmed this result. According to the present study, the positive association between serum manganese and serum klotho levels was significant among the population aged 40–44 years and 45–64 years ($p \leq 0.05$), whereas the association was non-significant among the population aged 65–80 years ($p \leq 0.05$). We speculated two reasons to explain this result. First, manganese absorption might differ between older adults and younger adults. The level of the divalent metal transporter-1 was lower in old mice than that in adult mice (Lossow et al., 2020); thus, the old mice inadequately absorbed serum manganese and were prone to manganese poisoning. The second potential reason was the reduction in sample size after grouping. The effect of gender on this association was noteworthy. This positive association was significant among females, whereas it was insignificant among males. We speculated that this result might be caused by the difference in manganese metabolism between women and men. Lee and Kim (Lee and Kim, 2014) discovered that the serum ferritin level was lower in women than in men, which led to the blood manganese levels of women being prone to higher than in men. Lv et al. [ 2021] also found that the serum manganese levels in males among the elderly were lower than that in females, which might partially explain why women tended to live longer. However, another study failed to show gender-related differences in serum manganese levels among the elderly (Rambousková et al., 2013). These two results are inconsistent. A reasonable speculation is the racial differences between these two studies. The former study included Asian people, whereas the latter included European people. The present study showed that the difference in races among participants could affect the positive association between serum manganese and serum klotho levels. This positive association of serum manganese levels with serum klotho levels was significant in non-Hispanic whites ($p \leq 0.05$) compared with non-Hispanic black and Mexican Americans ($p \leq 0.05$). This study has many advantages: 1) The sample size of this study was large, with a total of 3014 subjects, which was the most representative cross-sectional study on manganese and the longevity protein in Americans. 2) We performed threshold-effect and saturation-effect analyses and determined the lg (manganese) value of 0.90 as the threshold, which has the guiding significance for facilitating the healthy aging of middle-aged and elderly people (Supplementary Table S1). 3) We performed stratification and interaction tests to evaluate the stability of this result further. However, the present study has the following limitations. 1) The data from the questionnaire survey about smoking habit, alcohol use, total energy intake, and physical activity inevitably had some recall bias. 2) Due to the cross-sectional nature of this study, a causal association between blood manganese and blood klotho levels could not be established. 3) Although we adjusted for most confounding factors, a few confounding factors might have been missed, which would have affected the final results. Thus, further prospective studies and basic mechanistic research are crucial to determine the precise effect of manganese levels on klotho levels. In conclusion, the present study showed a significantly positive association between serum manganese and serum klotho levels after full adjustment for potential confounders. Due to its cross-sectional nature, more basic studies should be performed to clarify the direction and intensity of the effect of manganese on klotho. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ## Ethics statement The studies involving human participants were reviewed and approved by National Center for Health Statistics (NCHS) Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YX and YZ were responsible for data collection and supervision; JS and JW were responsible for writing the methodology; SX and JC developed the study design and proofreading the article; GG was responsible for article writing, analysis, and data processing; SZ reviewed and edited the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'Effect of pregnancy versus postpartum maternal isoniazid preventive therapy on infant growth in HIV-exposed uninfected infants: a post-hoc analysis of the TB APPRISE trial' authors: - Ashenafi S. Cherkos - Sylvia M. LaCourse - Daniel A. Enquobahrie - Barbra A. Richardson - Sarah Bradford - Grace Montepiedra - Blandina T. Mmbaga - Tapiwa Mbengeranwa - Gaerolwe Masheto - Patrick Jean–Phillippe - Nahida Chakhtoura - Gerhard Theron - Adriana Weinberg - Haseena Cassim - Mpho S. Raesi - Elsie Jean - Deo Wabwire - Teacler Nematadzira - Lynda Stranix-Chibanda - Anneke C. Hesseling - Linda Aurpibul - Amita Gupta - Grace John-Stewart - Timothy R. Sterling - Timothy R. Sterling - Renee Browning - Katie McCarthy - Lisa Aaron - Katherine Shin - Amanda Golner - Bonnie Zimmer - Jyoti S. Mathad - Savita Pahwa - Vandana Kulkarni - Diane Costello - Vivian Rexroad - Monica Gandhi - Joan Du Plessis - Amy James Loftis journal: eClinicalMedicine year: 2023 pmcid: PMC10031034 doi: 10.1016/j.eclinm.2023.101912 license: CC BY 4.0 --- # Effect of pregnancy versus postpartum maternal isoniazid preventive therapy on infant growth in HIV-exposed uninfected infants: a post-hoc analysis of the TB APPRISE trial ## Body Research in contextEvidence before this studyPregnancy isoniazid preventive therapy (IPT) has not been associated with adverse pregnancy outcomes in observational studies on pregnant women. A rigorously assessed multicenter randomized control trial in women living with HIV on antiretroviral therapy revealed that pregnancy-IPT was associated with an increased incidence of composite adverse pregnancy outcomes (stillbirth, spontaneous abortion, low birth weight (LBW), preterm delivery, and infant congenital anomalies). However, in subsequent secondary analyses of pregnancy cohorts and in a large-scale study of programmatic data in South Africa, antenatal IPT did not appear to be associated with adverse pregnancy outcomes. On the basis of these data, the World Health Organization (WHO) continues to recommend IPT for pregnant women living with HIV, both for their own and their infant's health. In reviewing these studies, we noticed that the long-term and sex-specific effects of pregnancy-IPT on HIV-exposed-uninfected infants were unknown. Added value of this studyThis post-hoc analysis of the TB APPRISE trial studied the effects of pregnancy-IPT beyond the previously reported composite pregnancy and birth outcomes to include longer term infant growth outcomes and employed sex-stratified analyses. The data shows that maternal IPT during pregnancy was associated with a significantly increased risk of low birth weight and risk of becoming underweight among HEU infants. Male infants exposed to pregnancy-IPT had a significant risk of low birth weight, preterm birth, and longer-term risk of being underweight that persisted over the first year of life. Implications of all the available evidenceThese data add to prior TB APPRISE findings of increased risk of composite adverse pregnancy and birth outcomes associated with IPT during pregnancy, suggesting IPT during pregnancy also impacts the birth size and subsequent infant growth, specifically among male infants. Given that WHO recommended IPT and ART during pregnancy based on data from nonpregnant adults and the absence of harm from observational studies, these data could inform monitoring and management and warrants further examination of potential mechanisms. ## Summary ### Background Isoniazid preventive therapy (IPT) initiation during pregnancy was associated with increased incidence of adverse pregnancy outcomes in the TB APPRISE trial. Effects of in utero IPT exposure on infant growth are unknown. ### Methods This post-hoc analysis used data from the TB APPRISE trial, a multicentre, double-blind, placebo-controlled trial, which randomised women to 28-week IPT starting in pregnancy (pregnancy-IPT) or postpartum week 12 (postpartum-IPT) in eight countries with high tuberculosis prevalence. Participants were enrolled between August 2014 and April 2016. Based on modified intent-to-treat analyses, we analysed only live-born babies who had at least one follow-up after birth and compared time to infant growth faltering between arms to 12 weeks and 48 weeks postpartum in overall and sex-stratified multivariable Cox proportional hazards regression. Factors adjusted in the final models include sex of infant, mother's baseline BMI, age in years, ART regimen, viral load, CD4 count, education, and household food insecurity. ### Results Among 898 HIV-exposed uninfected (HEU) infants, 447 ($49.8\%$) were females. Infants in pregnancy-IPT had a 1.47-fold higher risk of becoming underweight by 12 weeks (aHR 1.47 [$95\%$ CI: 1.06, 2.03]) than infants in the postpartum-IPT; increased risk persisted to 48 weeks postpartum (aHR 1.34 [$95\%$ CI: 1.01, 1.78]). Maternal IPT timing was not associated with stunting or wasting. In sex-stratified analyses, male infants in the pregnancy-IPT arm experienced an increased risk of low birth weight (LBW) (aRR 2.04 [$95\%$ CI: 1.16, 3.68), preterm birth (aRR 1.81 [$95\%$ CI: 1.04, 3.21]) and becoming underweight by 12 weeks (aHR 2.02 [$95\%$ CI: 1.29, 3.18]) and 48 weeks (aHR 1.82 [$95\%$ CI: 1.23, 2.69]). Maternal IPT timing did not influence growth in female infants. ### Interpretation Maternal IPT during pregnancy was associated with an increased risk of LBW, preterm birth, and becoming underweight among HEU infants, particularly male infants. These data add to prior TB APPRISE data, suggesting that IPT during pregnancy impacts infant growth, which could inform management, and warrants further examination of mechanisms. ### Funding The TB APPRISE study Supported by the $\frac{10.13039}{100000002}$National Institutes of Health (NIH) (award numbers, UM1AI068632 [IMPAACT LOC], UM1AI068616 [IMPAACT SDMC], and UM1AI106716 [IMPAACT LC]) through the $\frac{10.13039}{100000060}$National Institute of Allergy and Infectious Diseases, with cofunding from the $\frac{10.13039}{100009633}$Eunice Kennedy Shriver National Institute of Child Health and Human Development (contract number, HHSN275201800001I) and the $\frac{10.13039}{100000025}$National Institute of Mental Health. ## Evidence before this study Pregnancy isoniazid preventive therapy (IPT) has not been associated with adverse pregnancy outcomes in observational studies on pregnant women. A rigorously assessed multicenter randomized control trial in women living with HIV on antiretroviral therapy revealed that pregnancy-IPT was associated with an increased incidence of composite adverse pregnancy outcomes (stillbirth, spontaneous abortion, low birth weight (LBW), preterm delivery, and infant congenital anomalies). However, in subsequent secondary analyses of pregnancy cohorts and in a large-scale study of programmatic data in South Africa, antenatal IPT did not appear to be associated with adverse pregnancy outcomes. On the basis of these data, the World Health Organization (WHO) continues to recommend IPT for pregnant women living with HIV, both for their own and their infant's health. In reviewing these studies, we noticed that the long-term and sex-specific effects of pregnancy-IPT on HIV-exposed-uninfected infants were unknown. ## Added value of this study This post-hoc analysis of the TB APPRISE trial studied the effects of pregnancy-IPT beyond the previously reported composite pregnancy and birth outcomes to include longer term infant growth outcomes and employed sex-stratified analyses. The data shows that maternal IPT during pregnancy was associated with a significantly increased risk of low birth weight and risk of becoming underweight among HEU infants. Male infants exposed to pregnancy-IPT had a significant risk of low birth weight, preterm birth, and longer-term risk of being underweight that persisted over the first year of life. ## Implications of all the available evidence These data add to prior TB APPRISE findings of increased risk of composite adverse pregnancy and birth outcomes associated with IPT during pregnancy, suggesting IPT during pregnancy also impacts the birth size and subsequent infant growth, specifically among male infants. Given that WHO recommended IPT and ART during pregnancy based on data from nonpregnant adults and the absence of harm from observational studies, these data could inform monitoring and management and warrants further examination of potential mechanisms. ## Introduction Prevention of tuberculosis (TB) among women living with HIV (WLWH) is a high priority in TB endemic areas.1 Children born to WLWH who are HIV-exposed but uninfected (HEU) have a higher risk of TB exposure, and TB-related morbidity and mortality compared to HIV-unexposed uninfected (HUU) children.2 Thus, the World Health Organization (WHO) recommends TB preventive therapy such as isoniazid preventive therapy (IPT), which reduces the risk of progression from TB infection to TB disease,3,4 for WLWH, both for their own and their infant's health, including during pregnancy.5 Observational studies on pregnant women, primarily secondary analyses, did not reveal associations of pregnancy IPT with adverse pregnancy outcomes.6,7 Until recently, safety data regarding IPT in pregnancy rigorously assessed in a trial have been lacking. The TB APPRISE trial evaluated the safety of the immediate (pregnancy-IPT) arm vs. deferred (postpartum-IPT) arm in WLWH on antiretroviral therapy (ART).8 In this study, although pregnancy-IPT was as safe as postpartum-IPT with regards to adverse maternal outcomes, pregnancy-IPT was associated with an increased incidence of composite adverse pregnancy outcomes (stillbirth or spontaneous abortion, low birth weight (LBW), preterm delivery, or infant congenital anomalies).8 Exposure to HIV and ART in-utero may increase the risk of preterm birth, LBW and low birth length, small for gestational age (SGA), stillbirth,9, 10, 11, 12, and growth compromise.13 The potential effect of in utero IPT exposure on long-term growth in HEU is not known. Using the TB APPRISE study, we examined the effects of maternal pregnancy-IPT versus postpartum-IPT on growth, and assessed cofactors of growth among HEU in the first year of life. Additionally, we evaluated whether infant sex-modified maternal IPT effect on birth outcomes and infant growth. ## Parent trial design and intervention This post-hoc analysis utilised data from the P1078 TB APPRISE trial – a randomised, double-blind, placebo-controlled, multicentre, non-inferiority study designed to evaluate the effect of pregnancy-IPT vs postpartum-IPT on maternal complications and composite adverse birth outcomes. The trial, as reported in detail previously,8 was conducted in eight countries (Botswana, Haiti, India, South Africa, Tanzania, Thailand, Uganda, and Zimbabwe) at 13 different sites with high TB prevalence (>60 cases per 100,000). Participants were randomised to receive a 28-week course of IPT (300 mg daily) either during pregnancy (pregnancy-IPT) or at postpartum week 12 (postpartum-IPT). The pregnancy-IPT arm received isoniazid daily for 28 weeks (initiated between 14- and 34-weeks gestation, immediately after enrolment), then switched to placebo until the 40th week postpartum. The postpartum-IPT arm initiated a placebo immediately after trial entry during pregnancy until the 12th week postpartum and then switched to isoniazid daily until the 40th week postpartum. All participants received vitamin B6 and a prenatal multivitamin from week 0 to week 40 postpartum. The randomisation was stratified by the gestational age at trial entry (≥14 weeks to <24 weeks or ≥24 weeks to ≤34 weeks) and was balanced at each site. As detailed in the parent paper,8 all women provided written informed consent and all local and collaborating institutional review boards approved it. An independent data and safety monitoring board reviewed it biannually. A proposal for these post-hoc analyses was approved by the IMPAACT operations team, and the manuscript was approved for publication by the IMPAACT publication team. The authors attested to the fidelity of the protocol and the accuracy of the analyses. This report conforms with CONSORT reporting guidelines. ## Participants and study period Pregnant WLWH, 14–34 weeks of gestation, weighing >35 kg, with >750 absolute neutrophil count cells/mm3, >7.5 g/dL haemoglobin, and >50,000 platelets count/mm3 were eligible. Participants were required to have liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and total bilirubin) at or below 1.25 times the upper limit of the normal range within 30 days prior to study entry. Women with active TB, recent TB exposure, TB treatment for more than 30 days in the previous year, or peripheral neuropathy of grade 1 or higher were excluded. The original study included 956 participants, 477 randomised to pregnancy-IPT and 479 to postpartum-IPT arm. Participants were enrolled between August 2014 and April 2016. This analysis was restricted to HEU infants born to mothers participating in the RCT. Exclusion criteria for this analysis included lack of infant information (withdrawal from the study before birth or no live birth, or lack of any growth measurement), HIV infection of the infant, and twin births. ## Infant growth characterisation Mother-infant pairs were followed up to 48 weeks postpartum. Weight and length of infants were measured at birth, 4th, 8th, 12th, 24th, 36th, 44th, and 48th weeks postpartum to the nearest 0.1 kg and 0.1 cm. The scales were calibrated regularly as per the manufacturer's instructions. Shoes and outer layers of clothing were removed before weight measurements were taken. Infants' lengths were measured with horizontal boards. The data collectors were trained and experienced in weight and length measurement. There was a two-week extension period for mothers who did not attend their last visit. Missing values at the scheduled last visit were replaced by measurements within two weeks after the end of the study. Low birth weight (LBW) was defined as less than 2.5 kg regardless of gestational age. Birth before completion of 37 weeks of pregnancy was regarded as preterm. Small for gestational age (SGA) was defined by weight less than the 10th percentile for gestational age using INTERGROWTH growth standards.14 Weight-for-age z-score [WAZ], weight-for-length z-score [WLZ], and length-for-age z-score [LAZ]) were defined using WHO child growth standards.15 Growth faltering was less than −2 Z-scores; with underweight defined as WAZ < –2, wasting WLZ < –2, and stunting LAZ < –2. ## Cofactors of growth faltering Cofactors of growth faltering assessed in the analyses included: Infant sex and maternal characteristics at enrolment, including body mass index (BMI), age, ART regimen, viral non-suppression (viral load ≥40 copies/ml), CD4 count (cells/mm3), education, and household food insecurity. Household food insecurity was considered positive if the respondents answered yes to at least one of the following questions: did you experience a lack of resources to get food, have you gone to bed hungry in the last 30 days, and have you passed the entire day and night hungry? For every 1 kg/m2 increase in maternal BMI, infant risk of being underweight decreased by $7\%$ (aHR 0.93 [$95\%$ CI: 0.90, 0.96]), and wasting risk decreased by $8\%$ (aHR 0.92 [$95\%$ CI: 0.89, 0.94]) (Table 4).Table 4Cofactors of risk of growth faltering in the overall cohort of HEU infants in analysis to 48 weeks postpartum. VariablesUnderweightaStuntingbWastingccHRd ($95\%$ CI)aHRe ($95\%$ CI)P-valuecHRd ($95\%$ CI)aHRe ($95\%$ CI)P-valueaHRd ($95\%$ CI)aHRe ($95\%$ CI)P-valueIPT started during pregnancy1.26 (0.96, 1.67)1.34 (1.01, 1.78)0.0421.05 (0.86, 1.26)1.08 (0.89, 1.30)0.461.06 (0.82, 1.38)1.05 (0.81, 1.37)0.72Male infant1.23 (0.93, 1.63)1.27 (0.96, 1.69)0.0951.14 (0.94, 1.37)1.14 (0.94, 1.38)0.200.80 (0.61, 1.03)0.82 (0.63, 1.07)0.14Mother's BMIf0.93 (0.90, 0.96)0.93 (0.90, 0.96)<0.00010.98 (0.96, 1.00)0.98 (0.96, 1.00)0.0750.93 (0.90, 0.96)0.92 (0.89, 0.95)<0.0001Age of a mother in years1.00 (0.97, 1.02)1.00 (0.97, 1.03)0.990.98 (0.96, 1.00)0.98 (0.96, 1.00)0.0351.03 (1.01, 1.05)1.04 (1.01, 1.06)0.0036Art regimen0.200.0930.20 3TC/FTC,TDF,EFV111111 3TC, ZDV, EFV0.82 (0.20, 3.29)0.89 (0.22, 3.65)2.03 (1.01, 4.10)1.91 (0.93, 3.91)–– 3TC/FTC, LVP/ATV, TDF/ZDV0.96 (0.35, 2.58)0.82 (0.30, 2.24)1.36 (0.75, 2.48)1.32 (0.72, 2.43)1.14 (0.47, 2.77)1.10 (0.45, 2.70) 3TC/FTC, ZDV/TDF, NVP1.36 (0.93, 1.99)1.59 (1.06, 2.37)1.20 (0.91, 1.57)1.34 (1.00, 1.79)1.09 (0.75, 1.58)1.13 (0.77, 1.67)Viral load (≥40 HIV copies/ml)1.07 (0.81, 1.43)1.11 (0.80, 1.51)0.551.11 (0.92, 1.36)1.13 (0.91, 1.40)0.260.90 (0.68, 1.17)0.83 (0.62, 1.12)0.23CD4 count (cells/mm3)0.300.900.60 <200111111 200–5000.71 (0.42, 1.22)0.65 (0.37, 1.12)0.95 (0.64, 1.40)0.91 (0.61, 1.36)0.92 (0.55, 1.54)0.87 (0.52, 1.47)0.61 >5000.80 (0.47, 1.36)0.75 (0.43, 1.32)0.98 (0.66, 1.44)0.94 (0.62, 1.41)0.81 (0.49, 1.36)0.78 (0.45, 1.34)0.37Mother's education0.400.0800.057 Primary school completed or less111111 Secondary school0.91 (0.67, 1.25)0.88 (0.63, 1.22)0.79 (0.64, 0.98)0.82 (0.65, 1.02)1.49 (1.07, 2.07)1.45 (1.04, 2.03) Some college education0.66 (0.34, 1.26)0.65 (0.33, 1.26)0.70 (0.47, 1.05)0.66 (0.43, 1.01)1.26 (0.71, 2.23)1.30 (0.73, 2.34)Food insecure household0.94 (0.61, 1.45)0.87 (0.56, 1.36)0.551.10 (0.83, 1.46)1.04 (0.78, 1.39)0.780.91 (0.61, 1.37)0.91 (0.60, 1.38)0.65aUnderweight – defined as weight-for-age (WAZ)<-2).bWasting – defined as weight-for-length (WLZ)<-2.cStunting – defined as length-for-age (LAZ)<-2.dcHR – crude hazard rate.eaHR – hazard ratio adjusted for maternal body mass index (weight in kilograms divided by the square of height in meters), age in years, ART regimen, viral suppression, CD4 count, education, and household food insecurity.fBody mass index is the weight in kilograms divided by the square of height in meters. For every additional year of maternal age, the risk of wasting in infants increased by $4\%$ (aHR 1.04 [$95\%$ CI: 1.01, 1.06]), while the risk of stunting decreased by $2\%$ (aHR 0.98 [$95\%$ CI: 0.96, 1.00]). Infants born to mothers who used NVP-regimens experienced 1.6-fold increased risk of being underweight (aHR 1.62 [$95\%$ CI: 1.09, 2.41]) and 1.34 increased risk of stunting (aHR 1.34 [$95\%$ CI: 1.00, 1.79]) compared to EFV, TDF, 3TC/FTC regimens. Infants born from mothers who have secondary education have a $21\%$ lower risk of stunting (aHR 0.79 [$95\%$ CI: 0.64, 0.98]) but a $45\%$ higher risk of wasting (aHR 1.45 [1.04, 2.03]) (Table 4). Multivariable GEE models used to assess cofactors of growth faltering yielded similar results as Cox proportional hazard regression models above (Supplemental Table S2). ## Statistical analysis Means and standard deviations (SDs) were used to describe normally distributed continuous variables, medians and interquartile ranges (IQRs) to describe skewed distributions, and frequencies and percentages to describe categorical variables. Baseline maternal and infant characteristics were compared between pregnancy-IPT and postpartum-IPT randomisation groups using two-sided t-tests (Mann–Whitney U tests if assumptions were not met) for continuous variables and Pearson χ2 tests (Fisher's exact tests if assumptions were not met) for categorical variables. In the primary study, randomisation was carried out on pregnant women. The randomisation groups were compared in modified intent-to-treat analyses adjusted for predetermined potential confounding variables. For the measurement of adverse birth outcomes, the effects of pregnancy-IPT on LBW and preterm birth were examined in the primary trial publication; however, sex-stratified analyses of these outcomes were not conducted. We examined the effects of pregnancy-IPT on birth outcomes (LBW, preterm birth, and SGA) using generalised linear models with a Poisson family and a log link (to estimate relative risks) in overall and sex-stratified analyses. Multivariable generalised linear models were fitted to control potential confounders. For the measurement of growth faltering during infancy, mothers in the postpartum-IPT arm initiated IPT at 12 weeks after delivery, therefore data were censored at 12-weeks postpartum to examine the effect of pregnancy-IPT compared to no IPT during pregnancy and postpartum. In addition, to compare the longer-term effects of pregnancy-IPT on growth faltering, randomised arms were compared up to 48 weeks after birth. Growth faltering was compared between randomised groups using Cox proportional hazards regression models and generalised estimated equations (GEE). We used Kaplan–Meier survival analysis to compare, unadjusted, time to the first event of growth faltering (underweight, wasting, or stunting) to 12 weeks postpartum and 48 weeks postpartum in overall and sex-stratified analyses. Univariate and multivariable Cox proportional hazards regression models and models including interaction terms between randomisation arm and infants' sex were fit to compare the risk of experiencing the first episode of growth faltering between the randomised groups and any effect modification by infant sex. For these analyses, time zero was the randomisation date, and no failure (no growth faltering) was assumed prior to birth. Since participants were randomly assigned to either a treatment or a control group during pregnancy, whatever difference occurred between the two arms, such as gestational age at birth or low birth weight, was assumed to be attributable to the intervention. Infants lost to follow-up or who died prior to failure were censored at their last visit date. Growth data from visits following growth faltering were censored. We used multivariate Cox proportional hazards regression models to identify cofactors (maternal BMI, age, ART regimen, viral non-suppression, CD4 count (cells/mm3), education, and household food insecurity and infant sex) of growth faltering. As fewer than $5\%$ of at-risk participants remained in the study after 60 post-randomisation weeks, the values at 60th week and after were censored. In multivariable models, we didn't adjust birth characteristics because adjusting for low birth weight, preterm birth, and/or SGA would underestimate the effect of the intervention on the outcome as they are in the causal pathway between pregnancy IPT exposure and long-term growth. Moreover, univariate and multivariable generalised estimated equations (GEE) were fit with a Poisson family and a log link (to estimate relative risk) and exchangeable correlation structure to compare risks of growth faltering (underweight, wasting, and stunting) at any time up until 12 weeks postpartum and up to 48 weeks, as well as testing for interaction by infants' sex and analyses stratified by sex. Infants who experienced growth faltering anytime were not censored in this analysis. The multivariate GEE model was used to identify cofactors of growth faltering in HEU infants. We also fitted multivariable linear regression to examine the long-term impact of IPT on growth (WAZ, LAZ, WLZ) at 48 weeks of infant age. We used R version 4.1.0 for analyses. ## Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. BAR, ASC, and GM had access to the dataset. ASC, SML, GJS, AG, and GM were responsible for the final decision on the submission of this manuscript for publication. ## Maternal and infant baseline characteristics In this study, 898 infants were included: 448 in the pregnancy-IPT and 450 in the postpartum-IPT arm (Fig. 1).Fig. 1Figure shows enrolment, randomisation, post-hoc exclusion criteria, and analysis. This includes the number of pregnant women randomised to randomisation arms, exclusion criteria, and number of infants included in this post-hoc analysis. The median age of mothers at enrolment was 29 years (IQR 24–33), and median BMI was 26.2 (IQR 23.3–29.7). All mothers were on ART and the majority received either efavirenz-tenofovir-lamivudine (EFV, TDF, 3TC, $57.3\%$ ($\frac{480}{850}$)) or efavirenz-tenofovir-emtricitabine (EFV, TDF, FTC, $26.2\%$ ($\frac{222}{850}$)). Almost half ($49.2\%$) of mothers had a CD4 count above 500 cells/mm3, and $37.6\%$ ($\frac{316}{850}$) of mothers had a viral load of >40 HIV RNA copies/ml. Almost half ($\frac{447}{898}$) of the infants were female – 228 in the pregnancy-IPT arm and 219 in the postpartum-IPT arm. Baseline maternal and infant demographic and clinical characteristics were similar between randomised arms (Table 1).Table 1Baseline maternal and infant demographics and clinical characteristics. CharacteristicsRandomized to start IPT duringPregnancy ($$n = 448$$)Postpartum ($$n = 450$$)Maternal characteristics at baselineMedian age (IQR) – years29 (25–33)29 (24–33)Education achievement of mothers – no. (%) Primary school completed or less106 (45.7)126 (54.3) Secondary school306 (51.2)292 (48.8) Some college education36 (52.9)32 (47.1)Median body mass index (IQR)a26.3 (23.5–30.2)26.1 (23.1–2.59)Median CD4 count (IQR)493.5 (360–678.3)497 (356.5–665.5)CD4 count (cells/mm3) <20027 (46.6)31 (53.4) 200-500201 (50.5)197 (49.5) >500218 (49.5)222 (50.5)ART regimen – no. (%) Efavirenz–tenofovir–emtricitabine/lamivudine373 (50.0)373 (50.0) Efavirenz–zidovudine–lamivudine3 (27.3)8 (72.7) Nevirapine–zidovudine or tenofovir–emtricitabine or lamivudine58 (49.2)60 (50.8) Lopinavir or atazanavir–ritonavir with tenofovir or Zidovudine–emtricitable or lamivudine12 (63.2)7 (36.8) Efavirenz only10Median HIV-1 RNA copies/ml (IQR)39 (39–107)39 (39–138)Viral load (≥40 HIV copies/ml) – no. (%) 165 (36.9)173 (38.5)Cotrimoxazole use – no. (%) 198 (44.3)189 (42.1)Positive IGRA status – no./total no. (%) $\frac{127}{442}$ (28.7)$\frac{134}{445}$ (30.1)Mean gestation age at enrollment ± sd26.1 ± 5.325.8 + 5.3Infant characteristics at birthFemale infants – no. (%) 228 (49.1)219 (51.3)Mean birthweight in kilogram (SD)3.0 (0.6)3.0 (0.6)Low birthweight – no./total no. (%) b$\frac{59}{425}$ (13.9)$\frac{40}{433}$ (9.2)Preterm birth – no. (%) c54 (12.1)42 (9.3)Small for gestational age – no./total no. (%) d$\frac{79}{425}$ (18.6)$\frac{86}{433}$ (19.7)aBody mass index is the weight in kilograms divided by the square of height in meters.bLow birth weight is an infant born weighing 5.5 pounds (2.5 kg) or less.cPreterm is a baby born before the 37th week of gestation.dSmall for gestation age is defined as weight less than 10th percentile for gestational age using intergrowth growth standards. ## Maternal and infant TB status throughout the study period As reported in the primary analysis, six mothers ($0.4\%$) and one infant ($0.1\%$) developed TB, and 241 mothers ($32.4\%$) and 41 ($5.8\%$) infants tested positive on a QuantiFERON-TB Gold In-Tube (QGIT) test. There was no significant difference in TB disease or infection incidence in mothers or infants between arms. ## Effect of IPT on WAZ, LAZ, and WLZ at 48 weeks of infants age There was no unadjusted mean difference in WAZ (B 0.04 [$95\%$ CI: −0.15, 0.23], $$p \leq 0.67$$), LAZ (B −0.04 [$95\%$ CI: −0.34, 0.27], $$p \leq 0.81$$), and WLZ (B 0.13 [$95\%$ CI: −0.09, 0.35], $$p \leq 0.24$$) at 48 weeks of infants age between pregnancy-IPT and postpartum-IPT. Similarly, there was no adjusted mean difference in WAZ (B 0.00 [$95\%$ CI: −0.18, 0.18], $$p \leq 0.99$$), LAZ (B −0.10 [$95\%$ CI: −0.41, 0.21], $$p \leq 0.52$$), and WLZ (B 0.11 [$95\%$ CI: −0.10, 0.33], $$p \leq 0.30$$) at 48 weeks of infants age between pregnancy-IPT and postpartum-IPT. ## Effect of pregnancy-IPT on adverse birth outcomes – underweight, preterm, and SGA Overall, $10.7\%$ of infants were premature, $11.5\%$ were LBW, and $19.2\%$ were SGA at birth. Adjusted for relevant cofactors, infants in the pregnancy-IPT arm had a 1.60-fold higher risk of LBW (aRR 1.60 [$95\%$ CI: 1.07, 2.41]) than infants in the postpartum-IPT arm (Table 2). There was no significant difference in risk of being preterm (aRR 1.31 [$95\%$ CI: 0.87, 1.97]) or SGA (aRR 0.97 [$95\%$ CI: 0.71, 1.32]) between randomisation arms. Table 2Risks of low birth weight, preterm, and small for gestational age overall and stratified by infant sex. AnalysisModelIsoniazid started duringLow birthweightcPretermdSmall for gestational ageecRRa ($95\%$ CI)aRRb ($95\%$ CI)P-valuecRRa ($95\%$ CI)aRRb ($95\%$ CI)P-valuecRRa ($95\%$ CI)aRRb ($95\%$ CI)P-valueBirthOverallPregnancy1.50 (1.01, 2.26)1.60 (1.07, 2.41)0.0221.29 (0.86, 1.94)1.31 (0.87, 1.97)0.200.94 (0.69, 1.27)0.96 (0.71, 1.31)0.82Postpartum111111Male infantsPregnancy1.87 (1.08, 3.35)2.04 (1.16, 3.68)0.0151.79 (1.04, 3.15)1.81 (1.04, 3.21)0.0381.12 (0.74, 1.69)1.21 (0.79, 1.84)0.37Postpartum111111Female infantsPregnancy1.18 (0.66, 2.13)1.25 (0.69, 2.27)0.470.87 (0.47, 1.60)0.94 (0.50, 1.75)0.850.75 (0.47, 1.19)0.70 (0.43, 1.12)0.15Postpartum111111acRR – crude relative risk.baRR – relative risk-adjusted for maternal body mass index (weight in kilograms divided by the square of height in meters), age in years, ART regimen, viral suppression, CD4 count, education, and household food insecurity.cLow birth weight is an infant born weighing 5.5 pounds (2.5 kg) or less.dPreterm is a baby born before the 37th week of gestation.eSmall for gestation age is defined as weight less than 10th percentile for gestational age using intergrowth growth standards. In sex-stratified analyses adjusted for all cofactors, male infants in the pregnancy-IPT arm had a 2.04-fold higher risk of LBW (aRR 2.04 [$95\%$ CI: 1.16, 3.68) and 1.81-fold increased risk of preterm birth (aRR 1.81 [$95\%$ CI: 1.04, 3.21]) than those in the postpartum-IPT arm. Pregnancy-IPT was not associated with LBW or preterm delivery among female infants or SGA among male and female infants (Table 2). ## Risk of experiencing growth faltering during infancy The overall risk of being underweight during the 48-week follow-up period was 22.8 per 100 person-years ($95\%$ CI: 19.4, 26.0), risk of stunting was 40.1 per 100 person-years ($95\%$ CI: 35.9, 44.1), and risk of wasting was 32.8 per 100 person-years ($95\%$ CI: 28.9, 36.5). In univariate analysis, male infants in the pregnancy-IPT had a significantly higher cumulative probability of being underweight (30.5 per 100 person-years [$95\%$ CI: 22.02, 38.1] vs 19.7 per 100 person-years [$95\%$ CI: 13.5, 25.5]), $$p \leq 0.041$$) than male infants in the postpartum-IPT arm (Fig. 2a). Fig. 2a–f illustrate Kaplan–Meier curves stratified by sex. Fig. 2Figure includes the Kaplan–Meier curves that show sex-stratified cumulative survival probability of underweight (defined as weight-for-age (WAZ)<-2), wasting (defined as weight-for-length (WLZ)<-2), and stunting (defined as length-for-age (LAZ)<-2) by randomised arms. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( a) includes the Kaplan–Meier curves that show cumulative survival probability of underweight (defined as weight-for-age (WAZ)<-2) by randomised arms in male infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( b) includes the Kaplan–Meier curves that show cumulative survival probability of underweight (defined as weight-for-age (WAZ)<-2) by randomised arms in female infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( c) includes the Kaplan–Meier curves that show cumulative survival probability of stunting (defined as length-for-age (LAZ)<-2) by randomised arms in male infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( d) includes the Kaplan–Meier curves that show cumulative survival probability of stunting (defined as length-for-age (LAZ)<-2) by randomised arms in female infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( e) includes the Kaplan–Meier curves that show cumulative survival probability of wasting (defined as weight-for-length (WLZ)<-2) by randomised arms in male infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. ( f) includes the Kaplan–Meier curves that show cumulative survival probability of wasting (defined as weight-for-length (WLZ)<-2) by randomised arms in female infants. For these Kaplan–Meier, time 0 was the randomisation date during pregnancy. In multivariable Cox regression models, pregnancy-IPT was associated with infant underweight in analyses to 12 weeks and 48 weeks postpartum. Infants in the pregnancy-IPT arm experienced a 1.47-fold higher risk of becoming underweight in the first 12 weeks (aHR 1.47 [$95\%$ CI: 1.06, 2.03]) and a 1.34-fold higher risk of becoming underweight in the first 48 weeks (aHR 1.34 [$95\%$ CI: 1.01, 1.78]) than infants in the postpartum-IPT arm. Maternal IPT timing was not associated with stunting (aHR by 12 weeks 1.12 [$95\%$ CI: 0.91, 1.39] and aHR by 48 weeks 1.08 [$95\%$ CI: 0.89, 1.30]) or wasting (aHR by 12 weeks 1.09 [$95\%$ CI: 0.81, 1.45] and aHR by 48 weeks 1.02 [$95\%$ CI: 0.79, 1.32]) (Table 3).Table 3Risks of growth faltering overall and stratified by infant sex in analyses to 12 and 48 weeks postpartum. UnderweightaStuntingbWastingccHRd ($95\%$ CI)aHRe ($95\%$ CI)P-valuecHRd ($95\%$ CI)aHRe ($95\%$ CI)P-valueaHRd ($95\%$ CI)aHRe ($95\%$ CI)P-value12-week postpartumOverallPregnancy1.37 (0.99, 1.89)1.47 (1.06, 2.03)0.0211.10 (0.89, 1.36)1.12 (0.91, 1.39)0.281.11 (0.83, 1.47)1.09 (0.81, 1.45)0.57Postpartum111111Male infantsPregnancy1.83 (1.18, 2.84)2.02 (1.29, 3.18)0.00221.21 (0.91, 1.62)1.22 (0.91, 1.64)0.191.43 (0.94, 2.19)1.61 (1.04, 2.49)0.031Postpartum1111Female infantsPregnancy0.97 (0.60, 1.56)0.97 (0.59, 1.58)0.890.98 (0.72, 1.34)0.98 (0.71, 1.35)0.900.88 (0.60, 1.29)0.76 (0.51, 1.12)0.17Postpartum11111148 weeks postpartumOverallPregnancy1.26 (0.96, 1.67)1.34 (1.01, 1.78)0.0421.05 (0.86, 1.26)1.08 (0.89, 1.30)0.461.03 (0.80, 1.33)1.02 (0.79, 1.32)0.87Postpartum111111Male infantsPregnancy1.66 (1.13, 2.44)1.82 (1.23, 2.69)0.00271.11 (0.85, 1.45)1.13 (0.86, 1.48)0.381.23 (0.84, 1.79)1.40 (0.95, 2.06)0.091Postpartum111111Female infantsPregnancy0.92 (0.61, 1.40)0.90 (0.59, 1.38)0.630.99 (0.75, 1.30)1.00 (0.75, 1.33)0.990.88 (0.62, 1.25)0.79 (0.55, 1.13)0.20Postpartum111111aUnderweight – defined as weight-for-age (WAZ)<-2).bWasting – defined as weight-for-length (WLZ)<-2.cStunting – defined as length-for-age (LAZ)<-2.dcHR – crude hazard ratio.eHRa – hazard ratio adjusted for maternal body mass index (weight in kilograms divided by the square of height in meters), age in years, ART regimen, viral suppression, CD4 count, education, and household food insecurity. Infant sex significantly modified the effect of pregnancy-IPT on underweight in analyses to 12 weeks (p-value = 0.037) and 48 weeks (p-value = 0.022). Male infants in the pregnancy-IPT arm experienced a 2.02-fold increased risk of becoming underweight in the first 12 weeks (aHR 2.02 [$95\%$ CI: 1.29, 3.18) and a 1.82-fold increased risk of becoming underweight in the first 48 weeks (aHR 1.82 [$95\%$ CI: 1.23, 2.69]) than male infants in the postpartum-IPT arm (Table 3). There was also a statistically significant differential effect of pregnancy-IPT on wasting in male and female infants by 12 weeks (p-value = 0.021), but not by 48 weeks (p-value = 0.057). Male infants in the pregnancy-IPT arm experienced a 1.61-fold higher risk of becoming wasted in the first 12 weeks (aHR 1.61 [$95\%$ CI: 1.04, 2.49), and a 1.43-fold higher risk (nonsignificant) of becoming wasted in the first 48 weeks (aHR 1.40 [$95\%$ CI: 0.95, 2.06) than male infants in the postpartum-IPT arm (Table 3). Among female infants, pregnancy-IPT was not associated with growth faltering – underweight (aHR at 12 weeks 0.97 [$95\%$ CI: 0.59, 1.58] and aHR at 48 weeks 0.90 ($95\%$ CI: 0.59, 1.38]), stunting (aHR at 12 weeks 0.98 [$95\%$ CI: 0.71, 1.35] and aHR at 48 weeks 1.00 [0.75, 1.33), and wasting (aHR at 12 weeks 0.76 [$95\%$ CI: 0.51, 1.12] and aHR at 48 weeks 0.79 [$95\%$ CI: 0.55, 1.13]). We also fitted GEE multivariable models to estimate the repeated prevalence of growth faltering during infancy which yielded similar results to Cox regression analyses (Supplemental Table S1). ## Discussion In this post-hoc analysis of a multi-site RCT evaluating maternal IPT in pregnancy versus postpartum, timing of maternal IPT influenced growth outcomes among HEU infants with a significantly higher risk of underweight among infants born to mothers in the pregnancy-IPT arm. There was an effect modification of associations of IPT with growth by infant sex, with significantly increased underweight and wasting in males born to mothers in the pregnancy-IPT versus postpartum-IPT arms. Our findings suggest that the timing of maternal IPT may influence birth size as well as postnatal growth in infants and provide valuable data for policymakers and clinicians considering the optimal timing of IPT in pregnant WLWH. Our data suggest growth-altering effects of in utero IPT exposure. Infants born to mothers randomised to pregnancy-IPT had a 1.60-fold higher risk of LBW and of becoming underweight during the first year of life than infants born to mothers randomised to postpartum-IPT. During pregnancy, IPT causes embryocidal effects on rats and rabbits, delays neurodevelopment in zebrafish, and affects postnatal growth, development, and cognitive ability in rats.16, 17, 18 In vitro studies have demonstrated cytotoxic effects of IPT that disturb the cell cycle in mammalian cells.19 Antenatal IPT may induce poor appetite, nausea, emesis, and hepatic changes in the mother that could, in turn, affect infant growth.3,20 In addition, since isoniazid crosses placenta barrier,16 direct drug effects on the Foetus could influence growth. We found that associations between pregnancy-IPT and growth were modified by infant sex. Male infants in the pregnancy-IPT arm had a significantly higher risk of preterm birth, LBW, and longer-term growth faltering than male infants in the postpartum-IPT arm. In contrast, birth outcomes and growth in female infants were not affected by pregnancy-IPT. In utero growth trajectories differ by sex; male foetuses typically grow faster and may not alter their growth trajectory when facing adverse challenges.21, 22, 23, 24, 25 Due to the continued growth without adaptations early in pregnancy, male Foetus exposed to adverse intrauterine exposure (maternal/ecological/environmental challenges) may have adverse outcomes later in pregnancy,22,23 including growth restriction,26,27 than female infants. Placental gene transcription differences,23,28 prenatal testosterone exposure,29 differences in cellular genes (XX-specific versus XY-specific),22,23,28,29 and maternal glucocorticoids23 influence sex-differential adaptation response to intrauterine exposure. These factors may contribute to sex-differential growth effects of in utero IPT. Our findings also suggest that in utero IPT continues to affect male growth after birth. Similarly, mixed-twin studies show male susceptibility: male twins are more likely to experience congenital anomalies and a higher risk of infant mortality and neonatal morbidity than their female twins.30 Pregnancy-IPT was not associated with overall or sex-stratified SGA. The fact that male infants in pregnancy-IPT had a 1.81 higher risk of preterm birth while there was no difference in SGA risk than male infants in postpartum-IPT suggests that the mechanism for LBW in male infants may be through preterm birth. In addition to our primary goal of defining the impact of pregnancy IPT exposure on infant growth, we assessed other cofactors of growth in HEU infants. We found expected associations between maternal BMI and being underweight. Nutritional status during pregnancy affects the Foetus's nutrition, potentially altering its growth. We also found that stunting risk decreased by $2\%$ for every additional year of maternal age. This was consistent with a study using data from 18 countries' Demographic Health Surveys, which found that infants of young mothers had lower heights than infants born to older mothers.31 Infants of young mothers may be more prone to intrauterine growth restriction.21 The maternal use of nevirapine regimens was associated with growth faltering; potential mechanisms for this association are unclear. This study has multiple strengths, including randomised allocation of IPT timing, excellent retention, and sufficient sample size to investigate growth outcomes. The study also has limitations. This post-hoc analysis was designed after the primary RCT was completed. After 12 weeks of age, infants in the deferred arm received postpartum IPT, so there is not a no-IPT comparator after this time point. However, in analyses that excluded the time period with exposure to postpartum IPT, growth effects of pregnancy-IPT remained similar. Because of the lack of information about infants' growth during pregnancy, we assumed no failure (no growth faltering) prior to birth, and birth was assumed as time-one, which could be a limitation. Infants with compromised growth before delivery would have been censored before birth if we had this prior birth information. As a result, it is possible that some infants might have switched categories before birth as a result - from compromised growth to normal growth and vice versa. However, we strongly believe that the design of our study has addressed/minimised the impact of this on the results of our study. Given that randomisation was applied, any difference between the two arms is attributed to IPT exposure during pregnancy. Similarly, the double-blind placebo-controlled randomised controlled trial design has addressed/minimised potential bias and measurement errors. Randomization has addressed selection bias. To reduce measurement error, data collectors were trained in how to measure height and weight in infants. As it was a double-blinded and placebo-controlled design, even if measurement error occurred, there would have been non-differential misclassification, which would attenuate the effect of the exposure on the outcome. Therefore, the design allows us to ensure that our findings are not biased or influenced by measurement errors (if anything, they are attenuated). And, randomisation was balanced by study site to account for heterogeneity. Breastfeeding is crucial for infant development and growth. We did not include breastfeeding-related variables in our models for the following reasons: 1) to avoid adjusting a potential mediator: Preterm birth and low birth weight are hypothesised pathways to long-term growth effects, and both affect breastfeeding negatively, which make breastfeeding part of the potential causal pathway. As a result, adjusting breastfeeding variables would attenuate intervention effect. 2) Differential censorship: Randomisation date during pregnancy was time zero, and breastfeeding is a time-varying variable that can only be collected after birth. The association between breastfeeding and growth would exclude infants with restricted growth at birth due to censoring in the survival analysis design. 3) Left truncation bias: If adverse birth outcomes affect breastfeeding practice and a large number of infants with adverse birth outcomes are censored at birth (time 1), including breastfeeding in our models to assess its association with growth over each follow-up period would introduce bias. We would be able to fit breastfeeding variables only to infants who survived time one (infants without adverse birth outcomes). For these reasons adding breastfeeding variables to our models would be problematic." As a result of these reasons, and the fact that only a few infants had concurrent infections at each follow-up (on average less than 4 infants had pneumonia), we did not include infection status in the analysis. Other important factors that affect the growth of infants during pregnancy are the drinking and smoking experiences of mothers. Due to the very small number of mothers smoking at enrolment (<$2\%$), we did not include maternal smoking experience as a cofactor in the model. Mothers' drinking experience at enrolment or after was not collected. Nonetheless, the results of the study regarding the effects of IPT on growth would not have been affected by these variables since randomisation would distribute them evenly between the randomisation arms. In conclusion, in this post-hoc analysis, maternal IPT during pregnancy was associated with a significantly increased risk of LBW and risk of becoming underweight among HEU infants. Male infants exposed to pregnancy-IPT had a significant risk of LBW, preterm birth, and longer-term risk of being underweight that persisted over the first year of life. These data add to prior TB APPRISE findings of increased risk of composite adverse birth outcomes associated with IPT during pregnancy, suggesting IPT during pregnancy also impacts birth size and infant growth specifically among male infants. These data could inform monitoring and management and warrants further examination of potential mechanisms. ## Contributors ACH participated in the investigation of the parent study and reviewed and edited this manuscript. AG designed and analysed the parent study. Reviewed and edited this manuscript. ASC∗ participated in all activities of this post-hoc analysis: data management, analysis, writing and editing the manuscript. AW participated in the investigation of the parent study and reviewed and edited this manuscript. BAR∗ participated in all activities of this post-hoc analysis: data management, analysis, writing and editing the manuscript. BTM participated in the investigation of the parent study and reviewed and edited this manuscript. DAE participated in all activities of this post-hoc analysis: data management, analysis, writing and editing the manuscript. DW participated in the investigation and validation of the parent study and reviewed and edited this manuscript. EJ participated in the investigation and validation of the parent study and reviewed and edited this manuscript. GM participated in the investigation of the parent study and reviewed and edited this manuscript. GM∗ designed/analysed parent study, data curation, and methodology and reviewed & edited the manuscript. GT participated in the investigation of the parent study and reviewed and edited this manuscript. GJS participated in all activities of this post-hoc analysis: data management analysis, writing and editing the manuscript. HC participated in the investigation of the parent study and reviewed and edited this manuscript. LA participated in the investigation of the parent study and reviewed and edited this manuscript. LSC participated in the investigation of the parent study and reviewed and edited this manuscript MSR participated in the investigation and validation of the parent study and reviewed and edited this manuscript. NC participated in the investigation of the parent study and reviewed and edited this manuscript. PJP participated in the investigation of the parent study and reviewed and edited this manuscript. SB participated in protocol development and study implementation, and operational support. SML participated in all activities of this post-hoc analysis: data management, analysis, writing and editing manuscript. TN participated in the investigation and validation of the parent study and reviewed and edited this manuscript. TM participated in the investigation and validation of the parent study and reviewed and edited this manuscript. ∗ASC, BAR, and GM have accessed and verified the underlying data. ## Data sharing statement Due to ethical restrictions in the informed consent documents and in the approved human subjects protection plan of the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) Network, the study's data cannot be made publicly available; public availability could compromise participant privacy. However, data are available to all interested researchers upon request to the IMPAACT Statistical and Data Management Center's data access committee (email address: sdac.data@fstrf.org) with the agreement of the IMPAACT Network. ## Declaration of interests AW declares grants from GSK, Merck, and Janssen; payment for expert testimony from GSK and Merck; and participation on a data safety monitoring board for GSK, Merck, and Seqirus. All other authors declare no competing interests. ## Supplementary data Supplementary Tables S1 and S2 IMPAACT P1078 TB APPRISE Study Team ## References 1. Gupta A., Bhosale R., Kinikar A.. Maternal tuberculosis: a risk factor for mother-to-child transmission of human immunodeficiency virus. J Infect Dis (2011) 203 358. PMID: 21208928 2. Marais B.J., Schaaf H.S.. Tuberculosis in children. Cold Spring Harb Perspect Med (2014) 4 3. Yuen M., Keshavjee S., Seddon J.A.. 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--- title: Effects of Helicobacter pylori eradication on esophageal motility, esophageal acid exposure, and gastroesophageal reflux disease symptoms authors: - Tong Zhao - Fang Liu - Yongjun Li journal: Frontiers in Cellular and Infection Microbiology year: 2023 pmcid: PMC10031050 doi: 10.3389/fcimb.2023.1082620 license: CC BY 4.0 --- # Effects of Helicobacter pylori eradication on esophageal motility, esophageal acid exposure, and gastroesophageal reflux disease symptoms ## Abstract ### Background The effects of Helicobacter pylori (HP) eradication on gastroesophageal reflux disease (GERD) are yet to be fully elucidated. Few studies have investigated the mechanisms underlying the correlations between HP and GERD with prospective methods. The objective of this prospective clinical study was to explore the effects of HP eradication on GERD. ### Methods Patients diagnosed with both GERD and HP were included. High-resolution esophageal manometry (HRM), 24-h esophageal pH monitoring, and the Gastroesophageal Reflux Disease Questionnaire (GerdQ) were performed before and after the successful eradication of HP, and the data were compared using statistical analysis. ### Results Sixty-eight patients diagnosed with both GERD and HP were included. The After HP eradication group showed significantly decreased median distal contractile integral (DCI) [610.40 (847.45) vs. 444.90 (559.60)] and significantly increased median inefficient esophageal motility (IEM) [36.00 (50.00) vs. 60.00 (57.00)] in the HRM compared with those of the Before HP eradication group, indicating that HP eradication reduced esophageal peristalsis. The 24-h esophageal pH monitoring showed that the longest reflux event, the percentage of time that the pH was <4, the number of reflux episodes, and the DeMeester score were all significantly different between the Before and After HP eradication groups ($P \leq 0.05$), suggesting that HP eradication increased esophageal acid exposure. The After HP eradication group also had a significantly higher GerdQ score than that of the Before HP eradication group ($P \leq 0.05$). ### Conclusions HP eradication reduced esophageal peristalsis, enhanced esophageal acid exposure, and aggravated GERD symptoms, suggesting that HP infection may be a protective factor for GERD. ## Introduction Gastroesophageal reflux disease (GERD) is a prevalent upper digestive tract disease, which primarily leads to acid reflux, dysphagia, heartburn, asthma, cough, and chest pain due to reflux of the contents from the stomach and duodenum into the esophagus (Vakil et al., 2006; Ashktorab et al., 2012; Maret-Ouda et al., 2020). GERD affects approximately $20\%$ of the adult population in high-income countries (Maret-Ouda et al., 2020). The estimated prevalence of GERD is $13.3\%$ of the population worldwide. There is a high incidence rate of $19.55\%$ in North America, and a rising trend is also observed in the Asia-Pacific region (Fock et al., 2016; Eusebi et al., 2018). According to an epidemiological survey in 2020, the prevalence of GERD in China was $4.16\%$ (Nirwan et al., 2020). The risk factors for GERD include female gender, smoking, genetic predisposition, nonsteroidal anti-inflammatory drug (NSAID) and aspirin use, and obesity (Eusebi et al., 2018; Maret-Ouda et al., 2020). Although GERD can be diagnosed based on the empirical proton pump inhibitor (PPI) therapy test and the presence of typical symptoms, additional diagnostic evaluation, such as ambulatory pH monitoring, high-resolution esophageal manometry (HRM), and digestive endoscopy, may also be required most of the time (Katz et al., 2013). The anti-reflux barrier of the esophagus includes the angle of His, the lower esophageal sphincter (LES), and the muscular fibers of the diaphragm. In most cases, there is a balance between the erosive effects of the reflux on the esophageal mucosa and the anti-reflux barrier of the esophagus. An impaired anti-reflux barrier and a weakened esophageal clearance function contribute to the occurrence of GERD (Satta et al., 2017). Helicobacter pylori (HP) is the primary gastroduodenal pathogen related to the pathogenesis of gastritis, gastric carcinoma, and gastroduodenal ulcer. Early HP eradication reduces the occurrence of gastroduodenal ulcer and carcinoma. However, there is no consensus on the effects of HP eradication on GERD, and the mechanisms are still not entirely known. There are no unified standards for the application of anti-HP therapy in GERD patients. Several complications may occur after HP eradication, including GERD (Hojo et al., 2021). Some researchers have claimed a negative correlation between HP eradication and GERD or its typical symptoms (Moayyedi et al., 2001; Ashktorab et al., 2012) due to impaired gastric acid secretion. However, eradication of HP is recommended in the guidelines of the Italian Society of Gastroenterology and guidelines of Japan (Kato et al., 2019; Romano et al., 2022). Other explanations show that HP eradication has a beneficial effect on GERD (Miwa et al., 2002). A study in Japan showed an improvement of GERD symptom-related quality of life after HP eradication (Hirata et al., 2013). Additionally, some evidence suggested no connection between HP and GERD (Qian et al., 2011; Bor et al., 2017). Studies in different geographic regions may lead to entirely different outcomes. The results of HP eradication also depend on the form of gastritis in the patients with GERD (Yucel, 2019). Thus, the management of HP eradication in patients with GERD is controversial. HRM measures the pressure from the pharynx to the stomach and therefore was used for the diagnosis of functional esophageal diseases in the 1990s (Pandolfino et al., 2009). HRM can also be used for localization of the LES, measurement of the esophageal pressure, accurate placement of the ambulatory pH monitoring catheters, and detection of the esophageal motor function before anti-reflux surgery (Gyawali et al., 2018; Patel et al., 2018). The 24-h esophageal pH (24-h pH) monitoring is a dynamic assessment of gastroesophageal reflux that allows an objective evaluation of acid reflux events and association with symptoms. The Gastroesophageal Reflux Disease Questionnaire (GerdQ) is a self-administered 6-item questionnaire to evaluate symptoms. In this study, we collected and analyzed the results of HRM, 24-h pH, and endoscopic examination, as well as the GerdQ of patients diagnosed with both GERD and HP. We investigated the underlying mechanisms in order to elucidate the effects of HP eradication on GERD. ## Patients A total of 234 patients who were diagnosed with both GERD and HP at the First Affiliated Hospital of Shihezi University between July 2021 and July 2022 were included in the study. The inclusion criteria were as follows: 1) diagnosed with GERD by both endoscopy and PPI test; 2) also diagnosed with HP infection by biopsy examination or 14C-urea breath test (14C-UBT); 3) willingness to undergo HP eradication therapy and HRM and 24-h pH; and 4) aged from 18 to 80 years. Patients were excluded if they had hiatal hernia, underwent gastric or esophageal surgery, or consumed food that could have affected the gastrointestinal motor function or acid reflux before the study. Patients with a history of acid secretion inhibitor and gastrointestinal motility drug usage in the 1 week prior to the study were also excluded. All patients signed an informed consent, and the study was approved by the local ethics committee. All participants were treated with PPI-based quadruple therapy (colloidal bismuth pectin, 600 mg, thrice daily; omeprazole, 40 mg, once daily; amoxycillin, 100 mg, twice daily; clarithromycin, 50 mg, twice daily) for 14 days. One month after the PPI-based quadruple therapy, patients underwent the 14C-UBT. After successful HP eradication, all patients performed a second GerdQ, HRM, and 24-h pH. ## Helicobacter pylori test During upper gastrointestinal endoscopy, the corpus and antrum tissue specimens were obtained and fixed in formalin. Biopsy specimens were subjected to the rapid urease test (RUT). HP infection was also determined by the 14C-UBT (Thor and Błaut, 2006). Being positive in either the RUT or the 14C-UBT suggested HP infection. ## Upper gastrointestinal endoscopy The endoscopic images of the reflux esophagitis (RE) were categorized based on the confluence of erosion and the longest length of the mucosal break according to the Los Angeles classification. RE was graded from A (the lowest severity) to D (the highest severity). GERD also included Barrett’s esophagus and non-erosive reflux disease (NERD). Patients having the symptoms without endoscopic esophageal abnormalities were considered to have NERD, which can be evaluated by the functional esophageal test. All patients underwent upper gastrointestinal endoscopy. The endoscopic findings were judged by two experienced endoscopists separately. When the judgments were different, the final conclusions were unified by discussion. ## Gastroesophageal reflux disease questionnaire (GerdQ) The GerdQ is used to assess reflux-related symptoms in the gastroenterology clinics. It covers six reflux-related symptoms, namely, heartburn, acid regurgitation, epigastric soreness, nausea, dyssomnia, and whether taking over-the-counter drugs. The GerdQ has a high diagnostic value for GERD (Bai et al., 2013). The GerdQ includes six questions and is shown in Figure 1. Patients with a total GerdQ score of over 8 have a higher possibility of developing GERD than patients with a score of 8 or below. The GerdQ was administered to all recruited patients by a research assistant prior to endoscopy. Figure 1: GerdQ score. ## High resolution esophageal manometry (HRM) HRM is commonly considered the gold standard for detecting motility disorders and anatomic associations at the esophagogastric junction. It shows the characteristics of the resting esophageal sphincter and the esophageal motor function during swallowing. The Chicago Classification (CC) is a classification scheme that allows the diagnosis of GERD based on manometry. CC v4.0 is the updated version (Fox et al., 2021; Yadlapati et al., 2021). The main metrics in the HRM are the integrated relaxation pressure (IRP), which measures deglutitive relaxation across the LES, and distal contractile integral (DCI), which detects the comprehensive value of length, pressure, and duration of esophageal contraction to evaluate the strength of esophageal body contraction. In CC v4.0, inefficient esophageal motility (IEM) is defined as normal IRP with $50\%$ failed peristalsis or more than $70\%$ ineffective swallows. Major peristalsis breaks (PBs) are defined as PBs longer than 5 cm for measuring with or without esophageal PBs (Figure 2). Figure 2: High resolution esophageal manometry (HRM) images. ## 24-h esophageal pH monitoring Ambulatory pH monitoring, which provides objective measures of acid reflux events and symptoms, is used to diagnose the reflux of esophageal gastric acid. All participants followed a restricted diet and consumed no medication or food that might affect the results. The parameters assessed during the 24-h pH monitoring included the number of reflux episodes, the number of reflux events >5 min, the acid exposure time percentile (AET%), the longest reflux event, and the DeMeester score. The AET% was defined as the percentage of the total time that the pH was <4, and when AET% >$6\%$, the data were considered abnormal. A DeMeester score >14.72 was considered to indicate pathologic acid reflux (Gyawali et al., 2020). The 24-h pH showed that the longest reflux event, esophageal acid exposure time (AET%), number of reflux episodes, and DeMeester score were statistically significant between the Before and After HP eradication groups ($P \leq 0.05$) (Table 4). Our results showed that HP eradication increased the esophageal acid exposure and reflux and damaged the anti-reflux barrier. HP seems to have a protective role in GERD patients. Table 4 \| Unnamed: 0 \| Before HP eradication \| After HP eradication \| P \| \| --- \| --- \| --- \| --- \| \| AET (%) \| 23.64 ± 18.49 \| 37.35 ± 26.89 \| 0.008 \| \| Number of reflux episodes \| 113.97 ± 84.87 \| 203.00 (64.00) \| 0.008 \| \| DeMeester score \| 49.00 (141.90) \| 71.40 (233.85) \| 0.008 \| \| Reflux events >5 min \| 27.00 (33.00) \| 45.00 (110.00) \| 0.085 \| \| Longest reflux event \| 6.00 (12.50) \| 17.78 ± 11.67 \| 0.011 \| ## Statistical analysis The SPSS software (version 22; SPSS Inc., Chicago, IL, USA) was used for data analysis. Continuous variables [age, body mass index (BMI), HRM parameters, 24-h pH monitoring parameters, and GerdQ scores] were expressed as mean ± standard deviation (mean ± SD) when data followed a normal distribution. When not obeying a normal distribution, they were expressed as median (quartile) or median [interquartile range (IQR)]. Categorical variables (gender, smoking, and drinking status) were presented as numbers and percentages. The differences between before and after HP eradication were calculated. When differences between paired data followed a normal distribution, a paired t-test was performed. When differences between paired data followed a skewed distribution, a nonparametric Wilcoxon rank sum test was performed to compare them. A P-value <0.05 was considered significant. To analyze the risk factors, univariate conditional logistic regression models were first used. We calculated $95\%$ confidence intervals (CIs) and odds ratios (ORs) to evaluate the strengths of the correlations. Clinically plausible variables identified in the univariate analysis were included in a multivariable conditional logistic regression model in a stepwise selection manner if $P \leq 0.05.$ ## Basic characteristics of patients Sixty-eight patients who underwent successful HP eradication therapy and completed the esophageal function examination before and after the therapy were recruited (Figure 3). There were 18 patients with RE, 129 patients with NERD, and one patient with Barrett’s esophagus. There were 46 men and 22 women (mean age = 52.06 years) (Table 1). The risk factors for GERD were identified by univariate and multivariate analyses. Results showed that three potential risk factors were associated with GERD, namely, high BMI and smoking and drinking habits (Table 2). Figure 3: Recruitment flowchart. A total of 234 patients were screened, and 166 were excluded due to 1) taking H2-receptor antagonists or PPIs ($$n = 45$$), 2) not undergoing upper GI endoscopy ($$n = 41$$), 3) unsuccessful first HRM and 24-h pH monitoring ($$n = 29$$), 4) unsuccessful HP eradication ($$n = 32$$), and unsuccessful second HRM and 24-h pH monitoring ($$n = 19$$). TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 ## HRM parameters Normal and abnormal HRM images are shown in Figure 4. The After HP eradication group showed a significantly decreased median DCI [610.40 (847.45) vs. 444.90 (559.60)] and a significantly increased median IEM [36.00 (50.00) vs. 60.00 (57.00)] in the HRM compared to those of the Before HP eradication group ($P \leq 0.05$), indicating that HP eradication reduced esophageal peristalsis. There was no significant difference in the IRP, LES pressure, and PB >5 cm rate between the two groups, indicating that HP eradication cannot change the LES pressure and large PBs (Table 3). Figure 4: Highresolution esophageal manometry (HRM) images. (A) Normal HRM image. (B–E) Abnormal HRM. TABLE_PLACEHOLDER:Table 3 ## GerdQ score The GerdQ score of the After HP eradication group was significantly lower than that of the Before HP eradication group ($P \leq 0.05$), suggesting that patients after HP eradication had more severe symptoms including heartburn, acid regurgitation, epigastric soreness, nausea, dyssomnia, and whether taking over-the-counter drugs (Table 5). Table 5 \| Unnamed: 0 \| Before HP eradication \| After HP eradication \| P \| \| --- \| --- \| --- \| --- \| \| GerdQ score \| 11.00 (2.00) \| 12.00 (3.00) \| 0.03 \| ## Discussion The role of HP eradication in GERD pathogenesis remains controversial, and the mechanisms are not yet fully understood. There are no unified criteria for the application of anti-HP therapy in GERD patients. Some researchers have claimed that HP eradication leads to GERD (Xie et al., 2013; Hojo et al., 2021), while another explanation has shown that HP eradication has a beneficial effect on GERD (Hirata et al., 2013). Additionally, some have suggested no correlation between HP and GERD (Bor et al., 2017). There are several possible mechanisms of HP eradication leading to GERD. One of the mechanisms is that HP infection increases the acid reflux owing to the disappearance of neutralization of bacterial ammonia (Arents et al., 2001; Queiroz et al., 2004). One hypothesis for the protective mechanism is that HP infection results in atrophy of the gastric mucosa and damage of acid production (Hirata et al., 2013). The nitric oxide synthase (NOS) regulation system is a modulator of the inflammatory reaction in the gastric mucosa of HP that can induce NO release to inhibit gastric acid secretion (Slomiany and Slomiany, 2011). Another possible explanation is that HP infection makes the vagus nerve receptor on the gastric fundus and cardia active, which enhances the secretion of serum gastrin and increases the LES pressure, reduces the reflux of gastric contents, and protects the esophageal mucosa (Thor and Błaut, 2006). Some researchers have claimed that the protective mechanism of HP is regarded as its negative impact on ghrelin and gastric acid production, and stomach ghrelin stimulates appetite, leading to obesity, which is a widely known risk factor in the development of GERD (Goll et al., 2007; Rubenstein et al., 2013). Others have hypothesized that HP eradication has a beneficial effect on GERD. In some patients with HP, the organism colonizes the antrum preferentially, resulting in an antrum-dominant gastritis characterized by aggravated GERD symptoms and increased gastrin and acid secretion. HP eradication reduced acid secretion (El-Omar et al., 1995; Vicari et al., 1998; Zullo et al., 2013). In patients with GERD on PPI therapy, eradication of HP is recommended in the guidelines of the Italian Society of Gastroenterology and guidelines of Japan (Kato et al., 2019; Romano et al., 2022). However, the guidelines of the American College of Gastroenterology (ACG) in 2013 indicated that screening for HP infection is not recommended in GERD patients, and treatment of HP infection is not routinely required as a part of the anti-reflux therapy (Katz et al., 2013). This recommendation was not mentioned in the ACG 2022 guidelines (Katz et al., 2022).Our results showed that HP eradication aggravated GERD. However, early HP eradication reduces the occurrence of gastroduodenal ulcer and carcinoma. Thus, HP eradication should proceed with uncertainty. IEM is a highly etiologically diagnosed type of esophageal dynamic disorder (Gyawali et al., 2019). DCI is used to evaluate the strength of esophageal body contraction. In this study, patients after HP eradication showed significantly increased IEM and reduced DCI, indicating that HP eradication reduced esophageal peristalsis. A majority of parameters measured in the 24-h pH monitoring in patients after HP eradication were significantly higher than those in patients before HP eradication ($P \leq 0.05$), suggesting that patients treated with anti-HP therapy had higher acid exposure in the esophagus. Our results support the hypothesis that HP eradication increases gastric acid production and reflux. However, our data do not support the viewpoint that HP eradication changes LES pressure. There was no statistical significance in reflux events >5 min between the two groups possibly owing to the small sample size. The GerdQ score also increased in the second examination, indicating that patients showed more severe symptoms after HP eradication, further showing a negative correlation between HP eradication and GERD symptoms. The results of GERD after HP eradication are most likely to depend on the form of gastritis (antrum-predominant active or corpus-predominant active). In western countries, antrum-predominant gastritis is the most common type in patients with GERD and prevalent in children and young adults. In Asia, corpus-predominant and atrophic gastritis is more frequent, and it appears that patients with HP infection have impaired acid secretion. After HP eradication, a repaired corpus mucosa and the recovery of acid secretion may promote the development of GERD (Haruma, 2004; Naylor et al., 2006). Bacterial virulence is important in determining acid secretion. The cytotoxin-associated gene (Cag) protein can inhibit cytokine production such as interleukin 1, which probably reduces gastric acid. In addition, the vacuolating cytotoxin A (VacA), especially the s1m1, reduces gastric acid secretion by damaging the gastric parietal cells, which may be a protective mechanism against GERD (Yucel, 2019). According to previous studies, CagA-positive HP strains may play a protective role in the development of GERD, especially in East Asian countries (Azuma et al., 2004; Ashktorab et al., 2012; Chiba et al., 2012). It has been reported that no association was detected between CagE HP strains and GERD (Godoy et al., 2003). An Iranian study showed that there was no difference between GERD patients and controls in the prevalence of HP, but the presence of the CagA strains and the coexistence of CagA and CagE strains were higher in the control group (Shavalipour et al., 2017). In this study, one of the limitations is the insufficient sample size. Many patients refused to undergo the functional examinations due to nausea caused by catheterization. Patients underwent unsuccessful HP eradication owing to the failure to adhere to the medication regimen. Other limitations are the lack of information on the virulence of the clinical strains responsible for infection, the composition of the microbiota, and the type of the different patients enrolled. The strengths of this study are as follows: 1) Instead of performing a retrospective study, we prospectively collected and compared the results of the same cohort before and after HP eradication. 2) Both the 24-h pH monitoring and HRM were performed, aiming to investigate not only the correlation between HP and GERD but also the underlying mechanisms with evidence. HP infection affected the reflux of acid. Our data indicated that it also affected esophageal motility, which should be further investigated. ## Conclusion This study showed that HP eradication therapy increased esophageal acid production and reflux, reduced esophageal peristalsis, and aggravated GERD symptoms in patients diagnosed with both HP infection and GERD, suggesting the protective role of HP in GERD. These findings may have implications for whether HP eradication should be used in clinical practice. More investigations are required to further explore the effects of HP on GERD patients. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics committee of first Affiliated Hospital, Shihezi University School of Medicine (protocol no. KJX-2021-051-02). The patients/participants provided their written informed consent to participate in this study. Written informed consent has been obtained from the patients to publish this paper. ## Author contributions TZ made a substantial contribution to the concept or design of the work; TZ and FL made a contribution to acquisition, analysis and interpretation of data; TZ drafted the article; YL revised the article critically for important intellectual content. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: A bi-directional Mendelian randomization study of sarcopenia-related traits and type 2 diabetes mellitus authors: - Simin Chen - Shikang Yan - Nuerbiyamu Aiheti - Kaidiriyan Kuribanjiang - Xuemei Yao - Qian Wang - Tao Zhou - Lei Yang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031051 doi: 10.3389/fendo.2023.1109800 license: CC BY 4.0 --- # A bi-directional Mendelian randomization study of sarcopenia-related traits and type 2 diabetes mellitus ## Abstract ### Background Previous studies have reported an association between sarcopenia and type 2 diabetes mellitus (T2DM), but causation was prone to confounding factors. A more robust research approach is urgently required to investigate the causal relationship between sarcopenia and T2DM. ### Methods The bi-directional two-sample MR study was carried out in two stages: Sarcopenia-related traits were investigated as exposure while T2DM was investigated as an outcome in the first step, whereas the second step was reversed. The GWAS summary data for hand-grip strength ($$n = 256$$,523), appendicular lean mass (ALM, $$n = 450$$,243), and walking pace ($$n = 459$$,915) were obtained from the UK Biobank. T2DM data were obtained from one of the biggest case-control studies on diabetes (DIAGRAM; $$n = 180$$,834 cases and 492,191 controls), which was published in 2022. The inverse-variance weighted (IVW) approach was used to obtain MR estimates, and various sensitivity analysis was also performed. ### Results Low hand-grip strength had a potential causal relationship with an increased incidence of T2DM (OR = 1.109; $95\%$ CI, 1.008–1.222; $$p \leq 0.0350$$). T2DM risk was reduced by increasing ALM and walking pace: A 1 kg/m2 increase in ALM decreased the risk of T2DM by $10.2\%$ (OR = 0.898; $95\%$ CI, 0.830–0.952; $p \leq 0.001$). A 1 m/s increase in walking pace decreased the risk of T2DM by $90.0\%$ (OR = 0.100; $95\%$ CI, 0.053–0.186; $p \leq 0.001$). The relationship was bidirectional, with T2DM as a causative factor of sarcopenia-related traits ($p \leq 0.05$) except for ALM (β = 0.018; $95\%$ CI, −0.008 to −0.044; $$p \leq 0.168$$). ### Conclusions Hand-grip strength and T2DM had a potential bidirectional causal relationship, as did walking pace and T2DM. We suggest that sarcopenia and T2DM may mutually have a significant causal effect on each other. ## Introduction With the worldwide population aging, the incidence of sarcopenia and type 2 diabetes mellitus (T2DM) is rapidly increasing, which is related to a worse standard of living and higher mortality [1, 2]. They have contributed to a serious global health problem, putting a heavy medical and economic burden on society [3]. Sarcopenia and TDM are both age-related diseases with the same underlying pathophysiological mechanism [4, 5]. People with sarcopenia have impaired glucose tolerance, and elevated blood insulin levels, and are at increased risk of T2DM. T2DM is characterized by insulin resistance and muscular metabolism, which may function as an accelerator for sarcopenia. Based on observational research, significant lines of evidence quantitatively revealed a tight association between sarcopenia and T2DM. The prevalence of sarcopenia among the elderly with T2DM ranges from 15.7–29.3 percent [6, 7]. A cohort study of 6895 adults with an average age of 52 years reported that people with a muscle mass index in the lowest third of the population were twice as likely to get T2DM as those in the control group [8]. These results make it difficult for researchers to determine which condition causes the other, particularly when confounders such as age, gender, and lifestyle are present [5]. Assessing the causal relationship between sarcopenia and T2DM may help in the development of new techniques for the prevention, diagnosis, and treatment of T2DM. Mendelian randomization (MR) investigates the causal effects associated with exposure and outcome by using genetic variants as instrumental variables (IVs), which avoids confounding variables and reverse causation [9, 10]. To the authors’ knowledge, no MR between sarcopenia and T2DM has been studied. Moreover, no randomized controlled study (RCT) specifically evaluated the bi-directional relationship. Therefore, we used a bi-directional two-sample MR analysis to investigate the links between sarcopenia (measured as hand-grip strength, ALM, and walking pace) and T2DM. ## Study design In this study, we adopted a bi-directional MR study design, employing two-Sample MR methodologies and different GWAS summary level data sets to explain the causation and causative direction between the sarcopenia-related traits (hand-grip strength, appendicular lean mass, walking pace) and the risk of T2DM and glycemic traits (fasting insulin, fasting blood glucose, glycated hemoglobin, and two-hour blood glucose challenge) in a European population. This study consisted of two stages. In first, we investigated whether sarcopenia-related traits were causally related to T2DM and glycemic characteristics. In the second, we evaluated whether genetic T2DM and glycemic characteristics were linked to sarcopenia-related traits. ( Figure 1B). Figure 1: Schematic diagram of the Bi-directional MR Analysis. (A) illustrates three assumptions of MR analysis as follows: 1) the IVs have a significant correlation with the exposure; 2) the IVs also had no pleiotropic correlations with any known confounding factors; and 3) the IVs have no connection with the outcome, with the possible exception of how that link is mediated by their association with the exposure. (B) This bidirectional MR analysis was performed in two steps: sarcopenia-related traits (hand grip strength, ALM, walking speed) was studied as exposure while T2DM and glycemic characteristics were studied as outcome in the first step, whereas the second step was reversed. The arrows indicate direction of causality in our results. MR, mendelian randomization; IVs, instrumental variables; T2DM, Type 2 diabetes mellitus; ALM, appendicular lean mass. ## Data source The open GWAS database established by the MRC Integrated Epidemiology Unit (IEU) (https://gwasmrcieu.ac.uk/) provided the majority of the summary-level data used in this investigation [11]. The datasets are largely public and may be downloaded using R and Python packages for accessing the application programming interface. Table 1 shows the phenotypic and consortium details. Table 1 \| Phenotype \| Consortium \| Participants \| Datatype \| GWAS ID \| Year of publication \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Low hand grip strength (EWGSOP) \| UKB \| n = 48,596 cases and 207,927 controls \| Binary \| ebi-a-GCST90007526 \| 2021 \| \| ALM \| UKB \| 450243 \| Continuous \| ebi-a-GCST90000025 \| 2020 \| \| Walking pace \| UKB \| 459915 \| Continuous \| ukb-b-4711 \| 2018 \| \| T2DM \| DIAMANTE \| n = 180,834 cases and 492,191 controls \| Binary \| \| 2022 \| \| Fasting glucose \| MAGIC \| 200622 \| Continuous \| ebi-a-GCST90002232 \| 2021 \| \| Fasting insulin \| MAGIC \| 151013 \| Continuous \| ebi-a-GCST90002238 \| 2021 \| \| HbA1c \| MAGIC \| 46368 \| Continuous \| ieu-b-103 \| 2010 \| \| Two-hour glucose challenge \| MAGIC \| 15234 \| Continuous \| ebi-a-GCST000569 \| 2010 \| ## Source of exposure data The GWAS summary data for hand-grip strength were obtained from a meta-analysis of 256,523 people of European origin ≥ 60 years of age and in 22 cohorts [12]. Based on hand-grip strength using the European Working Group on Sarcopenia in Older People’s (EWGSOP) definition of 30 kg for men and 20 kg for women, 46,596 ($18.9\%$) of all participants reported muscular weakness. We collected GWAS summary data from the UK Biobank for appendicular lean mass (ALM) in 450,243 people and walking pace in 459,915 people who were included in the discovery cohort [13]. The UK Biobank is an expansive biomedical database and a research resource [14]. It includes in-depth information on the genetic makeup and health of about half a million people between 40 and 69 years of age who participated in the study in the UK. Genetic association studies (GWAS) on sarcopenia-related variables were undertaken in European populations using data from population-based cohorts. ## Source of outcome data In this study, the instrument for the outcome (T2DM) was received from the DIAMANTE consortium and from the summary statistics of a GWAS meta-analysis. The GWAS meta-analysis comprised 22 GWAS that involved 180,834 patients with type 2 diabetes and 492,191 controls and five pedigree groups ($51.1\%$ of European ancestry). The GWAS database of European ancestors was released by Mahajan et al. in 2022 [15]. Summary data for fasting glucose, fasting insulin, glycated hemoglobin (HbA1c), and 2-hour glucose challenge were collected from an online source (https://gwas.mrcieu.ac.uk/) under the GWAS IDs ebi-a-GCST90002232 (fasting glucose, $$n = 200$$,622), ebi-a-GCST90002238 (fasting insulin, $$n = 151$$), ieu-b-103 (HbA1C, $$n = 46$$,368) and ebi-a-GCST000569 (two-hour glucose challenge, $$n = 15$$,234) [11]. ## IV selection and validation Under the foundational premise of MR, specific SNPs for each exposure attribute were chosen as IVs. The IVs med three assumptions (Figure 1A). [ 1] The IVs had a significant relationship with the exposure. [ 2] The IVs had no pleiotropic correlations with any known confounding factors, and [3] had no association with the outcome, with the possible exception of how the link was mediated by their association with the exposure [16, 17]. We obtained the IVs from the published GWAS using the clumping function in the Two-Sample MR package. Except for hand-grip strength, HbA1c, and two-hour glucose challenge ($p \leq 5$ × 10-6), all IV and exposure traits were significantly ($p \leq 5$ × 10-8) and independently [chain disequilibrium (LD) r2 < 0.01) associated [18]. Following the guidelines, we first chose several independent SNPs strongly related to each exposure variable before matching them in the outcome database. During the harmonization process, the harmonies data function from the two-sample MR package was used so the effect allele of each SNP could be matched with the corresponding allele of the exposure. To correct for multiple comparisons, the Bonferroni method was utilized (five outcomes). The associations with p-values < 0.003 ($\frac{0.05}{15}$) were regarded as statistically significant associations, and associations with p-values between 0.05 and 0.003 were considered as suggestive associations. ## Analysis of horizontal pleiotropy and heterogeneity Causal estimates and findings may be skewed due to the pleiotropy of SNPs in the IVW study [19]. We used the pleiotropy test function with the two-sample MR package to determine whether the SNPs were suitable IVs. If the pleiotropy was not significant ($p \leq 0.05$), then it was possible to use the IVs. We selected SNPs one by one on the Phenoscanner website (http://www.phenoscanner.medschl.cam.ac.uk/) if the p-value of the pleiotropy test was < 0.05 [20]. Cochran’s Q test was used to approximate heterogeneity among the chosen IVs. The fixed-effects model was used if the heterogeneity was negligible. Otherwise, the IVW with random effects approach was acceptable. ## Analysis of Mendelian randomization Mendelian randomization with genetic variations as the IVs allows investigation and assessment of the causal influence of an exposure on an outcome. First, we divided the per-allele log-odds ratio (or beta) of each variation in the outcome data by the log-odds ratio (or beta) of the same variant in the exposure data to get the Wald ratio for each IV. After that, the inverse variance weighted (IVW) approach was used to arrive at an estimation of the connection between the exposures and the results. IVW weights the Wald ratio of each SNP by its inverse variance and meta-analysis impact estimates using random or fixed effects. If the p-value of the Cochran Q test was < 0.05, we used the use of random-effect models; otherwise, we relied on fixed-effect models. To complement the outcome of IVW, MR-Egger and the weighted median approach were used [21]. ## Sensitivity analysis To further guarantee the reliability of the MR causal impact estimate, we performed sensitivity analyses [22]. To start, the MR-Egger approach was used to determine whether SNPs had horizontal pleiotropy [23]. If the p-value of the intercept term was < 0.05, then the IVs may have pleiotropic effects. But there is no evidence of horizontal pleiotropy across the chosen IVs if the p-value of the intercept term is not < 0.05. Second, the standard MR testing outliers were identified with Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO) test, which produced a robust estimate after outlier correction. A sensitivity analysis was performed to test for substantial distortions in the IVW causal estimate before and after MR-PRESSO adjustment. In this study, random-effects IVW was replicated by taking removing individual SNPs and then comparing the total analysis. The sensitivity of each SNP was inferred from how much the findings changed before and after it was removed. ## Reported results and software MR findings were reported as odd ratios (ORs) with $95\%$ confidence intervals (CIs) per standard deviation for dichotomous variables, and as beta values (β) with $95\%$ CIs per standard deviation for continuous variables. Both were stated consistently as estimate values. A two-tailed p-value of 0.05 was the threshold of statistical significance. The statistical analysis was conducted with two-sample MR and MR-PRESSO packages of R version 4.0.0 [24]. ## Stage 1: Effect of genetically predicted sarcopenia-related traits on T2DM and glycemic traits Screening of the IVs found SNP beta, SE, and p-values strongly related to exposure. We found the beta, SE, and p-value corresponding to the SNP in the outcome database. Supplementary Tables 1-3 has more information about IVs. For low hand-grip strength, ALM, and walking pace, we found 59, 690, and 57 LD-independent (r2 < 0.001) IVs that reached genome-wide significance ($p \leq 5$ × 10−8), except low hand-grip strength ($p \leq 5$ × 10−6). The horizontal pleiotropy test findings revealed that all IVs lacked horizontal pleiotropy ($p \leq 0.05$). One IV groups were not heterogeneous ($p \leq 0.05$). The IVW random-effect model for IV with heterogeneity was used; otherwise, the IVW fixed-effect model was used. The level pleiotropy and heterogeneity test results are shown in Table 2. Table 2 \| Exposures \| Outcomes \| No.of Ivs \| Heterogeneity test \| Heterogeneity test.1 \| Pleiotropy_test \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Exposures \| Outcomes \| No.of Ivs \| Cochran's Q \| P \| P \| \| Low hand grip strength (EWGSOP) \| T2DM \| 52 \| 225.3713 \| <0.001 \| 0.377 \| \| Low hand grip strength (EWGSOP) \| Fasting glucose \| 56 \| 108.4448 \| <0.001 \| 0.244 \| \| Low hand grip strength (EWGSOP) \| Fasting insulin \| 56 \| 95.9703 \| <0.001 \| 0.661 \| \| Low hand grip strength (EWGSOP) \| HbA1c \| 30 \| 38.4647 \| 0.112 \| 0.675 \| \| Low hand grip strength (EWGSOP) \| Two-hour glucose challenge \| 33 \| 46.5341 \| 0.047 \| 0.305 \| \| ALM \| T2DM \| 552 \| 3214.1050 \| <0.001 \| 0.289 \| \| ALM \| Fasting insulin \| 372 \| 981.8712 \| <0.001 \| 0.424 \| \| ALM \| Fasting glucose \| 617 \| 1635.6020 \| <0.001 \| 0.998 \| \| ALM \| HbA1c \| 362 \| 474.1836 \| <0.001 \| 0.903 \| \| ALM \| Two-hour glucose challenge \| 401 \| 453.5987 \| 0.033 \| 0.886 \| \| Walking pace \| T2DM \| 56 \| 496.6391 \| <0.001 \| 0.284 \| \| Walking pace \| Fasting glucose \| 56 \| 112.1423 \| <0.001 \| 0.962 \| \| Walking pace \| Fasting insulin \| 56 \| 125.2184 \| <0.001 \| 0.268 \| \| Walking pace \| HbA1c \| 43 \| 59.7183 \| 0.037 \| 0.745 \| \| Walking pace \| Two-hour glucose challenge \| 46 \| 67.2943 \| 0.017 \| 0.473 \| The findings of this MR study are mostly concerned with IVW analysis. Our findings reveal that sarcopenia-related features were all linked to T2DM. Low hand-grip strength had a potential causal relationship with an increased incidence of T2DM (OR = 1.109; $95\%$ CI: 1.008–1.222; $$p \leq 0.035$$). T2DM risk was reduced by increasing ALM and walking pace. A 1 kg2 increase in ALM decreased the risk of T2DM by $10.2\%$ (OR = 0.898; $95\%$ CI: 0.830-0.952; $p \leq 0.001$), but a 1 m/s increase in walking pace decreased the odds of T2DM by $90.0\%$ (OR = 0.100; $95\%$ CI: 0.053–0.186 $p \leq 0.001$). ALM also had a potential causal relationship with HbA1C (OR = −0.006; $95\%$ CI: −0.029 to −0.017; $$p \leq 0.012$$) and 2-hour glucose post-challenge (OR = −0.292; $95\%$ CI: −0.401 to −0.184; $$p \leq 0.001$$). Table 3 shows the findings of the thorough MR analysis. According to the intercept of the MR–Egger regression, there was no evidence of horizontal pleiotropy between the exposures and the outcomes ($p \leq 0.05$). As expected, IVW findings were consistent after MR-PRESSO adjustment. The sensitivity analysis findings are shown in Table 4. ## Stage 2: Genetically predicted T2DM and glycemic traits influence sarcopenia-related traits We obtained beta, standard error, and p-values of SNPs significantly associated with exposure using the instrumental variables (IV) screening. In the outcomes database, we discovered the beta value, standard error, and p-values that correlated with the SNPs. For fasting glucose, fasting insulin, and T2DM, we found 66, 38, and 187 linkage disequilibrium (LD)-independent (r2 < 0.001) IVs that reached genome-wide significance level ($p \leq 5$×10-8). For HbA1c, 2-hour blood glucose challenge, we found 31 and 11 linkage disequilibrium (LD)-independent (r2 < 0.001) IVs that reached genome-wide significance level ($p \leq 5$×10-6). Supplementary Tables 4-8 has detailed information about IVs. The horizontal pleiotropy test confirmed the of absence horizontal pleiotropy ($p \leq 0.05$). There were one IV groups that were not heterogeneous ($p \leq 0.05$). The IVW random-effect model was used if heterogeneity was present. Otherwise, the IVW fixed-effect model was used. The level pleiotropy and heterogeneity test results are shown in Table 5. Table 5 \| Exposures \| Outcomes \| No.of Ivs \| Heterogeneity test \| Heterogeneity test.1 \| Pleiotropy_test \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Exposures \| Outcomes \| No.of Ivs \| Cochran's Q \| P \| P \| \| T2DM \| Low hand grip strength (EWGSOP) \| 180 \| 367.2484 \| <0.001 \| 0.287 \| \| T2DM \| ALM \| 181 \| 5109.0300 \| <0.001 \| 0.157 \| \| T2DM \| Walking pace \| 163 \| 395.5066 \| <0.001 \| 0.139 \| \| Fasting glucose \| Low hand grip strength (EWGSOP) \| 64 \| 106.3683 \| <0.001 \| 0.886 \| \| Fasting glucose \| ALM \| 64 \| 1120.4190 \| <0.001 \| 0.648 \| \| Fasting glucose \| Walking pace \| 40 \| 113.3818 \| <0.001 \| 0.141 \| \| Fasting insulin \| Low hand grip strength (EWGSOP) \| 38 \| 80.3508 \| <0.001 \| 0.691 \| \| Fasting insulin \| ALM \| 38 \| 2377.4080 \| <0.001 \| 0.326 \| \| Fasting insulin \| Walking pace \| 37 \| 164.1926 \| <0.001 \| 0.523 \| \| HbA1c \| Low hand grip strength (EWGSOP) \| 28 \| 43.4848 \| 0.023 \| 0.061 \| \| HbA1c \| ALM \| 29 \| 368.1774 \| <0.001 \| 0.073 \| \| HbA1c \| Walking pace \| 29 \| 60.4644 \| <0.001 \| 0.970 \| \| Two-hour glucose challenge \| Low hand grip strength (EWGSOP) \| 7 \| 8.6807 \| 0.192 \| 0.086 \| \| Two-hour glucose challenge \| ALM \| 7 \| 288.4325 \| <0.001 \| 0.406 \| \| Two-hour glucose challenge \| Walking pace \| 7 \| 25.4576 \| <0.001 \| 0.528 \| This MR analysis mostly refers to IVW. It was shown that T2DM was a potential pathogenic factor in sarcopenia-related characteristics ($p \leq 0.05$), except for ALM (β = 0.018; $95\%$ CI: −0.008 to −0.044; $$p \leq 0.168$$). There was a potential positive correlation between fasting insulin and walking pace (β = 0.062; $95\%$ CI: −0.0001 to −0.125; $$p \leq 0.050$$). There was no correlation between HbA1c and sarcopenia-related characteristics ($p \leq 0.05$). Low hand-grip strength (OR = 0.058; $95\%$ CI: 0.005–0.111; $$p \leq 0.031$$) was the only sarcopenia-related characteristic causally linked with a 2-hour glucose test. A gene predictive of 2-hour glucose test results after the challenge would increase the of having a weak hand-grip strength. Table 6 shows the findings of the MR analysis. No horizontal pleiotropy ($p \leq 0.05$) was found in the MR-Egger regression of exposures and outcomes, as shown by the intercept. As expected, the IVW findings were consistent after MR-PRESSO adjustment. The sensitivity analysis findings are shown in Table 7. ## Discussion The bi-directional two-sample MR analysis in this study was performed using summary-level data. We examined the potential causative connections between sarcopenia-related characteristics (hand-grip strength, ALM, and walking pace) and T2DM and glycemic features. Major findings were: [1] Sarcopenia-related variables predicted genetically were all causally associated with T2DM; [2] There was a potential causal relationship between genetically predicted T2DM and sarcopenia-related characteristics but not expected ALM in the other direction. [ 3] The results showed there was potential bi-directional causation between sarcopenia and T2DM to a greater extent. In observational studies, hand-grip strength was shown to have a bidirectional relationship with T2DM. A previous English longitudinal study found that for each unit gain in hand-grip strength, T2DM risk was reduced by $2\%$ (HR = 0.98; $95\%$ CI: 0.96–0.99) [25]. In another cohort study, T2DM was an independent predictor of weak hand-grip strength [26]. A TSMR investigation was conducted to evaluate the causal relationship and discovered a bi-directional causal relationship between hand-grip strength and T2DM [27]. Our results were consistent with these previous studies and provided evidence of a potential bi-directional causality between hand-grip strength and T2DM. In a long-term retrospective analysis of 159 older women, researchers found that patients with low ALM had a 3.81 higher incidence of IFG/T2DM than those with normal ALM (OR = 3.81; $95\%$ CI: 1.09-9.80) [28]. Our MR findings follow prior research that ALM decreased the risk of T2DM. But in the other direction, T2DM was not causally associated with ALM. Notably, the major glucose metabolic reservoir of T2DM was in muscle mass, suggesting that our results only partially demonstrate a causal relationship between ALM and diabetes. More studies with better MR methods and data are needed in the future to validate the causal relationship between ALM and diabetes. Prospective studies have shown that a slower walking pace was associated with an increased risk of T2DM. Men and women who walked slowly were shown to have an increased risk of incident T2DM [29]. A Baltimore Longitudinal Study of Aging found that T2DM was a significant predictor of slower gait speed in the elderly [30]. Our MR findings, in contrast to those of the traditional studies, included a strong bi-directional causative connection between walking pace and T2DM. Many previous studies had demonstrated the positive association between sarcopenia and T2DM, and the interaction between the two conditions seems to tilt more toward a bidirectional causation correlation owing to the complexity and proximity of their relationship. Landin et al. described sarcopenia and T2DM as two sides of the same coin when discussing the correlation between the two conditions [31]. Through the use of transcriptome analysis, Huang et al. discovered 15 shared genes associated with both sarcopenia and T2DM, in addition to multiple shared pathways between the two disorders [32]. A meta-analysis of community-dwelling Asian populations found that T2DM patients had a considerably greater incidence of sarcopenia than non-diabetics [33]. In another recent meta-analysis of observational studies, T2DM was related to an elevated risk of sarcopenia [34]. Our bidirectional MR study further complements previous studies and provides evidence for a potential causal relationship between sarcopenia and T2DM. Sarcopenia and TDM are both age-related diseases with the same underlying pathophysiological mechanism [4, 5, 35]. T2DM is often associated with insulin resistance, increased inflammation, accumulation of advanced glycation end products, and elevated oxidative stress [36]. These pathways would result in activated inflammation, mitochondrial and vascular dysfunction, and protein metabolism deficiencies, all of which would be harmful to muscle health, including muscle mass, muscular strength, and muscle quality [32]. Because of reduced insulin sensitivity, the anabolic activity of insulin in skeletal muscle may be gradually lost in T2DM. Furthermore, poor insulin action may result in increased protein breakdown and reduced protein synthesis, resulting in a loss of muscle mass and strength [37, 38]. As a result, T2DM patients are at a higher risk of sarcopenia [39, 40]. On the other hand, older people may be at a higher risk for developing T2DM due to the effects of sarcopenia. Sarcopenia is a condition defined by decreased muscular mass, low muscle strength, or poor athletic performance [41], and may contribute to impaired glucose clearance, increased inflammation, and reduced metabolic rate and physical activity. Skeletal muscle is essential for glucose clearance, accounting for more than $80\%$ of postprandial glucose uptake. As a result, sarcopenia alters glucose disposal by lowering muscle mass and increasing localized inflammation, which may contribute to the development and progression of T2DM [42, 43]. Our research has several advantages. First, this was the first bidirectional MR study to investigate the causal association between three sarcopenia-related traits (EWGSOP) and T2DM, especially evaluating the causal relationship between walking pace and T2DM, which had been neglected in previous studies. Second, the database for this study was the most recent and reliable. The GWAS summary data for low hand-grip strength was defined by EWGSOP; T2DM data originated from one of the largest diabetes case-control studies, which was published in 2022. Third, different MR analysis methods were used to confirm the correctness and validity of the results, and sensitivity analyses such as MR-PRESSO were performed to obtain consistent estimations of the causal impact sizes of MR. Finally, we investigated the link between walking pace and diabetes, which had been neglected in earlier research. Still, the study’s weaknesses should be mentioned. To begin, this study was carried out using summary-level data, which restricted our capacity to carry out subgroup analyses, such as those regarding low hand-grip strength and ALM according to gender. Besides, we applied ALM (appendicular lean mass) rather than ASM (appendicular skeletal muscle mass) to evaluate muscle mass, which may be inaccurate due to bias from other non-fat soft tissue components such as lungs, kidneys, and other internal organs. In addition, our research population was exclusive of European descent, so our results may not apply to other racial or ethnic groups. At last, residual bias cannot be avoided, as it is a recognized shortcoming of the MR technique, even when the pleiotropy test and MR-PRESSO procedures are used to prevent confounding by pleiotropy. In conclusion, sarcopenia and T2DM may mutually have a potential causal influence on each other. Our study suggests that sarcopenia-related traits (hand-grip strength, ALM, walking pace) may benefit T2DM. Instead, T2DM may be linked to decreased hand-grip strength and walking pace. This study provides solid evidence that hand-grip strength, ALM and walking pace are possible predictors of T2DM in middle-aged and elderly people. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Materials. Further inquiries can be directed to the corresponding author. ## Author contributions Study design: LY, SC. Manuscript writing: SC, SY. Statistical analysis and data interpretation: SY, NA, KK. Critical revision of the manuscript: LY, XY. Literature Search: SC, SY, NA, KK, QW, TZ. All authors contributed to the article and approved the submitted version. ## Conflict of interest We declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1109800/full#supplementary-material ## References 1. Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP. Global prevalence of sarcopenia and severe sarcopenia: A systematic review and meta-analysis. 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--- title: 'Increased prevalence of autoimmune thyroid disease after COVID-19: A single-center, prospective study' authors: - Alessandro Rossini - Sara Cassibba - Francesca Perticone - Simone Vasilij Benatti - Serena Venturelli - Greta Carioli - Arianna Ghirardi - Marco Rizzi - Tiziano Barbui - Roberto Trevisan - Silvia Ippolito journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031076 doi: 10.3389/fendo.2023.1126683 license: CC BY 4.0 --- # Increased prevalence of autoimmune thyroid disease after COVID-19: A single-center, prospective study ## Abstract ### Introduction Thyroid dysfunctions associated with SARS-CoV-2 acute infection have been extensively described since the beginning of COVID-19 pandemics. Conversely, few data are available on the occurrence of thyroid autoimmunity after COVID-19 resolution. We assessed the prevalence of autoimmune thyroid disease (ATD) and thyroid dysfunctions in COVID-19 survivors three months after hospital admission. ### Design and methods Single-center, prospective, observational, cohort study performed at ASST Papa Giovanni XXIII Hospital, Bergamo, Italy. 599 COVID-19 survivors were prospectively evaluated for thyroid function and autoimmunity thyroperoxidase antibodies (TPOAb), thyroglobulin antibodies (TgAb). When a positive antibody concentration was detected, thyroid ultrasound was performed. Multiple logistic regression model was used to estimate the association between autoimmunity and demographic characteristics, respiratory support, and comorbidities. Autoimmunity results were compared to a cohort of 498 controls referred to our Institution for non-thyroid diseases before the pandemic onset. A sensitivity analysis comparing 330 COVID-19 patients with 330 age and sex-matched controls was performed. ### Results Univariate and multivariate analysis found that female sex was positively associated (OR 2.01, SE 0.48, $$p \leq 0.003$$), and type 2 diabetes (T2DM) was negatively associated (OR 0.36, SE 0.16, $$p \leq 0.025$$) with thyroid autoimmunity; hospitalization, ICU admission, respiratory support, or COVID-19 treatment were not associated with thyroid autoimmunity ($p \leq 0.05$). TPOAb prevalence was greater in COVID-19 survivors than in controls: $15.7\%$ vs $7.7\%$, $$p \leq 0.002.$$ Ultrasonographic features of thyroiditis were present in $94.9\%$ of the evaluated patients with positive antibodies. TSH was within the normal range in $95\%$ of patients. ### Conclusions Autoimmune thyroid disease prevalence in COVID-19 survivors was doubled as compared to age and sex-matched controls, suggesting a role of SARS-CoV-2 in eliciting thyroid autoimmunity. ## Introduction Viral infections may trigger autoimmune diseases [1]. Reports of autoimmune conditions occurring after SARS-CoV-2 infection have been described [2], including anecdotal cases of Graves’ disease (3–5) and Hashimoto’s thyroiditis [6, 7]. However, only few studies systematically evaluated the impact of COVID-19 in the development of autoimmune thyroid disease (ATD). Anaya et al. [ 8] found an increased prevalence of thyroperoxidase antibodies (TPOAb) in 120 patients hospitalized for COVID-19 as compared to healthy, pre-pandemic controls, suggesting an activation of thyroid autoimmunity by SARS-CoV-2. Consistently, Lui et al. [ 9] reported an increase in TPOAb concentration in COVID-19 survivors three months after hospital admission. However, most patients in this cohort were treated with interferon beta (IFN-beta) that has been associated per se with the induction of thyroid autoimmunity; the reassessment of a larger cohort of patients not exposed to IFN-beta was thus advocated by the Authors to provide a conclusive answer. Alterations of thyroid function tests (TFTs) during the acute phase of COVID-19 have been more extensively characterized since the beginning of the pandemics [10, 11]. Low TSH levels, attributed either to a destructive thyroiditis associated with thyrotoxicosis or to a non-thyroidal illness (NTI), were reported in several studies (12–18). According to most studies (12–14, 16, 19), TFTs usually normalize after COVID-19 recovery, but this finding has not been established in a large population. Aim of our study was to assess the prevalence of ATD and thyroid dysfunction in a large cohort of COVID-19 survivors at a medium-term (three months) follow-up after hospitalization. ## Study cohort COVID-19 survivors participating to our outpatient service program were eligible for the study. The enrollment protocol has been described in a previous paper [20]. Briefly, a list of all patients with COVID-19 discharged from the emergency department or admitted to the hospital wards of our Institution (ASST Papa Giovanni XXIII, Bergamo, Italy) was obtained from the hospital electronic health records database. Asymptomatic positive patients admitted for planned procedures were excluded. Other exclusion criteria were: age less than 18 years, pregnancy, history of thyroid disease or previous thyroid surgery, concomitant medications known to interfere with thyroid function (lithium, amiodarone, interferon-α and antiretroviral drugs), severe kidney insufficiency (eGFR < 30 ml/min), and severe liver failure. Patients’ enrollment took place between 2 May and 31 July 2020, before availability of SARS-CoV-2 vaccines, to avoid potential biases due to occurrence of post-vaccination thyroid disorders [21, 22]. To compare thyroid autoimmunity data, a control group was retrieved from the hospital electronic health records database. Controls were included if i) had one assessment of TPOAb and/or thyroglobulin antibodies (TgAb) from January 2016 to January 2020, ii) their medical history was negative for thyroid disease, and iii) they referred to our Institution for reasons other than a suspected thyroid disease. ## Assays Thyroid stimulating hormone (TSH), TPOAb, and TgAb were measured in all patients; free thyroxine (fT4) and free tri-iodothyronine (fT3) were measured in patients with abnormal TSH levels. A chemiluminescent immunoassay (Atellica Solution, Siemens) was employed. Normal range for TSH, fT4 and fT3 were 0.5-5.0 mIU/L, 0.7-1.8 ng/dL, and 2.3-4.5 pg/mL, respectively. For TPOAb, measuring interval was 28-1300 U/mL and range of normality was below 60 U/mL. For TgAb, measuring interval was 15-500 U/mL and range of normality was below 60 U/mL. ## Ultrasound assessment When positive antibodies were detected, ultrasonography of the thyroid was prescribed. Thyroid volume was calculated with the ellipsoid formula [23]: width (mm) x length x thickness x 0.52 = volume (mL) for each lobe. Ultrasonographic diagnosis of thyroiditis was made if one or more of the following features were present: hypoechogenity of gland parenchyma, non-homogeneous parenchymal texture, and increased vascularity. All thyroid ultrasound examinations were performed by two operators (AR and SC) with the same instrument (My Lab Seven, Esaote, Italy), using a 3- to 13-MHz linear transducer. ## Statistical analysis Descriptive statistics was used to summarize clinical characteristics of COVID-19 patients during the acute phase of the disease and at the subsequent clinical evaluation. Continuous variables were expressed as medians and interquartile ranges (IQRs) and categorical variables were presented as frequencies and percentages. The study population was then stratified based on the presence of thyroid autoimmunity (yes/no), and differences between groups were tested using the Mann-Whitney test for continuous variables and the chi-square test (or Fisher’s exact test when appropriate) for categorical variables. To evaluate the association of thyroid autoimmunity and COVID-19, we conducted a sensitivity analysis comparing 330 COVID-19 patients with 330 age and sex-matched subjects retrieved from control group. A multiple logistic regression model was used to estimate odds ratios (ORs) of autoimmunity and their corresponding $95\%$ confidence intervals (CIs) for the following variables: age (at entrance), sex, respiratory support (no support/low need/high need), and diabetes mellitus (yes/no). In the multivariable analysis were included demographic characteristics, respiratory support (as proxy of disease severity) and covariates that resulted significantly different between groups in the univariate analysis. For all tested hypotheses, two-sided p-values of 0.05 or less were considered significant. Statistical analysis was performed using STATA Software, release 16.1 (StataCorp LP, College Station TX, USA) and was carried out at the biostatistical laboratory of the Foundation for Research (FROM) at Papa Giovanni XXIII Hospital in Bergamo. ## Results The search in hospital electronic health records database identified 2965 patients eligible for the study (946 discharged from emergency department and 2019 admitted to Hospital), of which 646 died before the enrollment and 405 declined to participate. Of the remaining 1914, 767 were screened by 31 July 2020. In total, 168 patients met the exclusion criteria for this study. The final population therefore consisted of 599 patients (180 females). Figure 1 shows the flow-chart describing screened, included, and excluded subjects. Median time at evaluation was 102.5 days after hospital admission. Figure 1: Flow chart of screened, included, and excluded patients. TPOAb were above the normal range in 85 patients ($14.2\%$), TgAb in 43 ($7.2\%$) and both antibodies in 23 ($3.8\%$) patients. At least one antibody was positive in 105 patients (48 females), with an overall prevalence of thyroid autoimmunity of $17.5\%$. Median TPOAb was 102 U/mL (IQR 68.5 – 611) in patients with positive TPOAb and 36 U/mL (IQR 27-44) in patients with negative TPOAb; median TgAb was 174 U/mL (IQR 89.5 – 285.7) in patients with positive TgAb and 18 U/mL (IQR 14-23) in patients with negative TgAb. Median TSH was 1.55 mIU/L (IQR 1.09 - 2.15); thirty patients ($5.0\%$) showed abnormal TSH values, of which 19 ($3.2\%$) had values below 0.5 mIU/L and 11 ($1.8\%$) above 5.0 mIU/L. All patients with TSH levels < 0.5 mIU/L had normal fT3 and fT4 levels. Nine out of the eleven patients with TSH levels > 5.0 mIU/L had normal fT4 levels, exhibiting a condition of subclinical hypothyroidism. The other two patients displayed overt hypothyroidism. Median TSH of patients with thyroid autoimmunity was 1.77 mIU/L (1.25 - 2.55). Ten patients ($9.6\%$) in this subgroup showed abnormal TSH values, of which six ($5.7\%$) had values below 0.5 mIU/L and 4 ($3.9\%$) had values above 5.0 mIU/L. Ultrasonography was prescribed to every patient with positive thyroid antibodies; however, only 59 patients (26 females) accepted to undergo the examination, which was performed at a median time of 23 days after the blood tests. Mean thyroid volume was 11.5 mL in males and 9.5 mL in females. Ultrasonographic features of thyroiditis were present in 56 patients ($94.9\%$). Univariate analysis found that thyroid autoimmunity was positively associated with female sex ($p \leq 0.001$) and negatively associated with type 2 diabetes (T2DM) ($$p \leq 0.009$$), but not with hospitalization, ICU admission, respiratory support, or COVID-19 treatment (Table 1). Multivariable analysis confirmed the association between thyroid autoimmunity and both female sex and T2DM (see Table 2). The control group included 498 patients (320 females, median age 52.7 years). TPOAb were available in 444 patients, TgAb in 373 and both autoantibodies in 325. TPOAb were above the normal range in $\frac{37}{444}$ patients ($8.3\%$), TgAb in $\frac{33}{373}$ ($8.8\%$) and both antibodies in $\frac{14}{325}$ ($4.3\%$) patients. The sensitivity analysis included 660 subjects (330 patients and 330 controls) matched for age and sex, with a female prevalence of $49.7\%$ for both groups and a median age of 60 (IQR 51-70) in patients and of 59 (47–68) in controls. Positive TPOAb prevalence was higher in patients than in controls ($\frac{52}{330}$, $15.7\%$ vs. $\frac{23}{297}$, $7.7\%$; $$p \leq 0.002$$), while no difference was observed in positive TgAb prevalence ($\frac{22}{330}$, $6.7\%$ vs. $\frac{20}{250}$, $8\%$; $$p \leq 0.539$$). Median TPOAb (40 (IQR 31-51) vs. 31 (IQR [27-40]) and TgAb (18 (IQR 14-25) vs. 14 (IQR 14-20)) were within the normal range but significantly higher in COVID-19 patients as compared to controls (both $p \leq 0.001$) (Table 3). Table 3 \| Unnamed: 0 \| COVID-19 Patients \| Controls \| p-value \| \| --- \| --- \| --- \| --- \| \| Female Sex* (n, %) \| 164/330 (49.7%) \| 164/330 (49.7%) \| \| \| Age* (median, IQR) \| 60 (51-70) \| 59 (47-68) \| \| \| Positive TPOAb (n, %) \| 52/330 (15.7%) \| 23/297 (7.7%) \| 0.002 \| \| Positive TgAb (n, %) \| 22/330 (6.7%) \| 20/250 (8%) \| 0.539 \| \| TPOAb (U/mL) (median, IQR) \| 40 (31-51) \| 31 (27-40) \| < 0.001 \| \| TgAb (U/mL) (median, IQR) \| 18 (14-25) \| 14 (14-20) \| < 0.001 \| ## Discussion Studies evaluating the impact of SARS-CoV-2 infection on thyroid mainly focused on the alterations of TFTs during the acute phase of the disease, with less evidence about possible long-term effects on thyroid autoimmunity. Our aim was indeed to characterize thyroid autoimmunity and function in the largest cohort of COVID-19 survivors to date. Most patients had normal TSH levels three months after hospital admission, as already reported in previous studies with smaller cohorts (12–14, 16, 19). Accordingly, the rate of newly diagnosed thyroid dysfunction was comparable to general population [24, 25]. This finding seems to rule out a permanent direct damage to the thyroid gland induced by SARS-CoV-2. In this view, the alterations of TFTs in the acute phase of COVID-19 could more probably be secondary to a NTI [16] or a transient, self-limiting, thyroiditis. The overall prevalence of thyroid autoimmunity in our cohort was $17.5\%$. Interestingly, the prevalence of positive TPOAb in COVID-19 patients was doubled as compared to controls matched for sex and age ($15.7\%$ vs $7.7\%$). Few authors evaluated thyroid autoimmunity in COVID-19 patients, mostly during the acute phase. Anaya et al. [ 8] reported an increased prevalence of TPOAb in 120 patients hospitalized for COVID-19 as compared to healthy, pre-pandemic controls ($36.7\%$ vs. $20\%$). Lui et al. [ 9] identified TPOAb in $20.5\%$ of patients hospitalized for COVID-19; the Authors reevaluated thyroid autoimmunity three months after the admission, reporting a significant increase in TPOAb with 4 out of 82 patients becoming TPOAb positive and an overall prevalence of TPOAb positivity of $25\%$. The same group confirmed these results in a subsequent study including also asymptomatic COVID-19 patients [19]. Our finding of an increased concentration and prevalence of TPOAb in COVID-19 survivors strengthens the hypothesis that SARS-CoV-2 could be able to trigger thyroid autoimmunity; similarly, we found a slightly increased TgAb concentration in patients as compared to controls, though positive TgAb prevalence did not differ between the two groups; TgAb, however, are less useful than TPOAb in predicting thyroid dysfunction [26]. Besides, the presence of an actual autoimmune process was consistently confirmed by the evidence of ultrasonographic features of thyroiditis in almost all patients with positive thyroid antibodies. As already described for other viruses, SARS-CoV-2 may elicit autoimmune conditions through an hyperactivation of both the innate and adaptive immune response [27]. Specifically, SARS-CoV-2 may directly trigger thyroid autoimmunity infecting thyroid follicular cells, where ACE-2 receptor is abundantly expressed [28]; viral presence has indeed been retrieved in thyroid specimens [29, 30] and reactivity between TPO antigen and SARS-CoV-2 has been demonstrated in vitro, favoring the hypothesis of molecular mimicry [31]. Alternatively, the hyperinflammatory status caused by severe COVID-19 may induce thyroid damage through the systemic increase of cytokines, unleashing thyroid autoimmunity in genetically predisposed individuals [32]. Our findings may suggest that the latter mechanism plays a minor role in triggering thyroid autoimmunity, since the presence of ATD was unrelated with clinical parameters of COVID-19 severity. In our cohort, thyroid autoimmunity directly correlated with female sex, as expected, and inversely correlated with type 2 diabetes mellitus (T2DM). Diabetes is generally characterized by an impairment of the immune system [33] and diabetic patients with non-severe COVID-19 have a reduced antibodies response to SARS-CoV-2 [34]. It is therefore conceivable that diabetic survivors were also characterized by a decrease of autoantibodies against thyroid. Moreover, since during hospitalization in our hospital most diabetic patients were treated with sitagliptin, the immunomodulatory role exerted by the drug [35] may have limited the onset of thyroid immunity. The strength of our study relies on i) being a large monocentric study, with all patients treated at the same institution and subsequently evaluated by two endocrinologists, and ii) the inclusion of a sensitivity analysis that allowed a direct comparison with age and sex matched controls evaluated before the pandemics. The main limitations are i) the lack of baseline data about thyroidal status (function and antibodies) of the patients; and ii) not having assessed TSH-receptor antibodies in patients with low TSH. However, it has to be taken into account that routine assessment of thyroid function and autoimmunity is not recommended in the clinical care of acute COVID-19 patients [36]. In conclusion, our study showed that a relatively high proportion of COVID-19 survivors develop both serological and ultrasonographic features of thyroiditis, with only a minority displaying TFTs abnormalities. It is thus possible that the activation of immune response occurring during the acute phase of COVID-19 may induce or precipitate the onset of ATD in some patients. Since the development of thyroid autoimmunity usually precedes the onset of thyroid dysfunction, further longitudinal studies are needed to evaluate thyroid function in a long-term follow-up. Accordingly, the assessment of TPOAb and TFTs could be considered in patients evaluated for long COVID [19], as symptoms of this condition may overlap with those associated with ATD. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Author contributions AR, SC, MR, SB, SV, and RT designed the study. AR and SC evaluated the patients. AR, SI, GC, AG, and TB designed and performed the analyses. AR, FP, and SI drafted the manuscript and prepared figure and tables. 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--- title: 'Clinical characteristics and risk factors of cardiac surgery associated-acute kidney injury progressed to chronic kidney disease in adults: A retrospective, observational cohort study' authors: - Xiaoguang Fan - Zehua Shao - Suhua Gao - Zhenzhen You - Shuai Huo - Zhu Zhang - Qiuhong Li - Saijun Zhou - Lei Yan - Fengmin Shao - Pei Yu journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC10031078 doi: 10.3389/fcvm.2023.1108538 license: CC BY 4.0 --- # Clinical characteristics and risk factors of cardiac surgery associated-acute kidney injury progressed to chronic kidney disease in adults: A retrospective, observational cohort study ## Abstract ### Introduction To retrospectively investigate the clinical characteristics and risk factors of cardiac surgery associated-acute kidney injury (CS-AKI) progressed to chronic kidney disease (CKD) in adults and to evaluate the performance of clinical risk factor model for predicting CS-AKI to CKD. ### Methods In this retrospective, observational cohort study, we included patients who were hospitalized for CS-AKI without a prior CKD [estimated glomerular filtration rate (eGFR) < 60 ml · min−1·1.73 m−2] at Central China Fuwai Hospital from January 2018 to December 2020. Survived patients were followed up for 90 days, the endpoint was CS-AKI to CKD, and then divided them into two groups (with or without CS-AKI to CKD). The baseline data including demographics, comorbidities, renal function, and other laboratory parameters were compared between two groups. The logistic regression model was used to analyze the risk factors for CS-AKI to CKD. Finally, receiver operator characteristic (ROC) curve was drawn to evaluate the performance of the clinical risk factor model for predicting CS-AKI to CKD. ### Results We included 564 patients with CS-AKI (414 males, 150 females; age: 57.55 ± 11.86 years); 108 ($19.1\%$) patients progressed to new-onset CKD 90 days after CS-AKI. Patients with CS-AKI to CKD had a higher proportion of females, hypertension, diabetes, congestive heart failure, coronary heart disease, low baseline eGFR and hemoglobin level, higher serum creatinine level at discharge ($P \leq 0.05$) than those without CS-AKI to CKD. Multivariate logistic regression analysis revealed that female sex(OR = 3.478, $95\%$ CI: 1.844–6.559, $$P \leq 0.000$$), hypertension (OR = 1.835, $95\%$ CI: 1.046–3.220, $$P \leq 0.034$$), coronary heart disease (OR = 1.779, $95\%$ CI: 1.015–3.118, $$P \leq 0.044$$), congestive heart failure (OR = 1.908, $95\%$ CI: 1.124–3.239, $$P \leq 0.017$$), preoperative low baseline eGFR (OR = 0.956, $95\%$ CI: 0.938–0.975, $$P \leq 0.000$$), and higher serum creatinine level at discharge (OR = 1.109, $95\%$ CI: 1.014–1.024, $$P \leq 0.000$$) were independent risk factors for CS-AKI to CKD. The clinical risk prediction model including female sex, hypertension, coronary heart disease, congestive heart failure, preoperative low baseline eGFR, and higher serum creatinine level at discharge produced a moderate performance for predicting CS-AKI to CKD (area under ROC curve = 0.859, $95\%$ CI: 0.823–0.896). ### Conclusion Patients with CS-AKI are at high risk for new-onset CKD. Female sex, comorbidities, and eGFR can help identify patients with a high risk for CS-AKI to CKD. ## Introduction Acute kidney injury (AKI) is considered an emerging health problem due to its high incidence, mortality, and disability rate worldwide [1]. The incidence of AKI in hospitalized patients is $11.6\%$ [2]. Furthermore, AKI is associated with increased long-term poor outcomes such as chronic kidney disease (CKD) and end-stage renal disease (3–6). Cardiac surgery related-acute kidney injury (CS-AKI) is a common serious complication in patients after cardiac surgery. Moreover, AKI occurs in up to $30\%$ of patients who undergo cardiac surgery; even a slight increase in the postoperative serum creatinine level increases hospital costs and mortality [7]. A strong association exists between the severity of AKI and its later progression to CKD. Moreover, even a slight increase in the postoperative serum creatinine levels is significantly associated with an increased risk of subsequent CKD and CKD progression (8–11), which seriously affects the prognosis of patients who undergo cardiac surgery and is associated with adverse outcomes, such as prolonged hospital stay, progression to CKD, and even dialysis dependence [12]. Systematic evaluation of renal function recovery 3 months or even longer after the onset of AKI helps judge the prognosis of patients and early prevention and treatment of CKD [13, 14]. The current research on CS-AKI mainly focuses on the evaluation of risk factors, the establishment of prediction models, and the occurrence and short-term consequences of AKI (in-hospital death and hemodialysis). Few small-sample studies on the long-term prognosis of CS-AKI progression to CKD are available. Therefore, our study established a retrospective cohort of hospitalized patients with CS-AKI, took the progression to CKD 90 days after the occurrence of CS-AKI as the research endpoint, paid attention to the clinical characteristics and related risk factors of progression to CKD after the occurrence of CS-AKI, and established a clinical risk prediction model for CS-AKI progression to CKD. The evaluation of its predictive performance can help identify high-risk patients with CS-AKI progressing to CKD, provide early intervention for subsequent renal monitoring and treatment, and reduce the risk of the special group of patients who develop CKD after CS-AKI. ## Results Figure 1: Clinical prediction model (ROC curve) for the progression of chronic kidney disease in patients 90 days after CS-AKI. Note: The Goodness of Fit Hosmer-Lemeshow test, Chi-square value 5.693, $$P \leq 0.682$$, cutoff value = 0.226, np < cutoff = 401, np ≥ cutoff = 163. TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 ## Discussion The results of our study showed that $19.1\%$ of patients with CS-AKI developed CKD (eGFR < 60 ml · min−1 · 1.73 m−2) in 90 days after the occurrence of CS-AKI, which is consistent with the result of previous study [17]. Patients with CS-AKI progressing to CKD were older and had a higher proportion of females; hypertension, diabetes, congestive heart failure, and coronary heart disease; preoperative use of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, calcium ion channel blockers, and beta-blockers; and preoperative low baseline eGFR, preoperative low hemoglobin level, and higher serum creatinine level at discharge than those without CS-AKI progressing to CKD. Multivariate logistic regression analysis showed that female sex, preoperative hypertension, coronary heart disease, congestive heart failure, preoperative low baseline eGFR, and higher serum creatinine level at discharge were independent risk factors for CS-AKI progressing to CKD [18]. The clinical risk prediction model that included factors, such as older age; female sex; combined hypertension, diabetes, coronary heart disease, congestive heart failure; high mean arterial pressure on admission; preoperative use of beta-blockers; preoperative low hemoglobin level, preoperative low baseline eGFR, high blood creatinine level at discharge; and other factors, produced a moderate performance for predicting CS-AKI progression to CKD (area under receiver operator characteristic curve = 0.859, $95\%$ CI: 0.823–0.896, $$P \leq 0.000$$), which is similar to the results of previous studies [19, 20]. Our study showed that the female sex is a risk factor for the progression of CS-AKI to CKD [odds ratio (OR) = 3.478], considering the possible reason that the ability of females to withstand major surgeries may be lower than that of males. This is similar to the conclusion that women have a significantly higher risk of progression to CKD after AKI than men from the Kidney Int [21] and JAMA [14], even incorporating it into the predictive scoring system as a reference variable for the progression to CKD after AKI [14]. Next, the results of our study showed that preoperative complications such as hypertension, coronary heart disease, and congestive heart failure are risk factors for the progression to CKD after CS-AKI. The surgical procedures used in our study population mainly included coronary artery bypass grafting, valve replacement or coronary artery bypass grafting combined with valve replacement, aortic arch replacement for Stanford type A aortic dissection, and a small number of other cardiac surgeries. It should be noted that coronary heart disease and coronary artery bypass grafting, congestive heart failure and valve replacement, and hypertension and aortic dissection (Stanford type A) aortic arch replacement have a more direct association. Cardiac surgery in patients with congenital heart disease is associated with an increased risk of AKI and CKD [21]; some of these patients even have chronic heart failure. However, there are few reports in the literature regarding coronary heart disease and congestive heart failure as risk factors for CS-AKI progression to CKD. AKI is a common clinical complication associated with poor prognosis [22]. Furthermore, a meta-analysis of AKI issues in type I cardiorenal syndrome found that nearly $\frac{1}{4}$ of patients developed AKI, approximately $3\%$ required renal replacement therapy, patients with acute heart failure had the highest incidence of AKI, and cardiac surgery patients had the greatest impact on mortality [23]; however, there are no reports on long-term CKD. Our study is similar to previous ones in that the current literature or guidelines all believe that hypertension is a risk factor for the occurrence and development of CKD [24]. Moreover, regarding preoperative comorbidities, patients with diabetes have a higher risk of AKI and CKD [25]. The univariate logistic analysis results in our study showed that diabetes was a risk factor for CS-AKI to CKD; however, the results of the multivariate logistic analysis showed no statistical difference. The possible reason was that the patients included in the study might have both diabetes and coronary artery disease. There was a certain correlation between the two factors, but we still included diabetes as a risk factor in the final clinical risk prediction model; the prediction performance of this model was moderate. In addition, our study identified preoperative low baseline eGFR and higher serum creatinine levels at discharge as risk factors for the development of CS-AKI to CKD, which is consistent with the previous reports in the literature [14, 17, 20]. The baseline eGFR represents the patient's renal function reserve, and the serum creatinine level at discharge represents the patient's recovery after CS-AKI. The higher the serum creatinine level at discharge, the bigger the risk of developing CKD after 90 days after CS-AKI. Some large-scale clinical retrospective studies have confirmed that AKI significantly increases the risk of subsequent CKD and end-stage renal disease in patients [4, 26]. Therefore, KDIGO's 2012 AKI clinical practice guidelines pointed out that patients with AKI without CKD should also be regarded as a higher-risk group for CKD. It is recommended to evaluate the renal function of patients 90 days after the symptoms of AKI are relieved to determine whether there is a new development of CKD [15]. Our study observed that $19.1\%$ of patients developed CKD 90 days after the occurrence of CS-AKI, which was significantly higher than the $10.8\%$ of the general population in China [27]. Previous studies have similarly suggested that delayed AKI recognition is an independent risk factor for increased in-hospital mortality and that renal referral is an independent protective factor for AKI under-recognition and death [18]. Our study further confirms that even if CS-AKI recovers, there are still patients who progress to CKD after 90 days, indicating that special attention should be paid to the evaluation of renal function and timely intervention after 90 days and increase kidney referrals to reduce the risk of conversion to CKD in the CS-AKI population. Our study has the following advantages: First, the study population was relatively pure, including patients with CS-AKI; there are few clinical studies on the risk factors of CS-AKI progression to CKD, thus focusing on CS-AKI conversion to CKD has a special clinical guiding value. Second, this was a retrospective, observational cohort clinical study. The research endpoint of CS-AKI progression to CKD was formulated in accordance with international guidelines. The research team collected complete baseline data; preoperative, intraoperative, and postoperative data; and primary endpoint data to ensure the scientificity of the study and the reliability of the conclusions. ## Study limitations There are some limitations of this study: [1] In a retrospective clinical study, there are no 90-day follow-up data for some patients, and some patients have incomplete preoperative detection indicators, such as cystatin C, hemoglobin A1c, and N-terminal-pro hormone BNP; however, these indicators were not included for univariate and multivariate analyses in this study and [2] Patients without AKI after cardiac surgery were not included for comparison to analyze the occurrence of CKD in this population. ## Conclusion In conclusion, patients with CS-AKI are a higher risk group for new-onset CKD, and a comprehensive clinical risk prediction model consisting of sex, disease status, and preoperative eGFR can help identify high-risk patients with CS-AKI progressing to CKD; however, further perspective, multicenter, and long-term follow-up studies are needed to observe its long-term impact and predictive power on CKD. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee on Oct. 9, 2020 (No.136) in Henan Provincial People's Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions XF, ZS, and PY conceived the idea and design for the study. XF, ZS, SG, ZY, SH, QL, and LY collected and analyzed the data. XF and ZS drafted the manuscript. ZZ, LY, SZ, and FS provided guidance and suggestions throughout the entire study. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Rainfed assessment of foxtail millet (Setaria italica L. beauv) germplasms through genotyping and principal component analysis authors: - Divya Singh - Kapil Lawrence - Shailesh Marker - Indranil Bhattacharjee - Reena Lawrence - Ravish Choudhary - Sezai Ercisli - Rohini Karunakaran journal: Frontiers in Plant Science year: 2023 pmcid: PMC10031084 doi: 10.3389/fpls.2023.1017652 license: CC BY 4.0 --- # Rainfed assessment of foxtail millet (Setaria italica L. beauv) germplasms through genotyping and principal component analysis ## Abstract ### Introduction Foxtail millet (Setaria italica L. beauv) is an important crop in underdeveloped countries; however, yield levels are low. The use of varied germplasm in a breeding approach is critical for increasing productivity. Foxtail millet can be cultivated effectively in a wide range of environmental circumstances but it is best suited to hot and dry climates. ### Methods In the current study, multivariant traits were used to define 50 genotypes in the first year and 10 genotypes in the second year. The phenotypic correlations among all traits in the entire germplasm were assessed, and the data acquired for all quantitative characters were subjected to analysis of variance for augmented block design. Furthermore, WINDOWS STAT statistical software was used to carry out a principal component analysis (PCA). The presence of substantial variations in most symptoms was shown by analysis of variance. ### Results Genotypic coefficient of variation (GCV) projections for grain yields were the highest, followed by panicle lengths and biological yields. Plant height and leaf length had the highest PCV estimates, followed by leaf width. Low GCV and phenotypic coefficient of variation (PCV) were measured as leaf length and $50\%$ flowering in days. According to the PCV study, direct selection based on characters, panicle weight, test weight, and straw weight had a high and positive effect on grain yield per plant in both the rainy and summer seasons, indicating the true relationship between these characters and grain yield per plant, which aids indirect selection for these traits and thus improves grain yield per plant. Variability in foxtail millet germplasm enables plant breeders to effectively select appropriate donor lines for foxtail millet genetic improvement. ### Discussion Based on the average performance of genotypes considered superior in terms of grain yield components under Prayagraj agroclimatic conditions, the best five genotypes were: Kangni-7 (GS62), Kangni-1 (G5-14), Kangni-6 (GS-55), Kangni-5 (GS-389), and Kangni-4 (GS-368). ## Introduction Plant genetic resources (PGR) are the backbone of the agricultural system, playing a positive and distinguishing role in the development of new cultivars from the past to the present, including the restructuring of existing ones (Sapkota et al., 2016). Genes for such traits are typically available in wild animals and landraces, allowing for the development of genotypes that can endure biotic and abiotic pressures. The current study concentrated on the genetic diversity of wild crop relatives. Genetic diversity, endangered plant species, species diversity, ecosystem stability, global floristic diversity in food plants, genetic resources in India, wild collections of major crops, plant genetic resources vis-à-vis crop breeding emphasis, and conservation of plant genetic resources are among the information needed to develop a breeding plan for sustainable agriculture: foxtail millet is a C4 crop that is diploid (2n = 18) (Mohammadi and Prasanna, 2003; Aghaee-Sarbarzeh and Amini, 2012). Foxtail millet cultivation is currently restricted to a few pockets, and in some locations it has been replaced by other crops that require irrigation. Its high nutritional value, combined with its low water requirement, makes it a climate-resilient crop appropriate for production in dryland agricultural systems. It has a tiny genome, and its use as a model crop for bioenergy has generated much interest, with more groups working on it than ever before. This troop’s floral morphology and flowering behavior make it challenging to establish crosses between the desired parents. As a result, we have seen several published studies on creating strategies for crossing in foxtail millet to date. The experiment addresses floral biology, crossing procedures, and the generation of cytoplasmic male sterile (CMS) lines (Bhat et al., 2018). The main component of foxtail millet grain is starch. Aside from grain, protein and fats are found in significant proportions. There are also some free sugar and non-starchy carbohydrates (CSE, 2007). Starch is widely used as a raw material in a variety of sectors, including textile, food, pharmaceutical, and paper. Native starch has relatively few industrial applications. Physical, chemical, or enzymatic processes can be used to create modified starches with specified qualities for a variety of uses (Kim et al., 2010). Owing to the rapid expansion of foxtail millet improvement in recent decades, as in other crops, foxtail millet landraces have been replaced by current cultivars, resulting in a significant loss of genetic diversity. As a result, established techniques of maintaining and multiplying foxtail millet landraces must be reconsidered. This could give germplasm conservationists and breeders some insight into the domestication, evolution, selection, and preservation of the world’s oldest cereal crop (Ahmed et al., 2013). Foxtail millet is a promising source of micronutrients and protein compared with other cereals. Foxtail millet grain is (per 100 g) rich in protein ($12.3\%$), iron (2.8 mg), and calcium (31 mg) compared with rice ($7.9\%$ protein and 1.8 mg iron) according to the Millet Network of India (MINI). Additionally, they contain a high quantity of beta-carotene and have a higher proportion of non-starchy polysaccharides and dietary fiber. Foxtail millet releases sugars very slowly and thus has a low glycemic index (GI) and hence can be used in a therapeutic diet but its potential role as low GI food has remained unrealized and unexploited. The low glycemic index diet has been shown to reduce blood glucose levels (Ahmed et al., 2013). For selecting a new variety, GPB hybridization is one of the most efficient methods at present, with the ultimate goal of selecting a new variety (Islam, 2004). Appropriate parental line selection is the most important aspect of a dry lab experiment to improve the genetic recombination of potential breeds (Verma et al., 2018; Singh et al., 2019). Additionally, a vast number of morphologically documented germplasm studies are needed to determine the differences between all germplasm populations and their breeding potential. Breeders assessed a huge number of germplasm varieties, some of which may or may not have enough discriminatory power for germplasm selection, characterization, assessment, and management (Maji and Shaibu, 2012). If this is the case, then principal component analysis (PCA) can be used to determine parentage and reduce duplication in experimental data sets in which morphological and physiological variation occurs on a regular basis in GPB sciences (Singh and Verma, 2020). PCA is a multivariate statistical methodology that seeks to simplify and analyze the relationships between a large number of variants in terms of a relatively small number of variables or components while retaining all crucial information from the original genotype data set. ( Amy and Pritts, 1991; Adams, 1995). However, these genotypes of foxtail millets have not been systemically determined so far; therefore, the current investigation provides a detailed overview of the rainfed assessment of S. italica genotypes through genotyping and principal component analysis. ## Materials and methods The experimental material consisted of accessions from 2018 and 2019, including 50 germplasm accessions of foxtail millet from 2018 and 10 from 2019. These 50 germplasm accessions were collected from ICRISAT and NBPGR, New Delhi during Kharif 2018. For evaluation and characterization, these 50 germplasm accessions and three check varieties were grown in a randomized complete block design (RBD) at the Field Experimental Centre, Department of Genetics and Plant Biotechnology, SHUATS, Prayagraj, India. The selection of the 10 best genotypes from 2019 was based on the yield of 50 foxtail millet germplasm accessions. The characterization site, Naini Prayagraj, is located at 13° 05' N latitude and 77° 34' E longitude. The Centre is 924 m above mean sea level. The annual rainfall ranges from 528 to 1374.4 mm with a mean of 915.8 mm. The germplasm accession was divided into three blocks, each consisting of 46 accessions and four check varieties (ISE375, ISE1468, ISE132, and ISE376). Each accession was grown in a single row 3 m in length and spaced 30 cm apart, and plant-to-plant spacing within the row was 10 cm. After 15 days, the crop was supplied with the recommended dose of fertilizer (10 kg N and 20 kg P-05 ha as a basal dose and 10 kg N at the time of earthing up). Irrigation was not provided and crops only received rainwater and were protected from weeds, pests, and diseases. For all characters, except days to emergence and days to maturity, observations were made for five randomly selected plants in each entry of each replication. The phenotypic correlation coefficients were obtained using the formula proposed by Vinsonias [2018]. The phenotypic correlations of all traits in the complete germplasm were estimated, and numerous significant correlations were found. Data for all quantitative characters were collected and subjected to analysis of variance for augmented block design using the method described by Kempthorne [1957]. PCA was calculated for 15 quantitative traits to examine the relative value of various traits in capturing variation across the entire germplasm. The PCA was performed using WINDOWS STAT statistical software, as recommended by Fujita et al. [ 1996]. ## Results Accessions showed variability among the quantitative and qualitative characters studied. The genetic parameters of 10 genotypes for 15 characters of foxtail millet were observed. Genotypic variance was high for plant height and low for leaf width. Phenotypic variance was at maximum for plant height and at minimum for leaf width. GCV was at maximum for economic yield and at minimum for conductivity temperature and depth (CTD). PCV was at maximum for economic yield and at minimum for CID. Heritability was at maximum for days of $50\%$ flowering and at minimum for economic yield. Genetic advance (GA) was at maximum for plant height and at minimum for harvest index (Figures 1, 2). Ten accessions were used for calculations of genotypic and phenotypic coefficient of variation for different parameters in S. Italica (Singh et al., 2021). GCV and PCV ratios were highest for economic yield, while GCV and PCV ratios were very low for CTD. GCV and PCV ratios for all characters were as follows: days to $50\%$ flowering, 6.50 and 7.03; days to $70\%$ flowering, 5.68 and 5.88; plant height, 20.27 cm and 22.38 cm; leaf width, 10.10 cm and 13.06 cm; leaf length, 10.69 cm and 13.08 cm; pedicle length, 19.07 cm and 20.43 cm; panicle length, 23.97 cm and 25.25 cm; panicle weight, 32.77 g and 37.36 g; leaf area index, 25.23 and 28.86; stem girth, 8.42 cm and 10.31 cm; soil plant analysis development (SPAD), 7.01 and 10.38; CTD, 3.08 and 3.22; harvest index, $31.70\%$ and $40.77\%$; biological yield, 32.55 g and 38.37 g; and grain yield, 47.24 g and 82.29g. Figure 1: The genotypic and phenotypic coefficient of variation in 50 accessions for different parameters in S. italica. Figure 2: Histogram depicting heritability and genetic advance coefficient of variation for different parameters in S. italica. The heritability and GA ratio was high for the leaf area index and very low for economic yield and the harvest index. The heritability and GA ratios for all characters were as follows: days to $50\%$ flowering, 96.26 and 7.29; days to $70\%$ flowering, 93.31 and 7.29; plant height, 82.00 cm and 42.46 cm; leaf width, 59.86 cm and 0.22 cm; leaf length, 66.80 cm and 6.40 cm; peduncle length, 87.17 cm and 10.09 cm; panicle length, 90.13 cm and 8.10 cm; panicle weight, 76.96 gm and 1.59 gm; leaf area index, 76.43 and 37.96; stem girth, 66.63 cm and 0.39 cm; SPAD, 45.61 nm and 5.26 nm; CTD, 91.43°C and 2.12°C; harvest index, 60.47 gm and 0.21 gm; biological yield, 71.99 gm and 4.26 gm; and economic yield, 32.96 gm and 1.70 gm. Brunda et al. [ 2014] reported that different crops have contributed to the overall parameters which involve the GCV and PCV traits. ## Correlation analysis The genotypic and phenotypic correlation between yield and yield components and the interrelationships among them are estimated and presented in Tables 1, 2. The qualitative and quantitative characters of the 50 genotypes from 2018 were analyzed to help identify the 10 best genotypes. The same methodology was used to select the five best genotypes from the 10 from 2019. ## Genotypic correlation For the 50 genotypes from 2018, the following genotypic correlations were obtained: days to $70\%$ flowering had a $1\%$ significant genotypic correlation with days to $50\%$ flowering (0.466); grain yield had a $50\%$ significant genotypic correlation with leaf width (0.184 cm); grain yield had a $1\%$ significant genotypic correlation with biological yield (0.554); grain yield had a $1\%$ significant genotypic correlation with harvest index ($1.059\%$); and grain yield showed a negative genotypic correlation with SPAD (−0.403), CTD (−0,037), stem girth (−0.326 cm), and panicle length (−0.048 cm). For the 10 genotypes from 2019, the genotypic correlations are shown in Table 2 and were as follows: grain yield with plant height (0.915), peduncle length (0.568 cm), panicle length (0.551 cm), panicle weight (1.028 g), SPAD (0.609), harvest index ($1.062\%$), and biological yield (1.197 g) showed a $1\%$ significant genotypic correlation; grain yield with leaf width (−0.034 cm), stem girth (−0.275 cm), and CTD (−0.053) showed a negative genotypic correlation; and grain yield with days to $50\%$ flowering (0.109), days to $70\%$ flowering (0.102), plant height (0.015 cm), leaf length (0.249 cm), peduncle length (0.568 cm), panicle length (0.551 cm), leaf area index (1.028), SPAD (0.098), harvest index ($0.609\%$), biological yield (1.062 g), and grain yield (1.197 g) showed a positive genotypic correlation. ## Phenotypic correlation Phenotypic correlations were calculated among the 50 genotypes from 2018. There was $1\%$ significance with days to $70\%$ flowering, plant height, leaf area index, leaf length, leaf width, peduncle length, harvest index, biological yield, grain yield, peduncle weight, and stem girth. Grain yield with days to $70\%$ flowering (−0.015), panicle length (−0.036 cm), stem girth (−0.023 cm), CTD (0.043), and SPAD (0.150) showed a negative phenotypic correlation. Grain yield showed a significantly increased positive correlation with days to $50\%$ flowering (0.071), plant height (0.053 cm), leaf area index (0.099), leaf length (0.117 cm), leaf width (0.084 cm), peduncle length (0.024 cm), panicle weight (0.207 g), biological yield (0.297 g), harvest index ($0.506\%$), and grain yield (1.000 g). Grain yield showed an increased positive phenotypic correlation in 10 genotypes. The relationship pattern of grain yield with panicle weight, panicle length, leaf width, leaf length, leaf area index, and plant height was comparable at genotypic and phenotypic levels for the 50 genotypes from 2018. A profoundly huge positive affiliation was observed for grain yield per plant with panicle length, leaf width, leaf length, and leaf area index at both the genotypic and phenotypic levels. Comparative outcomes showed that grain yield per plant had a positively huge relationship at the two levels in terms of days to development, panicle length, panicle weight, plant stature, and test weight. Connection examinations likewise give provided data about the relationship between other plant attributes. Plant height had an exceptionally critical positive relationship with number of tillers and panicle length, which was in line with Nirmlakumari and Vetriventhan [2010], and furthermore with panicle width and panicle weight. Leaf area index and panicle weight showed an exceptionally critical positive relationship among themselves. Thus, the determination of both of these qualities increases the chances of improving the other characteristics; therefore, both attributes further improve grain yield. It is fascinating to note that stem girth shows a positively huge relationship with plant height and peduncle length. Phenotypic correlations in the 10 genotypes collected in 2019 are shown in Table 2. Economic yield showed a $5\%$ significant phenotypic correlation with plant height (0.457), a $1\%$ significant phenotypic correlation with panicle weight (0.615), harvest index (0.553), and biological yield (0.521), a negative phenotypic correlation with leaf width (−0.046), stem girth (−0.064), SPAD (−0.113), and CTD (−0.088), and a positive correlation with days to $50\%$ flowering (0.104), days to $70\%$ flowering (0.071), plant height (0.116), leaf length (0.091), peduncle length (0.304), panicle length (0.331), biological yield (0.521), and economic yield (1.000). ## Path coefficient analysis The direct and indirect effects of different yield components in grain yield were calculated through path coefficient analysis at genotypic and phenotypic levels and are shown in Tables 3, 4. The phenotypic and genotypic correlations reveal the extent and direction of association between different characters. These are in agreement with the results obtained by Brunda et al. [ 2015] in foxtail millet and suggest that selection for these traits indirectly improves grain yield. ## Genotypic path correlation Genotypic path correlation revealed a highly positive direct effect of panicle length. Days to $50\%$ flowering, plant height, leaf width, biological yield, economic yield, and harvest index showed a negative genotypic path coefficient analysis with plant height (−0.1375), leaf width (−0.1275), peduncle length (−0.0190), panicle length (−0.1401), panicle weight (−0.051), stem girth (−0.6868), CTD (−0.1372), and SPAD (0.3700). Harvest index showed a significant increase and positive correlation coefficient analysis with days to $50\%$ flowering (0.0979), days to $70\%$ flowering (0.2048), leaf area index (0.0218), leaf length (0.2456), biological yield (0.0109), and economic yield (0.7547). ## Phenotypic path correlation The phenotypic path in the 50 genotypes collected in 2018 is shown in Table 5 and revealed a highly positive direct effect of plant height, leaf length, leaf width, panicle length, and biological yield. Harvest index showed a negative phenotypic path correlation with plant height (−0.0630), leaf area index (−0.0032), leaf width (−0.0431), peduncle length (−0.0561), panicle length (−0.0988), stem girth (−0.0201), CTD (−0.281), SPAD (−0.1502), and biological yield (−0.2820), and a positive phenotypic path coefficient analysis with days to $50\%$ flowering (0.0419), days to $70\%$ flowering (0.0582), leaf length [0,0740], and panicle weight (0.0359). Table 5 \| Unnamed: 0 \| Days of 50% flowering \| Days of 70% flowering \| Leaf length(cm) \| Leaf width(cm) \| Panicle length(cm) \| Panicle weight(gm) \| Peduncle length(cm) \| Plant height(cm) \| SPAD \| Stem girth(cm) \| Biological yield(cm) \| CTD \| Harvest Index(%) \| Leaf area index \| Economic yield(gm) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Days of 50% flowering \| 0.123 \| 0.059 \| 0.044 \| 0.032 \| 0.037 \| 0.015 \| 0.051 \| 0.048 \| 0.039 \| 0.002 \| 0.008 \| 0.017 \| 0.010 \| 0.045 \| 0.085 \| \| Days of 70% flowering \| -0.012 \| -0.025 \| -0.002 \| -0.003 \| -0.006 \| -0.005 \| -0.006 \| -0.007 \| -0.003 \| 0.001 \| 0.002 \| -0.004 \| -0.004 \| -0.006 \| -0.007 \| \| Leaf length(cm) \| -0.008 \| -0.002 \| -0.023 \| -0.008 \| -0.008 \| -0.005 \| -0.007 \| -0.011 \| -0.002 \| -0.005 \| -0.001 \| 0.007 \| -0.003 \| -0.009 \| 0.140 \| \| Leaf width(cm) \| 0.073 \| 0.038 \| 0.092 \| 0.279 \| 0.103 \| 0.07 \| 0.058 \| 0.092 \| -0.033 \| 0.0 \| 0.087 \| -0.003 \| -0.025 \| 0.244 \| 0.151 \| \| Panicle length (cm) \| -0.014 \| -0.011 \| -0.016 \| -0.017 \| -0.047 \| -0.001 \| -0.012 \| -0.023 \| 0.005 \| -0.008 \| -0.001 \| -0.004 \| 0.007 \| -0.014 \| -0.032 \| \| Panicle weight (gm) \| -0.01 \| -0.017 \| -0.018 \| -0.02 \| -0.002 \| -0.081 \| -0.019 \| -0.031 \| 0.018 \| 0.0 \| -0.017 \| -0.001 \| -0.002 \| -0.024 \| 0.150 \| \| Peduncle length (cm) \| -0.01 \| -0.005 \| -0.007 \| -0.005 \| -0.006 \| -0.005 \| -0.023 \| -0.017 \| 0.001 \| -0.015 \| -0.002 \| -0.004 \| 0.003 \| -0.003 \| -0.022 \| \| Plant height (cm) \| 0.096 \| 0.069 \| 0.112 \| 0.083 \| 0.124 \| 0.097 \| 0.178 \| 0.25 \| 0.013 \| 0.111 \| 0.059 \| 0.015 \| -0.036 \| 0.075 \| 0.123 \| \| SPAD \| -0.061 \| -0.024 \| -0.019 \| 0.023 \| 0.019 \| 0.042 \| 0.005 \| -0.01 \| -0.192 \| -0.006 \| 0.016 \| 0.042 \| 0.072 \| 0.015 \| -0.369 \| \| Stem girth (cm) \| -0.003 \| 0.009 \| -0.044 \| 0.0 \| -0.031 \| 0.001 \| -0.126 \| -0.087 \| -0.006 \| -0.196 \| -0.001 \| 0.053 \| 0.059 \| 0.038 \| -0.150 \| \| Biological yield (cm) \| 0.043 \| -0.059 \| 0.019 \| 0.2 \| 0.019 \| 0.132 \| 0.062 \| 0.151 \| -0.055 \| 0.003 \| 0.644 \| 0.162 \| -0.201 \| 0.155 \| 0.422 \| \| CTD \| -0.039 \| -0.046 \| 0.079 \| 0.003 \| -0.021 \| -0.003 \| -0.048 \| -0.016 \| 0.061 \| 0.077 \| -0.071 \| -0.282 \| 0.023 \| 0.024 \| -0.021 \| \| Harvest index (gm) \| 0.052 \| 0.111 \| 0.081 \| -0.058 \| -0.091 \| 0.018 \| -0.091 \| -0.095 \| -0.245 \| -0.195 \| -0.203 \| -0.052 \| 0.651 \| -0.007 \| 0.557 \| \| Leaf area index \| -0.147 \| -0.103 \| -0.158 \| -0.357 \| -0.121 \| -0.122 \| -0.044 \| -0.122 \| 0.031 \| 0.079 \| -0.098 \| 0.034 \| 0.005 \| -0.408 \| 0.123 \| \| Economic yield (gm) \| 0.085 \| -0.007 \| 0.14 \| 0.151 \| -0.032 \| 0.15 \| -0.022 \| 0.123 \| -0.369 \| -0.15 \| 0.422 \| -0.021 \| 0.557 \| 0.123 \| 1.000 \| \| Partial R2 \| 0.01 \| 0.0 \| -0.003 \| 0.042 \| 0.002 \| -0.012 \| 0.001 \| 0.031 \| 0.071 \| 0.029 \| 0.272 \| 0.006 \| 0.363 \| -0.05 \| \| For the 10 genotypes collected in 2019, genotypic path coefficient analysis is shown in Table 6, which reveals the highly positive direct effect of harvest index, panicle weight, and plant height. Genotypic path coefficient analysis showed a positive genotypic path for days to $50\%$ flowering (2.364), leaf length (0.427), panicle length (0.741), and leaf area index (2.747). Biological yield showed a positive genotypic path coefficient analysis with days to $50\%$ flowering (0.234), days to $70\%$ flowering (0.296), stem girth (0.366), and CTD (0.236). The immediate and roundabout impacts of various yield segments on grain yield were determined through weight examination at phenotypic and genotypic levels. This examination uncovered the high and immediate impact of plant height, peduncle length, and leaf width on grain yield per plant in 50 germplasm assortments. This demonstrates a genuine connection between these characters with grain yield per plant and the direct determination of these attributes helps to improve grain yield per plant. Comparable investigations of grain yield were carried out at the genotypic and phenotypic level in terms of panicle weight, test weight, and straw weight. Weight examination showed that plant height and peduncle length significantly and immediately affected grain yield. This positive direct impact of plant stature and peduncle length on grain yield suggests that the biomass of a plant should be built up to increase yield. The weight investigation revealed that the immediate impact of plant height, leaf length, leaf width, and panicle length on grain yield was positive. For this characteristic to produce the desired results, it would seem that determination must be focused in a particular direction. Table 6 \| Unnamed: 0 \| Days of 50% flowering \| Days of 70% flowering \| Leaf length(cm) \| Leaf width(cm) \| Panicle length(cm) \| Panicle weight(gm) \| Peduncle length(cm) \| Plant height(cm) \| SPAD \| Stem girth(cm) \| Biological yield(cm) \| CTD \| Harvest index(%) \| Leaf area index \| Economic yield(gm) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Days of 50% flowering \| 0.007 \| 0.01 \| -0.002 \| -0.003 \| -0.001 \| 0.0 \| 0.0 \| -0.005 \| 0.003 \| -0.004 \| -0.004 \| -0.004 \| -0.002 \| 0.005 \| 0.111 \| \| Days of 70% flowering \| -0.04 \| -0.027 \| 0.013 \| 0.019 \| 0.002 \| -0.004 \| 0.002 \| 0.007 \| -0.018 \| 0.017 \| 0.019 \| 0.012 \| 0.009 \| -0.021 \| 0.145 \| \| Leaf length(cm) \| 0.014 \| 0.03 \| -0.063 \| -0.058 \| -0.026 \| 0.009 \| 0.014 \| 0.034 \| -0.002 \| -0.029 \| -0.026 \| -0.024 \| -0.035 \| 0.03 \| 0.443 \| \| Leaf width(cm) \| -0.114 \| -0.167 \| 0.222 \| 0.237 \| 0.119 \| 0.007 \| -0.015 \| -0.095 \| -0.007 \| 0.135 \| 0.148 \| 0.124 \| 0.145 \| -0.181 \| 0.436 \| \| Panicle length (cm) \| -0.017 \| -0.012 \| 0.07 \| 0.085 \| 0.17 \| 0.108 \| 0.041 \| -0.028 \| 0.087 \| 0.102 \| 0.111 \| 0.096 \| 0.149 \| -0.055 \| 0.811 \| \| Panicle weight (gm) \| -0.001 \| -0.005 \| 0.005 \| -0.001 \| -0.023 \| -0.036 \| -0.02 \| 0.003 \| -0.022 \| -0.005 \| -0.014 \| 0.0 \| -0.022 \| 0.013 \| 0.685 \| \| Peduncle length (cm) \| -0.001 \| 0.001 \| 0.003 \| 0.001 \| -0.003 \| -0.007 \| -0.013 \| -0.002 \| 0.0 \| -0.001 \| -0.004 \| 0.002 \| -0.005 \| -0.005 \| 0.226 \| \| Plant height (cm) \| -0.003 \| -0.001 \| -0.002 \| -0.002 \| -0.001 \| 0.0 \| 0.001 \| 0.004 \| 0.0 \| -0.001 \| 0.0 \| 0.0 \| -0.002 \| 0.0 \| -0.192 \| \| SPAD \| 0.235 \| 0.336 \| 0.012 \| -0.014 \| 0.254 \| 0.304 \| 0.002 \| 0.047 \| 0.494 \| -0.112 \| -0.087 \| -0.113 \| 0.179 \| 0.03 \| 0.789 \| \| Stem girth (cm) \| 0.111 \| 0.127 \| -0.091 \| -0.114 \| -0.12 \| -0.026 \| -0.012 \| 0.036 \| 0.045 \| -0.2 \| -0.209 \| -0.216 \| -0.1 \| 0.167 \| 0.097 \| \| Biological yield (cm) \| -0.024 \| -0.027 \| 0.016 \| 0.024 \| 0.025 \| 0.015 \| 0.011 \| -0.004 \| -0.007 \| 0.04 \| 0.039 \| 0.034 \| 0.023 \| -0.04 \| 0.184 \| \| CTD \| 0.054 \| 0.036 \| -0.032 \| -0.045 \| -0.048 \| -0.001 \| 0.011 \| -0.001 \| 0.019 \| -0.092 \| -0.076 \| -0.085 \| -0.025 \| 0.081 \| -0.044 \| \| Harvest index (gm) \| -0.129 \| -0.176 \| 0.303 \| 0.326 \| 0.469 \| 0.325 \| 0.192 \| -0.188 \| 0.193 \| 0.267 \| 0.315 \| 0.154 \| 0.534 \| -0.152 \| 0.842 \| \| Leaf area index \| 0.018 \| 0.019 \| -0.012 \| -0.019 \| -0.008 \| -0.009 \| 0.01 \| -0.001 \| 0.002 \| -0.021 \| -0.026 \| -0.024 \| -0.007 \| 0.025 \| -0.102 \| \| Economic yield (gm) \| 0.111 \| 0.145 \| 0.443 \| 0.436 \| 0.811 \| 0.685 \| 0.226 \| -0.192 \| 0.789 \| 0.097 \| 0.184 \| -0.044 \| 0.842 \| -0.102 \| 1.0 \| \| Partial R2 \| 0.001 \| -0.004 \| -0.028 \| 0.103 \| 0.138 \| -0.025 \| -0.003 \| -0.001 \| 0.39 \| -0.02 \| 0.007 \| 0.004 \| 0.45 \| -0.003 \| \| The immediate impact of days to $70\%$ flowering on grain yield was low and negative in both genotype and phenotype. The immediate impact of stem girth on grain yield was positive and low, which corroborates the findings of Nirmlakumari and Vetriventhan [2010]. This positive direct impact of plant height on grain yield is attractive as it offers a way to increase straw and grain yield. The immediate impact of test weight on grain yield per plant was positive and high in both seasons, which demonstrates the genuine relationship between these attributes and a straightforward method for increasing grain yield. There is a tendency to believe that the determination of panicle length in foxtail millet will lead to plant height, stem girth, and leaf length being targeted to expand grain yield per plant. In light of the consequences of the weight examination, there is a tendency to infer that increasing characters such as panicle length, plant height, and stem girth, which had a positive connection with and direct impact on yield, will improve yield. Henceforth, lavish plants with enormous panicles, increased grain weight, and high panicle weight might bring about a better return in genotypes of foxtail millet. The phenotypic path in the 10 genotypes collected in 2019 is shown in Table 7 and revealed a highly positive direct effect of plant height, harvest index, and biological yield. Biological yield showed a positive phenotypic path with plant height (0.037), leaf width (0.012), leaf length (0.031), pedicle length (-0.034), panicle length (0.036), panicle weight (0.052), and SPAD (0.027) and a negative phenotypic path with days to $70\%$ flowering (−0.013), days to $50\%$ flowering (−0.016), stem girth (−0.016), CTD (−0.011), harvest index (0.013), and biological yield. ( 0.016). Path analysis indicated that plant height and harvest index had a highly positive direct effect on grain yield (Table 8). This positive direct effect of plant height and biological yield on economic yield provides scope to increase the biomass of plants with increased yield. ## Principal component analysis PCA reduces a very large series of data into a smaller number of components by looking for groups with very strong intercorrelations with a set of variables, and each component is explained as a percentage of variation to the overall variability. The first main component explains most of the overall population variation, followed by subsequent components for huge data, PCA was used to reduce the multivariate data to determine the importance and contribution of each component to the total variance. From the data shown in Figure 3, total variation could be $100\%$ explained by 15 principal components (PCs). The PC eigenvalues of 50 genotypes were calculated and represented F1 to F15. F1 was the largest contributing PC, followed by F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, F12, F13, F14, and F15. PC F1 was the most important contributing character, with an eigen value of 3.590, variability of $23.931\%$, and a cumulative variance of $23.931\%$. For the entire germplasm, PC F1 mainly separated accessions with the following 14 traits: days of $50\%$ flowering (0.324), days of $70\%$ flowering (0.231), leaf length (0.318), leaf width (0.362), panicle length (0.291), panicle weight (0.242), peduncle length (0.327), plant height (0.416), SPAD (0.004), stem girth (0.128), biological yield (0.167), CTD (0.039), economic yield (0.103), and leaf area index (0.360), which had the highest loadings in PC F1, indicating the significant importance of this component. These traits explained the largest portion of the variability. Figure 3: Phenotypic path diagram for grain yield of 15 yield component in 50 foxtail millet accessions collected in 2018. The results of the PCA are shown in Figures 3–6. PC F2 had an eigenvalue of 2.069, a variability of $13.794\%$, and a cumulative variability of $37.725\%$. PC F2 mainly separates accessions with eight traits: leaf length (0.005), leaf width (0.194), panicle weight (0.155), biological yield (0.190), CTD (0.044), economic yield (0.499), harvest index (0.416), and leaf area index (0.253), indicating their significant importance for these components. The remaining characters contributed negatively to the first component. The main traits for PC F3 were days of $50\%$ flowering (0.230), days of $70\%$ flowering (0.261), leaf length (0.225), panicle length (0.018), peduncle length (0.064), plant height (0.017), SPAD (0.073), stem girth (0.108), economic yield (0.165), and harvest Index (0.557). PC F4 had an eigenvalue of 1.512, a variability of $10.080\%$, and a cumulative variability (CV) of $58.221\%$. PC F5 had an eigenvalue of 1.235, a variability of $8.236\%$, and a CV of $66.457\%$. PC F6 had an eigenvalue of 1.050, a variability of $7.002\%$, and a CV of $73.458\%$. PC F7 had an eigenvalue of 0.902, a variability of $6.014\%$, and a CV of $79.473\%$. PC F8 had an eigenvalue of 0.798, a variability of $5.323\%$, and a CV of $84.796\%$. PC F9 had an eigenvalue of 0.671, a variability of $4.471\%$, and a CV of $89.267\%$. PC F10 had an eigenvalue of 0.460, a variability of $3.065\%$, and a CV of $92.332\%$. PC F11 had an eigenvalue of 0.419, a variability of $2.795\%$, and a CV of $95.128\%$. PC F12 had an eigenvalue of 0.323, a variability of $2.156\%$, and a CV of $97.284\%$. PC F13 had an eigen value of 0.204, a variability of $1.360\%$, and a CV of $98.643\%$. PC F14 had an eigenvalue of 0.109, a variability of $0.729\%$, and a CV of $99.373\%$. For the PCA for the entire germplasm, six traits (days to $50\%$ flowering, plant height, peduncle length, panicle weight, leaf length, and economic yield) explained most of the variance in the first five principal components, indicating their importance for the characterization of foxtail millet germplasm accessions. Figure 4: Phenotypic path diagram for grain yield of 15 yield component in 10 foxtail millet accessions collected in 2019. Figure 5: PCA scree plot series 1 and 2 for 50 foxtail millet accessions collected in 2018. Figure 6: PCA scree plot series 1 and 2 for 10 foxtail millet accessions collected in 2019. The principal component analysis featured the eigenvalue, variability (%), and cumulative variability (%) with respect to principal components F1 – F9. Component F1 was the largest contributing principal component followed by F2, F3, F4, F5, F6, F7, F8, and F9. PC F1 had the highest eigenvalue (5.839), with a variability of $38.926\%$ and cumulative variability of $38.926\%$. PC F2 had the second highest eigenvalue (3.613), with a variability of $24.087\%$, and a cumulative variability of $63.013\%$. PC F3 had the third highest values, followed by F4 and F5. PCs F9 and F8 had the lowest eigenvalues. PC F9 had an eigenvalue of 0.031, a variability of $0.204\%$, and a cumulative variability of $100.00\%$. PC F8 had an eigenvalue of 0.091, a variability of $0.607\%$, and a cumulative variability of $99.796\%$. PC F6 and F7 had moderate ECV values (Table 4). In the scree plot, the red line represents cumulative variability (%) with respect to PCs F1 to F9. In the biplot graph, the PCA in general confirmed the groupings, which were obtained through cluster analysis. The results of PCA are shown in Figure 7. The first two PCs with an eigenvalue of >1 accounted for $63.013\%$ of the total variance. Accessions GS-14 and GS-62 had more PCA value than the other genotype principal components. Figure 7: PCA biplot series 1 and 2 for 10 foxtail millet accessions collected in 2019. The breeding of high-yielding varieties is dependent on the yield-contributing morphological features, and we chose a small number of key traits with a favorable association. F lag leaf area, plant height, peduncle length, and tiller count per plant are major morphological yield contributing factors that are positively connected with yield per plant (Eberhart and Russel, 1966). This experiment suggested that high yielding foxtail millet accessions can be selected through indirect selection of panicle length, panicle weight, stem girth, and economic yield. The accessions GS-14 and GS-62 demonstrated the best performance for the majority of yield-related parameters, and hence can be relevant for further investigation in other regions of Uttar Pradesh similar to the Naini regions. ## Discussion The short-term strategy for identifying foxtail millet genotypes rich in grain nutrients to fulfil the urgent requirement of target micronutrient and protein-deficient populations is to analyze, detect, and explore existing genetic diversity. Significant heterogeneity in all grain nutrients was identified in the foxtail millet core collection, implying that there is plenty of room for selecting nutrient-rich accessions for use in breeding approaches. Field trials were carried out at a variety of sites and across two seasons (2018 and 2019). Estimates of variability, heritability, genetic advance, genotypic correlation coefficient, phenotypic correlation coefficient, genotypic route, and phenotypic path were obtained from the data. Significant differences were observed in Kharif Season-2018 to Kharif Season-2019 among the genotypes for all the characters studied. The results showed that analysis of variance revealed significant differences for most of the traits, including days to $50\%$ flowering, days to $75\%$ maturity, plant height, leaf length, leaf width, leaf area index, panicle length, panicle weight, biological yield, economic yield, harvest index, and test weight, indicating that all genotypes were genetically diverse for most of the traits. GCV estimations for grain yield were the highest, followed by panicle length and biological yield. Leaf length had the highest PCV estimates, followed by plant height and leaf width. Leaf length had the highest heritability, followed by pedicle length. Biological yield, economic yield, and agricultural yield all showed significant genetic progress. H arvest index, leaf breadth, panicle weight, panicle length, and leaf area index were all moderately recorded. L ow GCV and PCV were recorded in leaf length and days to $50\%$ flowering. In conclusion, the genotypes Kangni-1, Kangni-7, Kangni-6, Kangni-5, and Kangni-4 showed the best mean performance in the agroclimatic conditions of Allahabad. The direct influence of biological yield on economic yield per plant was positive and high in both years, which indicates that this feature has a true link and that direct selection using this attribute will be effective. In foxtail millet, the direct selection of biological yield resulted in the simultaneous indirect selection of several panicles, panicle length, pedicle length, panicle weight, number of productive tillers, and biological yield for higher economic production per plant. Seed yield per plant was found to be positively and significantly linked with biological yield, panicle weight, harvest index, leaf length, leaf area index, leaf breadth, plant height, days to flowering, and days to maturity. This suggests that these traits are mostly driven by additive gene action, and thus direct selection for these traits will result in increased grain yield. Similar results were reported by Vinsonias [2018] for plant height and panicle length for plant height for 1,000 g grain weight and flag leaf blade length (Brunda et al., 2015). Characters such as leaf length, days to $50\%$ flowering, and days to $75\%$ maturity demonstrated high heritability combined with moderate genetic advance, indicating that there is a greater chance of inheritance from progeny to offspring, and thus these characters should be prioritized for effective selection. Earlier studies have also reported a significantly positive association of biological yield per plant with productive panicle and peduncle length (Brunda et al., 2014; Kumar et al., 2015; Kavya et al., 2017). The positive correlation of yield with other characters indicated that all these characters could be simultaneously improved and that an increase in any one of them would lead to an improvement of other characters. Selection criteria should consider all these characters for to improve biological yield in foxtail millet. The PCA data reduction technique extracts the most important information from the data table (Islam, 2004), compresses the size of the data set by keeping only the important information (Maji and Shaibu, 2012), simplifies the description of the data set (Adams, 1995), and analyzes the structure of the observations and the variables (Amy and Pritts, 1991). Often, only the important information needs to be extracted from a data matrix, and the number of components that are needed should be considered. This problem can be overcome by using some guidelines. The first procedure is to plot the eigenvalues according to their size and to see whether there is a point in the graph (elbow) such that the slope of the graph goes from steep to flat and keep only the components that occur before the elbow. This procedure is called the scree or elbow test (Cattell, 1966; Jolliffe, 2002). Germplasm evaluation and characterization for plant breeders and multivariate statistical analysis estimate the genotypic and phenotypic parameters. The characteristics described in the list of pre-harvest and post-harvest observations were used for selecting the five best genotypes. PCV values were higher than GCV values, which indicates the effect of the environment on the expression of characters. These results are based on data for 2 years. The genotypes Kangni-1 (GS-14), Kangni-7 (GPF-7), Kangni-6 (GS-55), Kangni-5 (GS-389), and Kangni-4 (GS-368) cannot be found anywhere except SHUATS. That is why these genotypes are named by SHUATS. These five best genotypes will be further analyzed through biochemical trait analysis (Singh et al., 2022). ## Conclusion The present study found substantial diversity in the 50 genotypes of foxtail millet investigated for several agro-morphological variables that might be exploited efficiently in crop improvement approaches for diverse traits. According to the findings of this study, plant height and leaf length had the highest PCV estimates, followed by leaf width. Leaf length and $50\%$ flowering in days determines the low GCV and PCV. Furthermore, direct selection based on panicle weight, test weight, and straw weight had a high and positive effect on grain yield per plant in both the rainy and summer seasons, indicating the true relationship between these characters and grain yield per plant, which aids indirect selection for these traits and thus improves grain yield per plant. The top five genotypes were therefore chosen using the pre-harvest and post-harvest attribute observation list. Based on the average performance of the best genotypes in terms of grain yield components in the agroclimatic conditions of Prayagraj, the best five genotypes were Kangni-1 (GS-14), Kangni-7 (GPF-7), Kangni-6 (GS-55), Kangni-5 (GS-389), and Kangni-4 (GS-368). As these findings are based on 2 years of data, biochemical testing of these genotypes validated their consistency. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement All the genotypes were obtained from the NBPGR and ICRICIT for this project by the Directorate of Research at SHUATS. ## Author contributions Conceptualization and methodology, DS and KL; Data curation, DS, KL, and SM; Investigation, DS; Writing—original draft, DS and KL; Writing—review and editing, DS, KL, SM, IB, RL, SE, RC, and RK. All authors have read and agreed the submitted manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Adams M. W.. An estimate of homogeneity in crop plants with special reference to genetic vulnerability in dry season. phseolus vulgaris. Ephytica. (1995) 26 665-679 2. Aghaee-Sarbarzeh M., Amini A.. Genetic variability for agronomy traits in bread wheat genotype collection of Iran. Seed. Plant Improv. J. (2012) 1 581-599 3. Ahmed S. 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--- title: 'Association between hemoglobin glycation index and diabetic kidney disease in type 2 diabetes mellitus in China: A cross- sectional inpatient study' authors: - Sixu Xin - Xin Zhao - Jiaxiang Ding - Xiaomei Zhang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031087 doi: 10.3389/fendo.2023.1108061 license: CC BY 4.0 --- # Association between hemoglobin glycation index and diabetic kidney disease in type 2 diabetes mellitus in China: A cross- sectional inpatient study ## Abstract ### Objective To investigate the association between Hemoglobin Glycation Index (HGI) and Diabetic Kidney Disease (DKD) in Chinese type 2 diabetic individuals and to construct a risk score based on HGI to predict a person’s risk of DKD. ### Methods We retrospectively analyzed 1622 patients with type 2 diabetes mellitus (T2DM). HGI was obtained by calculating the fasting plasma glucose (FPG) level into the formula, and they were grouped into low HGI group (L-HGI), medium HGI group (H-HGI) and high HGI group (H-HGI) according to tri-sectional quantile of HGI. The occurrence of DKD was analyzed in patients with different levels of HGI. Multivariate logistics regression analysis was used to analyze the risk factors of DKD in patients with T2DM. ### Results A total of 1622 patients with T2DM were enrolled in the study. Among them, 390 cases were DKD. The prevalence of DKD among the three groups was $16.6\%$, $24.2\%$ and $31.3\%$. The difference was statistically significant ($$P \leq 0.000$$). There were significant differences in age ($$P \leq 0.033$$), T2DM duration ($$P \leq 0.005$$), systolic blood pressure (SBP) ($$P \leq 0.003$$), glycosylated hemoglobin (HbA1c) ($$P \leq 0.000$$), FPG ($$P \leq 0.032$$), 2-hour postprandial plasma glucose (2h-PPG) ($$P \leq 0.000$$), fasting C-peptide FCP ($$P \leq 0.000$$), 2-hour postprandial C-peptide (2h-CP) ($$P \leq 0.000$$), total cholesterol (TC) ($$P \leq 0.003$$), low density lipoprotein cholesterol (LDL-C) ($$P \leq 0.000$$), serum creatinine (sCr) ($$P \leq 0.001$$), estimated glomerular filtration rate (eGFR) ($$P \leq 0.000$$) among the three groups. Mantel-Haenszel chi-square test showed that there was a linear relationship between HGI and DKD (x2=177.469, $p \leq 0.001$). Pearson correlation analysis showed that with the increase of HGI level the prevalence of DKD was increasing ($R = 0.445$, $$P \leq 0.000$$). It was indicated by univariate logistic regression analysis that individuals in H-HGI was more likely to develop DKD (OR: 2.283, $95\%$ CI: 1.708~ 3.052) when compared with L-HGI. Adjusted to multiple factors, this trend still remained significant (OR: 2.660, $95\%$ CI: 1.935~ 3.657). The combined DKD risk score based on HGI resulted in an area under the receiver operator characteristic curve (AUROC) of 0.702. ### Conclusions High HGI is associated with an increased risk of DKD. DKD risk score may be used as one of the risk predictors of DKD in type 2 diabetic population. ## Introduction Diabetic kidney disease (DKD) is an important microvascular complication of diabetes and has become the main cause of chronic kidney disease (CKD) and end stage renal disease (ESRD) (1–3). Early detection of DKD and conducting the most effective targeted intervention are the key steps to offset the development of adverse clinical outcomes of diabetes mellitus (DM). Glycated hemoglobin (HbA1c) level is recommended as the gold standard method to evaluate glycemic control in DM patients. Despite the generally acknowledged role of HbA1c in the management of patients with diabetes, considerable differences in HbA1c exist even in patients with similar mean blood glucose (MBG) profiles [4, 5]. Studies have shown that some individuals have persistently higher or lower HbA1c levels than expected. However, recent researches had shown that considerable biological variation of HbA1c was not only affected by blood glucose levels but also influenced by interindividual biological differences and environmental factor [6, 7]. That means, even at the same blood glucose level, the level of HbA1c could be different. Therefore, solely relying on HbA1c level to evaluate the risk of DM is not suitable for all populations, which will produce a significant deviation. Hempe et al. [ 8] described this discrepancy by hemoglobin glycation index (HGI), which was calculated as the difference between an individual’s observed HbA1c and the estimated HbA1c. HGI can identify people with HbA1c levels that are higher or lower than average compared to other people with the same blood glucose concentration [8, 9]. It has been found that HGI could promote the development of some microvascular and macrovascular complications in DM patients [10]. A recent meta-analysis showed that increased HbA1c variability was associated with increased risk of all-cause mortality, cardiovascular disease (CVD), renal disease, peripheral neuropathy in patients with type 2 diabetes mellitus (T2DM) [11]. In the Diabetes Control and Complications Trial (DCCT), a high HGI at the baseline was a predictor of CKD and retinopathy in patients with T1DM after 7 years of follow-up [12]. The individual variation in HbA1c observed in the DCCT was attributable to biological variation and not measurement error. In the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial, a high HGI at the baseline predicted major microvascular events, which comprised new or worsened nephropathy or retinopathy in T2DM [13]. Chih-Hung Lin et al. [ 14] found that HGI independently predicted renal function deterioration in patients with T2DM and a low CKD risk. Although cumulative evidence suggests a role for HGI in diabetes complications, Lachin et al. [ 15] had contradictory findings on reassessment of HGI for prediction of microvascular complications in the DCCT. They concluded that HGI was not a useful predictor for microvascular complications because it is not statistically independent of HbA1c. Although the above-mentioned studies have shed light on the potential application of HGI in the management of diabetic complications, the evidence for HGI as a predictor of DKD remains unclear. Therefore, this study intends to analyze the relationship between HGI level and the risk of DKD in Chinese patients with T2DM and to construct a risk score to conveniently predict a person’s risk of DKD aiming to provide a new reference for clinical evaluation of diabetic complications. ## Participants In this retrospective study, 1622 T2DM who were hospitalized in Peking University International Hospital endocrinology department from March 2015 to April 2021 were analyzed. Among them, 1016 ($62.64\%$) were males and 606 ($37.36\%$) were females, with an average age of (55.8 ± 13.47) years. The average duration of T2DM was 9.31 ± 7.73 years. All subjects met the T2DM diagnostic criteria of the World Health Organization (WHO) in 1999 [16]. According to the diagnostic criteria of DKD [17], the subjects were divided into 1232 cases of non-DKD and 390 cases of DKD. The exclusion criteria included: [1] Other type of diabetes mellitus; [2] Acute complications of diabetes; [3] With primary renal parenchyma; [4] Recent urinary tract infection, taking drugs that affect renal function, etc.; [ 5] With severe anemia or blood loss; [6] Pregnant and lactating women; [7] Patients who were hospitalized for twice or more times. ## General conditions collected All participants’ age, date of birth and diabetic duration (unit by year) were collected and recorded. All participants were asked to take off their shoes and socks and wear light and thin clothes, following which height (cm) and weight (kg) were measured with measuring instrument, and body mass index (BMI) was obtained according to the formula weight/height2 (kg/m2). Blood pressure including systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in all participants. ## Laboratory measurement All subjects were asked to fast for at least 8 hours, and venous blood samples were collected in the morning. Chemiluminescence method was then used to test blood glucose and blood lipid profile. Other biochemical indices were then determined. High-pressure liquid chromatography was used to test HbA1c level. The tests were carried out in the biochemical laboratory of Peking University International Hospital. Laboratory measurements included fasting plasma glucose (FPG), 2 hour postprandial plasma glucose (2h-PPG), fasting C-Peptide (FCP), 2 hour C-Peptide (2h-CP), HbA1c, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), uric acid (UA), serum creatinine (sCr), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and urinary microalbumin/creatinine ratio (UACR). The estimated glomerular filtration rate (eGFR) was estimated according to the sCr level. ## HGI calculation Taking the actual measured value of HbA1c as the dependent variable and FPG as the independent variable, a linear regression equation was established as follows: predict HbA1c= 5.249 + 0.383* FPG ($r = 0.636$ and $p \leq 0.001$). Predicted HbA1c was then subtracted from the individual’s observed HbA1c to generate HGI (HGI = observed HbA1c – predicted HbA1c). HGI values were divided into three groups by using the tri quantile method: L-HGI, M-HGI and H-HGI (Figure 1). Figure 1: Correlation between HbA1c and FPG. We drove the predicted HbA1c, which was defined as follows: predict HbA1c= 5.249 + 0.383 FPG (r= 0.636 and p<0.001).* ## Statistical methods All data were processed by SPSS 22.0. Normal distribution data were shown as mean standard deviation (± s), and nonnormal distribution data were shown as mean median and quartile spacing. When quantitative data were normally distributed and variance was homogeneous, variance analysis was used for comparison among groups. When data were not normally distributed, variance analysis such as Kruskal Wallis test was used for comparison among multiple groups; qualitative data was expressed in percentage (%). Chi-square test was used to compare the qualitative data among the three groups. Pearson correlation analysis was used to analyze the correlation between HbA1c and FPG, and the regression equation was established accordingly. Logistic regression method was used for analysis of the main influencing factors of DKD with T2DM, and $p \leq 0.05$ was used for statistical significance. The AUROC was used to evaluate the sensitivity of the proposed risk score for the prediction of DKD. ## General characteristics among the 3 groups There were significant differences in age ($$P \leq 0.033$$), T2DM duration ($$P \leq 0.005$$), SBP ($$P \leq 0.003$$), HbA1c ($$P \leq 0.000$$), FPG ($$P \leq 0.032$$), 2h-PPG ($$P \leq 0.000$$), FCP ($$P \leq 0.000$$), 2h-CP ($$P \leq 0.000$$), TC ($$P \leq 0.003$$), LDL-C ($$P \leq 0.000$$), sCr ($$P \leq 0.001$$), eGFR ($$P \leq 0.000$$) among the three groups. There were no significant differences in sex ($$P \leq 0.299$$), DBP ($$P \leq 0.058$$), BMI ($$P \leq 0.274$$), TG ($$P \leq 0.932$$), HDL-C ($$P \leq 0.327$$), UA ($$P \leq 0.089$$), UACR ($$P \leq 0.111$$). The prevalence of DKD among the three groups was $16.60\%$, $24.20\%$ and $31.30\%$. The difference was statistically significant ($$P \leq 0.000$$). Mantel-Haenszel chi-square test showed that there was a linear relationship between HGI and DKD (x2 = 31.817, $$P \leq 0.000$$). Pearson correlation analysis showed that with the increase of HGI level the prevalence of DKD was increasing ($r = 0.140$, $$P \leq 0.000$$). ( Table 1, Figure 2) ## Binary logistic regression analysis of the relationship between DKD and related factors and HGI risk analysis in T2DM To investigate the potential interactions affecting the prevalence of DKD in T2DM, binary logistic regression analysis was performed as shown in Table 2. It was shown that age, T2DM duration, SBP, FCP, TC, eGFR, HGI were risk factors for DKD. Furthermore, in our study, HGI was found having a strong link with the incidence of DKD. Univariate logistic regression analysis showed that compared with L-HGI group, the risk of DKD in H-HGI group was significantly increased (OR: 2.283, $95\%$ CI: 1.708~ 3.052). After adjusting for age, T2DM duration, SBP, TC, FCP, eGFR, the risk of DKD in H-HGI group was 2.66 times than that in L-HGI group. ( Table 3) ## Construction of a risk score for DKD According to the results, the variables such as HGI, age, T2DM duration, SBP, TC, FCP, eGFR were the key risk factors ($P \leq 0.05$). We put them into the model, which determined the risk of DKD [DKD risk score =0.212* HGI + 0.042* T2DM duration (year) + 0.023* SBP (mmHg) + 0.164* FCP (ng/ml)– 0.017* age (year)- 0.024* eGFR (ml/min/1.73m2)- 2.301]. The area below the receiver operating characteristics (ROC) curve of this model was 0.702 ($95\%$CI: 0.671 - 0.734), which showed good discrimination ability. The sensitivity and specificity corresponding to the maximum Youden index were $0.640\%$ and $0.649\%$, respectively (Figure 3). Figure 3: Receiver operating characteristics curves of DKD risk score. We put HGI, age, T2DM duration, SBP, TC, FCP and eGFR into the model The area below the ROC curve of this model was 0.702 (95%CI: 0.671 - 0.734). The sensitivity and specificity corresponding to the maximum Youden index were 0.640% and 0.649% respectively. ## Discussion In this Cross- Sectional study of Chinese adults with T2DM, we found an association between HGI and incident DKD. A dramatic increase in DKD incidence was observed among subjects with higher values of HGI. Our findings suggested that HGI may be a useful predictor of incident DKD among patients with T2DM. HbA1c value is generally considered the gold standard method for evaluating glycemic control. However, in three large randomized controlled clinical trials (18–20), namely, the Action to Control Cardiovascular Risk in Patients with Diabetes (ACCORD), ADVANCE and the Veterans Affairs Diabetes Trial (VADT), intensive glycemic control in patients with T2DM did not benefit large blood vessels. Especially in the ACCORD trial, as compared with standard therapy, intensive therapy to target normal HbA1c levels for 3.5 years increased mortality and did not significantly reduce major cardiovascular events [21]. Accordingly, Sheng et al. [ 22] proposed a hypothesis that HbA1c variability may be related to all-cause mortality of intensive therapy, and conducted a post hoc analysis of the ACCORD trial. This study showed that long-term follow-up HbA1c variability was a strong predictor of all-cause mortality. Thus, HbA1c is not a one-size-fits-all indicator of blood glucose control. This phenomenon might be attributed to various biological factors, including genetic predisposition, erythrocyte turnover rates, intracellular glucose concentrations, intracellular or extracellular pH, lipid peroxides, inorganic phosphates, hemoglobin oxygenation status, cellular redox status, and the activity of non-enzymatic protein glycation [9, 23]. Increasing evidence is supporting the role of glucose variability (GV) in the development of diabetic complications [24]. Studies in recent years [25] have shown that HbA1c levels vary greatly among individuals, and some patients may have high or low HbA1c levels inconsistent with blood glucose control levels, which brings some difficulties to clinical prognosis assessment based on this indicator. HGI can identify people with HbA1c levels that are higher or lower than average compared to other people with the same blood glucose concentration [8, 9]. Biological sources of HGI variation include genetic and environmental factors that affect person-to-person variation in HbA1c or blood glucose. In our study, we also found that only $40.5\%$ of HbA1c variation can be explained by FPG. In addition, Sabanayagam C. et al. [ 26] designed a study to determine whether the relationship of HbA1c to diabetic microvascular complications showed any natural thresholds that could be useful in diagnosing diabetes. There data supported use of an HbA1c cut-off point of between 6.6 and $7.0\%$ in diagnosing diabetes. Any retinopathy, CKD, albuminuria and peripheral neuropathy were less well detected at these cut-off points. Our study also suggested that the incidence of DKD in L-HGI group [HbA1c (6.99 ± 1.34) %] is lowest, similar to previous studies. Before 2015, several studies shown a positive association between GV and diabetic complications, both macrovascular and microvascular [27]. Since 2015, new evidence has also emerged in support of GV as an independent risk factor for total mortality and death due to cardiovascular disease in both type 1 and type 2 diabetes (11, 28–32). R. J. McCarter et al. [ 12] concluded that individual biological variation in HbA1c, which is distinct from that attributable to mean blood glucose (MBG), was evident among type 1 diabetic patients in DCCT and was a strong predictor of risk for diabetic complications. At 7 years’ follow-up, patients in H-HGI had three times greater risk of retinopathy (30 vs. $9\%$, $P \leq 0.001$) and six times greater risk of nephropathy (6 vs$.1\%$, $P \leq 0.001$) compared with the L-HGI. The individual variation in HbA1c observed in DCCT was attributable to biological variation and not measurement error. Evidence of a link between biological variation in HbA1c and microvascular complications in DCCT suggested that factors responsible for biological variation in nonenzymatic HbA1c may also influence individual susceptibility to diabetic complications. Chih-Hung Lin et al. [ 14] found that a high HGI predicted rapid renal function decline without or with a resultant eGFR < 60 ml/min/1.73m2, but not onset of macroalbuminuria followed for a median of 7.3 years. Thus, HGI independently predicted renal function deterioration in patients with T2DM and a low CKD risk. In patients with T2DM, HbA1c variability affects CKD more than average HbA1c [33]. Even in nondiabetic individuals, studies have reported the effect on HGI and kidney dysfunction and CKD among the non-diabetic individuals and the adults with hypoglycemic drug naive prediabetes and diabetes. Teresa Vanessa Fiorentino et al. concluded that HGI may be a useful tool to identify nondiabetic individuals with an increased risk of having kidney dysfunction [34]. Wonjin Kim et al. found an association between HGI and incident CKD. High HGI was associated with an increased risk of incident CKD. Regardless of HbA1c value, subjects with higher values of HGI were at a higher risk of incident CKD during the 10-year follow-up period [35]. In the ACCORD population, baseline age, BMI, SBP, DBP, fasting serum glucose, TC and SCr had significant difference compared with low HbA1c variability, while sex and LDL-C had not [22]. Most of these results have been confirmed in our study but of BMI and LDL-C, suggesting that the influencing factor of HGI in T2DM is complex and that the causes are multifactorial. In addition, further logistic regression analysis showed that the age, T2DM duration, SBP, TC, FCP, 2h-CP, eGFR and HGI were the key risk factors of DKD in this study. The risk of DKD in T2DM patients with high HGI levels is 2.283 times higher than those with low HGI levels. Adjusted to multiple factors, this trend still remained significant (OR: 2.660, $95\%$CI: 1.935~ 3.657). Furthermore, we constructed a risk score based on HGI to predict a person’s risk of DKD, which could be useful for the clinician. At present, the mechanism of HGI on DKD in T2DM is still unclear. The possible mechanisms involve the following aspects: [1] Fluctuation of blood glucose leads to increased oxidative stress, production of inflammatory cytokines and endothelial dysfunction [36, 37]. Compared with persistent hyperglycemia, islet β-cell dysfunction and apoptosis increased significantly in the state of blood glucose fluctuation which leading to decrease of insulin secretion [38]. [ 2] Fluctuated blood glucose deteriorated the progression of DKD by increasing the blood urea nitrogen and sCr, decreasing creatinine clearance, and accelerating renal ultrastructural injury. This adverse result was probably due to its promoting oxidative stress activity and the p-AKT signaling pathway inhibition, which activated its downstream proteins, resulting in severe renal injury [39]. [ 3] One possible explanation is that even periods of sustained hyperglycemia are “remembered,” thus conferring an increased risk of microvascular complications [40, 41], hence, the detrimental effect of HbA1c variability may be mediated through the same mechanism underlying the “metabolic memory” phenomenon, including oxidative stress. [ 4] Because the risk of microvascular complications increases exponentially as HbA1c rises [42], subjects with higher HbA1c variability would “accumulate” a surplus of risk in the periods spent at the upper end of their HbA1c range. This hypothesis might be indirectly supported by GIUSEPPE PENNO’s observation that the effect of HbA1c variability is a statistically significant effect in the higher quartile of HbA1c-SD [33]. ## Conclusion To sum up, HGI may be a reference index for blood glucose control in T2DM patients and could predict the risk of DKD. This study brings important enlightenment for daily diabetes management, that is, diabetes patients should take into account the variability of HbA1c while controlling blood glucose or HbA1c levels. “ Beyond HbA1c” is an important concept of diabetes diagnosis and treatment at present [43]. Blood glucose, glycosylated albumin and HbA1c variability are important factors for blood glucose control and long-term prognosis. In order to reduce the risk of all-cause death in diabetic patients, measures should be taken as soon as possible to incorporate HbA1c variability into the management objectives of diabetic patients. This study also has some limitations. As the analysis was conducted on the basis of cross section, the existing database cannot collect relevant data at the time of diagnosis of diabetes. Unfortunately, previous studies have not reported it yet. Next, we will focus on collecting data of new-onset T2DM, and make further exploration on the impact of HGI on DKD. In addition, the subjects have not been followed up to determine the relationship between HGI and DKD in the existing cross-sectional study. However, according to the current research results, HGI has stable disease prediction value. Furthermore, high-quality and large sample prospective cohort studies and randomized controlled clinical trials and even cytological studies will be carried out to clarify the mechanism of HGI and the predictive value of HGI on DKD, and to develop a personalized HbA1c variability control target, so as to provide new reference indicators for clinical diabetic blood glucose control and reduce the occurrence of complications. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Biomedical Ethics Committee of Peking University International Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions XZhan, SX, XZhao and JD made substantial contributions to the conception and design of the study. SX and JD collected the data. SX and XZhao analyzed the data. SX and XZhan drafted the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients authors: - Peng Zhang - Honglin Chen - Bin Xie - Wenhua Zhao - Qi Shang - Jiahui He - Gengyang Shen - Xiang Yu - Zhida Zhang - Guangye Zhu - Guifeng Chen - Fuyong Yu - De Liang - Jingjing Tang - Jianchao Cui - Zhixiang Liu - Hui Ren - Xiaobing Jiang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031099 doi: 10.3389/fendo.2023.990078 license: CC BY 4.0 --- # Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients ## Abstract ### Background Postmenopausal osteoporosis (PMOP) is a common bone disorder. Existing study has confirmed the role of exosome in regulating RNA N6-methyladenosine (m6A) methylation as therapies in osteoporosis. However, it still stays unclear on the roles of m6A modulators derived from serum exosome in PMOP. A comprehensive evaluation on the roles of m6A modulators in the diagnostic biomarkers and subtype identification of PMOP on the basis of GSE56815 and GSE2208 datasets was carried out to investigate the molecular mechanisms of m6A modulators in PMOP. ### Methods We carried out a series of bioinformatics analyses including difference analysis to identify significant m6A modulators, m6A model construction of random forest, support vector machine and nomogram, m6A subtype consensus clustering, GO and KEGG enrichment analysis of differentially expressed genes (DEGs) between different m6A patterns, principal component analysis, and single sample gene set enrichment analysis (ssGSEA) for evaluation of immune cell infiltration, experimental validation of significant m6A modulators by real-time quantitative polymerase chain reaction (RT-qPCR), etc. ### Results In the current study, we authenticated 7 significant m6A modulators via difference analysis between normal and PMOP patients from GSE56815 and GSE2208 datasets. In order to predict the risk of PMOP, we adopted random forest model to identify 7 diagnostic m6A modulators, including FTO, FMR1, YTHDC2, HNRNPC, RBM15, RBM15B and WTAP. Then we selected the 7 diagnostic m6A modulators to construct a nomogram model, which could provide benefit with patients according to our subsequent decision curve analysis. We classified PMOP patients into 2 m6A subtypes (clusterA and clusterB) on the basis of the significant m6A modulators via a consensus clustering approach. In addition, principal component analysis was utilized to evaluate the m6A score of each sample for quantification of the m6A subgroups. The m6A scores of patients in clusterB were higher than those of patients in clusterA. Moreover, we observed that the patients in clusterA had close correlation with immature B cell and gamma delta T cell immunity while clusterB was linked to monocyte, neutrophil, CD56dim natural killer cell, and regulatory T cell immunity, which has close connection with osteoclast differentiation. Notably, m6A modulators detected by RT-qPCR showed generally consistent expression levels with the bioinformatics results. ### Conclusion In general, m6A modulators exert integral function in the pathological process of PMOP. Our study of m6A patterns may provide diagnostic biomarkers and immunotherapeutic strategies for future PMOP treatment. ## Introduction Postmenopausal osteoporosis (PMOP) is a common bone disorder associated with ageing occurring in postmenopausal women, which is resulted from bone mass decrease and structural changes in bone tissue due to estrogen deficiency, resulting in increased bone fragility and susceptibility to fracture, as well as pain, bone deformation, comorbidities and even death caused by fracture (1–3). It is reported that approximately $50\%$ of women experience at least one PMOP-related fracture [4]. Existing drugs including vitamin D, calcium, denosumab, teriparatide, and bisphosphonates serve as recommended therapies for the treatment of PMOP [5], but long-term use of them trigger some side effects causing rapid bone loss and increasing the risks of the jaw osteonecrosis, atypical femoral fractures, and multiple rebound-related vertebral fractures [6]. Therefore, PMOP still remains clinically not well managed [7]. PMOP seriously impacts the health and life quality of the elderly and even shortens their life expectancy, increasing the financial and social burden on the countries and the families [8]. Therefore, it is indispensable and critical to early identify patients at high risk of developing PMOP. Mounting evidence on the extensive developments in PMOP research shows that PMOP is a complicated disease of great heterogeneity that involves genetic changes [9]. Hence, early identification and effective prevention of high-risk patients from a genetic perspective will exert a profound influence on the epidemiological control of PMOP. Notably, recent studies have reported the promise of exosomes as potential therapies in osteoporosis [10, 11]. Exosomes are small single-membrane organelles between 40 and 160 nm in diameter [12], which can carry a variety of cargos, such as lipids, proteins, glycoconjugates, and nucleic acids [13]. Exosomes can transmit signals or molecules between cells and reshape the extracellular matrix by releasing these substances [14]. Moreover, exosome can carry circular RNAs (circRNAs) to regulate bone metabolism in PMOP via sponging microRNAs (miRNAs), which can control mRNA expression by regulate the interaction with m6A methylation [15]. N6-methyladenosine (m6A) is a widespread epigenetic modification that affects the variable splicing, translocation, translation and degradation of mRNA, as well as the epigenetic effects of certain non-coding RNAs [16]. As an essential epigenetic modification, m6A modification needs numerous regulatory proteins encoded by writers, erasers, and readers to coorperate together [17]. Abnormalities in m6A methylation can lead to a variety of diseases such as obesity, glioblastoma, acute myeloid leukaemia, type 2 diabetes, infertility, neuronal diseases, premature ovarian failure and various malignancies [18, 19]. With the further study on m6A, researchers also found that bone marrow mesenchymal stem cells (BMSCs), chondrocytes, osteoblasts, osteoclasts, osteosarcoma, and adipocytes cells are all subject to m6A modification to regulate the methylation of RNA in cells, affecting the transduction of mRNA and/or non-coding RNA associated genes, thus activating cellular signaling pathways and affecting cell cycle and DNA damage repair, which in turn determines the occurrence and development process of musculoskeletal disorders (20–24). Recently, existing researches have verified that m6A modifications exert vital functions on the pathology of PMOP via modulating the expression level of m6A-associated genes [25, 26]. However, it still stays unclear on the roles of m6A modulators derived from serum exosome in PMOP. In this study, we performed a comprehensive evaluation on the roles of m6A modulators in the diagnostic biomarkers and subtype identification of PMOP on the basis of GSE56815 and GSE2208 datasets with monocyte samples. We developed a PMOP susceptibility prediction gene model based on seven candidate m6A modulators including FMR1, FTO, WTAP, YTHDC2, HNRNPC, RBM15 and RBM15B, and found that the model provided good clinical benefits for patients. Our RT-qPCR experiments further validated these m6A modulators, exhibiting consistent expression levels with the bioinformatics results. Additionally, we excavated two different m6A patterns that were closely correlated with immature B cell, gamma delta T cell, CD56dim natural killer cell, monocyte, neutrophil and regulatory T cell immunity, indicating that m6A patterns may be used to identify PMOP and provide subsequent treatment strategies. Figure 1 displayed the flowchart of study design and process. Figure 1: Flow chart of the study design. ## Sample retrieval We collected monocyte samples separated from whole blood of elderly women by retrieving the GEO database (http://www.ncbi.nlm.nih.gov/geo/). The search terms were “BMD”, “Postmenopausal Osteoporosis”, “Gene expression”, “Microarray”, and the datasets were based on the following criteria: [1] each dataset includes at least 10 samples; [2] each dataset includes at least 5 cases in the groups of control and PMOP respectively; and [3] Both raw data and series matrix file can be obtained from the GEO datasets. Two datasets, GSE56815 [27] and GSE2208 [28] were eventually screened, which fully met our criteria. We chose 5 cases of control group and 5 cases of PMOP group from the dataset of GSE2208 as well as 20 cases of PMOP and 20 controls in GSE56815 dataset for subsequent analysis. Table 1 showed specific information of the corresponding datasets. Table 1 \| GEO Accession Total samples \| Selected samples Platform \| Source tissue \| \| --- \| --- \| --- \| \| 19 samples \| 10 samples \| blood monocytes \| \| GSE2208 Sample types: \| GPL96 Sample types: \| blood monocytes \| \| 10 high BMD \| 5 PreH BMD (Control) \| blood monocytes \| \| 9 low BMD \| 5 postL BMD (PMOP) \| blood monocytes \| \| 80 samples \| 40 samples \| blood monocytes \| \| GSE56815 Sample types: \| GPL96 Sample types: \| blood monocytes \| \| 40 high BMD \| 20 PreH BMD (Control) \| blood monocytes \| \| 40 low BMD \| 20 postL BMD (PMOP) \| blood monocytes \| ## Data acquisition We downloaded the annotated R package via Bioconductor (http://bioconductor.org/) to convert microarray probes to symbols in R (v4.1.2) software (Statistics Department of the University of Auckland, New Zealand). After data preparation, we carried out consolidation of the two datasets via SVA batch difference processing of combat and obtained the final dataset which contained 25 controls and 25 PMOP cases. Differential m6A madulators were identified from the dataset by difference analysis of control and PMOP cases using the R package of Limma. The screening thresholds to determine the significant m6A madulators were P-Value <0.05 and \|log2 fold change (FC)\| >0 [29]. ## Model construction We established random forest (RF) and support vector machine (SVM) models as training models to evaluate the PMOP occurrence, which were detected by “Reverse cumulative distribution of residual”, receiver operating characteristic (ROC) curve, and “Boxplots of residual”. In RF model, we used the R package of “RandomForest” to build an RF model to screen candidate m6A modulators with importance score (Mean Decrease Gini)>2. In SVM model, n stands for the number of m6A modulators and every data dot is presented as a dot in an n-dimensional space. We then selected an optimal hyperplane that distinguishes these two groups of control and PMOP very well [30]. We then used the R package of “rms” to establish a nomogram model to predict the prevalence of PMOP patients according to screened candidate m6A modulators. We utilized the calibration curve to assess how well our predicted values align with reality. We also carried out decision curve analysis (DCA) to draw a clinical impact curve and assess whether decisions based on the model produced benefit to patients [31]. ## Subtype classification Consensus clustering is a resampling-based algorithm that identifies each member and its subcluster number, and verifies the rationality of the clusters [31]. Using the R package of “ConsensusClusterPlus”, a consensus clustering method was conducted to identify different m6A patterns on the basis of significant m6A moderators [32]. ## Classification of differentially expressed genes between different m6a patterns and GO and KEGG enrichment analysis We utilized Limma package to identify differentially expressed genes (DEGs) between different m6A patterns with the threshold of adjusted P-Value <0.05 and \|log2 FC\| >0.5. Next, we used the R package of “clusterProfiler” to perform GO and KEGG analyses so as to investigate the possible mechanism of the DEGs involved in PMOP [33]. ## Calculation of the m6A score We utilized principal component analysis to calculate the m6A score for each sample for quantification of the m6A patterns, with the m6A score evaluated based on the following formula: m6A score = PC1i, where PC1 denotes principal component 1, and i denotes significant m6A gene expression [34]. ## Evaluation of immune cell infiltration We utilized single sample gene set enrichment analysis (ssGSEA) to evaluate the level of immune cell infiltration in the samples from PMOP groups. First, the gene expression levels in the samples were sequenced using ssGSEA to obtain a ranking of gene expression levels. Next, we searched for the significant m6A madulators in the input dataset and then summed their expression levels. According to these evaluations, we obtained the abundance of immune cells in each sample [35]. ## Experimental validation by RNA extraction and real-time quantitative polymerase chain reaction The clinical experiments involved in this paper were authorized by the Ethics Committee of the 1st Affiliated Hospital of GZUCM (No. K[2019]129). In the current research, all patients who participated in this trial provided informed consent at the beginning. Then, external venous blood was drawn from PMOP patients ($$n = 3$$) and healthy controls ($$n = 3$$) respectively. The two groups were age-matched. The manipulation of human peripheral blood monocytes (HPBMs) was performed as described previously [36]. First, whole blood from patients was put into a 50-mL centrifuge tube, then diluted with 10-mL PBS and gently mixed. Afterwards, we continuously centrifuged the initial blood specimen at 2000 rpm for 20 minutes. When centrifugation was finished, the blood sample was stratified and the leukocyte layer in the center of the sample containing HPBMs was aspirated by pipette and transferred to a single fresh 15 mL centrifuge tube in liquid with 10-15 mL of PBS. Next the solution was centrifuged at 1500 rpm in 10 min and the supernatant was lifted to precipitate and be the wanted HPBMs. HPBMs were inoculated in 6-well plates, and then 1mL of TRIzol reagent was applied to each well for total RNA extraction from the cells. Subsequently, retrotranscription of 1μg of total RNA was done using a cDNA synthesis kit (Takara Inc.Shiga, Japan). 20μL SYBR Green qPCR SuperMix (Takara Inc.) was used for detection of m6A cDNAs and RT-qPCR machine (Bio-Rad, Hercules, CA, USA). The thermal cycling conditions for the final gene amplification were: 95°C for 30s, 40 cycles of 95°C for 5s, and a final step of 60°C for 30s. Quantitative analysis was performed using the 2ΔΔCT method to calculation of the relative expression of each gene. The gene-related detection primers of m6A modulators were compounded by Shanghai Sangon Biotechnology Co.Ltd (China), as shown in Table 2. Table 2 \| m6A genes \| Sequence (5’->3’) \| Sequence (5’->3’).1 \| \| --- \| --- \| --- \| \| m6A genes \| Forward primer \| Reverse primer \| \| FTO \| ATTCTATCAGCAGTGGCAGC \| GGATGCGAGATACCGGAGTG \| \| FMR1 \| CCTGAACTCAAGGCTTGGCA \| TCTCTTCCTCTGTTGGAGCTTTA \| \| YTHDC2 \| ACGGGGACCAGAGAGAAATG \| TTGTTGAGTCGCCCACTTGT \| \| RBM15 \| ATGCCTTCCCACCTTGTGAG \| CAACCAGTTTTGCACGGACA \| \| WTAP \| GCTTCTGCCTGGAGAGGATT \| GTGTACTTGCCCTCCAAAGC \| ## Statistical analysis The correlations among writer, reader and eraser were evaluated via linear regression analyses. The differences between groups were calculated through Kruskal-Wallis tests in bioinformatics analysis, while unpaired t-tests with Welch’s correction were utilized in RT-qPCR data analysis. Two-tailed tests were conducted to estimate all parametric analyses with $P \leq 0.05$ considered as statistical significance. All results were expressed as mean ± standard deviation. ## Identification of the 12 m6A modulators in PMOP Totally 12 m6A modulators were identified based on difference analysis between controls and PMOP cases. These modulators included one eraser (FTO), five writers (METTL3, ZC3H13, RBM15B, WTAP, and RBM15), and six readers (YTHDC2, ELAVL1, FMR1, YTHDF3, HNRNPC, and IGFBP3). We finally filtrated 7 vital m6A modulators (HNRNPC, YTHDC2, FMR1, FTO, WTAP, RBM15B, and RBM15), which were visualized by a heat map and histogram. We observed that RBM15B expression was decreased in PMOP cases compared to controls, while the other significant m6A regulators displayed the opposite results (Figures 2A, C). And we visualized the chromosomal positions of the 12 m6A modulators via the “RCircos” package (Figure 2B). Figure 2: Identification of the 12 m6A modulators in PMOP. (A) Expression heat map of the 12 m6A modulators in controls and PMOP cases. (B) Chromosomal positions of the 12 m6A modulators. (C) Differential expression boxplots of the 12 m6A modulators between controls and PMOP cases. p < 0.05, and *p < 0.01. ## Correlation among writers, readers and eraser in PMOP We utilized linear regression analyses to investigate whether gene expression levels of writers or readers in PMOP exhibit correlation with the gene expression level of eraser. We observed that the gene expression levels of writers RBM15, WTAP, ZC3H13, and readers FMR1, YTHDC2, and HNRNPC in PMOP cases were positively correlated with eraser gene FTO. The other readers or writers were not significantly linked to eraser gene FTO (Figure 3). Thus, we demonstrated different correlations between different writers, readers and eraser. Figure 3: Correlation among Writers, Readers and Eraser in PMOP (A–K). Writer genes: RBM15, RBM15B, METTL3, WTAP, and ZC3H13; reader genes: ELAVL1, FMR1, HNRNPC, IGFBP3, YTHDC2, and YTHDF3; eraser gene: FTO. ## Establishment of the RF and SVM models Figure 4A showed “Reverse cumulative distribution of residual” and Figure 4B presented “Boxplots of residual”, which confirmed that the RF model has the smallest residuals. The residuals for most of the samples in the model are relatively small, suggesting that the RF model is better than the SVM model. Therefore, we determined the RF model to be the most suitable model for the prediction of PMOP occurrence. Then, we plotted ROC curve to estimate the models, and found that the RF model is more accurate than the SVM model according to their AUC values of the ROC curves (Figure 4C). Finally, we visualized these 7 significant m6A regulators after ranking them in order of importance and selected m6A regulators with importance score>2 as the candidate genes (Figure 4D). Figure 4: Establishment of the RF and SVM Models. (A) Reverse cumulative distribution of residual was constructed to display the residual distribution of RF and SVM models. (B) Boxplots of residual was construct to display the residual distribution of RF and SVM models. (C) ROC curves indicated the accuracy of the RF and SVM models. (D) The importance score of the 7 m6A modulators on the basis of the RF model. ## Establishment of the nomogram model We utilized the “rms” package in R to establish a nomogram model of the seven candidate m6A modulators for the prediction of the prevalence of PMOP patients (Figure 5A). We observed that the nomogram model exhibits high accuracy of prediction according to calibration curves (Figure 5B). The red line in the DCA curve stayed above the gray and black lines from 0 to 1, suggesting that decisions based on the nomogram model may be beneficial to PMOP patients (Figure 5C). Moreover, we noticed that the predictive power of the nomogram model was remarkable according to the clinical impact curve (Figure 5D). Figure 5: Establishment of the nomogram model. (A) The nomogram model was established on the basis of the 7 candidate m6A modulators. (B) The calibration curve was utilized to evaluate the predictive accuracy of the nomogram model. (C) Decisions on the basis of this nomogram model may be beneficial to PMOP patients. (D) The clinical impact curve was used to assess clinical impact of the nomogram model. ## Identification of two distinct m6A patterns We identified two m6A patterns (clusterA and clusterB) based on the 7 significant m6A regulators via the R package of “ConsensusClusterPlus” (Figures 6A–D). There were 16 cases in clusterA, and 9 cases in clusterB. Then, we plotted the heat map and histogram, which clearly displayed the differential expression levels of the 7 significant m6A modulators between the two clusters. We observed that the expression levels of RBM15, WTAP, FMR1, FTO, YTHDC2, and HNRNPC in clusterA were higher than those in clusterB, while the expression level of RBM15B exhibited no significant differences between the two cluster (Figures 6E, F). The PCA results revealed that the two m6A patterns could be distinguished by 7 significant m6A modulators (Figure 6G). We screened totally 90 m6A-associated DEGs between the two m6A patterns, and we carried out GO and KEGG enrichment analyses to excavate the role of these DEGs in PMOP (Figures 6H, I). The detailed information of GO and KEGG enrichment analysis was shown in Supplementary Tables 1, 2. We observed that GO: 0031331 (positive regulation of cellular catabolic process), GO:0030055(cell-substrate junction), GO:0005925(focal adhesion), and GO:0045296(cadherin binding) were the mainly enriched entries. We finally got totally 12 pathways as shown in Figure 6I. These signaling pathways like C-type lectin receptor signaling pathway, and Relaxin signaling pathway may exert regulatory functions on the pathological process of PMOP. Notably, KEGG enrichment analysis showed that osteoclast differentiation was one of the mainly enriched pathways. Specially, several key targets were involved in the pathway of osteoclast differentiation (e.g., RELB, SPI1, LILRA6, TGFB1). Figure 6: Consensus clustering of the 7 significant m6A modulators in PMOP. (A–D) Consensus matrices of the 7 significant m6A modulators for k = 2–5. (E) Expression heat map of the 7 significant m6A modulators in clusterA and clusterB. (F) Differential expression boxplots of the 7 significant m6A modulators in clusterA and clusterB. (G) Principal component analysis for the expression profiles of the 7 significant m6Amodulators that shows a remarkable difference in transcriptomes between the two m6A patterns. (H, I) GO and KEGG analysis that explores the potential mechanism underlying the effect of the 90 m6A-related DEGs on the occurrence and development of PMOP. p < 0.05, p < 0.01, and p < 0.001. Then, ssGSEA was performed to evaluate the immune cell abundance in PMOP samples, and we also assessed the correlation between immune cells and seven important m6A modulators. We observed that FMR1 was positively correlated with many immune cells (Figure 7A). We evaluated the differences in immune cell infiltration between patients with high and low FMR1 expressions. The results showed that patients with low FMR1 expression were more likely to exhibit increased immune cell infiltration than those with high FMR1 expression (Figure 7B). We found that clusterA was correlated with the immunity of immature B cell and gamma delta T cell while clusterB was related to CD56dim natural killer cell, monocyte, neutrophil and regulatory T cell immunity, indicating that clusterB may be more correlated with PMOP (Figure 7C). Figure 7: Single sample gene set enrichment analysis. (A) Correlation between immune cell infiltration and the 7 significant m6A modulators. (B) Difference in the abundance of infiltrating immune cells between high and low FMR1 protein expression groups. (C) Differential immune cell infiltration between clusterA and clusterB. p < 0.05, p < 0.01, and p < 0.001. ## Classification of two distinct m6A gene patterns and construction of the m6A gene signature To lucubrate the m6A patterns, we used a consensus clustering approach to classify the PMOP cases into different genomic subtypes on the basis of the 90 m6A-related DEGs. We identified two distinct m6A gene patterns (gene clusterA and gene clusterB), which aligned with the sectionalization of m6A patterns (Figures 8A–D). Figure 8E displayed the expression levels of the 90 m6A-associated DEGs in gene clusterA and gene clusterB. The differential expression levels of immune cell infiltration and the 7 significant m6A modulators between gene clusterA and gene clusterB were also analogous to those in the m6A patterns (Figures 8F, G). These results again verified the veracity of our sectionalization via the consensus clustering approach. The m6A scores for each sample between the two distinct m6A patterns or m6A gene patterns were calculated through PCA algorithms for the quantification of the m6A patterns. We found that the clusterB or gene clusterB exhibited higher m6A score than clusterA or gene clusterA (Figures 8H, I). Figure 8: Consensus clustering of the 90 m6A-associated DEGs in PMOP. (A–D) Consensus matrices of the 90 m6A-associated DEGs for k = 2–5. (E) Expression heat map of the 90 m6A-associated DEGs in gene clusterA and gene clusterB. (F) Differential expression boxplots of the 7 significant m6A modulators in gene clusterA and gene clusterB. (G) Differential immune cell infiltration between gene clusterA and gene clusterB. (H) Differences in m6A score between clusterA and clusterB. (I) Differences in m6A score between gene clusterA and gene clusterB. p < 0.05, p < 0.01, and p < 0.001. ## Role of m6A patterns in distinguishing PMOP We utilized a Sankey diagram to display the correlation among m6A scores, m6A patterns, and m6A gene patterns (Figure 9A). To lucubrate the link between m6A patterns and PMOP, we explored the relationship between m6A patterns and RELB, SPI1, LILRA6, and TGFB1, which were enriched in osteoclast differentiation according to KEGG enrichment analysis. We observed that clusterB or gene clusterB displayed higher expression levels of RELB, SPI1, LILRA6, and TGFB1 than clusterA or gene clusterA, indicating that clusterB or gene clusterB were closely correlated with PMOP characterized by osteoclast differentiation (Figures 9B, C). Figure 9: Role of m6A patterns in distinguishing PMOP. (A) Sankey diagram showing the relationship between m6A patterns, m6A gene patterns, and m6A scores. (B) Differential expression levels of osteoclast differentiation-related genes between clusterA and clusterB. (C) Differential expression levels of osteoclast differentiation-related genes between gene clusterA and gene clusterB. p < 0.01, and *p < 0.001. ## RT-qRCR validation of significant m6A modulators It was verified that m6A genes FTO, FMR1, YTHDC2, RBM15, WTAP exhibited significantly higher expression levels in PMOP cases than controls (Figure 10), which was consistent with the bioinformatics results. Figure 10: RT-qPCR experimental validation of significant m6A modulators. (A–E) Relative mRNA expressions of 5 key m6A modulators including FTO, FMR1, YTHDC2, RBM15 and WTAP between the two groups. All results were expressed as mean ± standard deviation. p < 0.05, p < 0.01, and p < 0.001. ## Discussion PMOP is a widespread musculoskeletal disorder accompanied by bone system symptoms in postmenopausal women [37]. Existing researches have confirmed that m6A modulators play an indispensable role in numerous biological processes [38]. However, the role of m6A rmodulators in PMOP stays unclear. This present study aimed at investigating the role of m6A modulators in PMOP. Firstly, a total of 7 significant m6A modulators were screened from 12 m6A modulators via differential expression analysis between controls and PMOP cases, which were selected as diagnostic m6A modulators (FMR1, WTAP, YTHDC2, HNRNPC, FTO, RBM15, and RBM15B) based on an established RF model to predict the occurrence of PMOP. Then, we established a nomogram model on the basis of the seven candidate m6A modulators, which has been evaluated via the DCA curve to produce benefit to PMOP patients in virtue of decisions based on the nomogram model. FMR1 encodes an RNA-binding protein FMRP, which maintains mRNA stability by binding to the m6A site of mRNA [39]. Existing study has confirmed that FMR1-deficiency affects skeleton and bone microstructure, demonstrating that knock-out (KO) of FMR1 in mice showed increased femoral cortical thickness, reduced cortical eccentricity, decreased femoral trabecular pore volumes, and a higher range of trabecular thickness distribution compared to controls [40]. WTAP (Wilm’s tumor 1 protein) is a ubiquitous nuclear protein that has been reported to facilitate the formation of m6A [41]. In addition, existing evidence has confirmed that the WTAP expression level was remarkably upregulated 7 days after fracture [42]. Moreover, the increased expression of WTAP has been reported to promote cellular senescence in aging-related diseases [43]. YTHDC2 belongs to the DExD/H box RNA helicase family, which exerts important functions in regulating the transcription of mRNA and maintaining the stability of mRNA [44]. YTHDC2 knockdown can exert a stimulative effect on the osteogenic differentiation of human BMSCs and suppress the adipogenic differentiation [45]. As a DNA binding protein, HNRNPC (Heterogeneous nuclear ribonucleoprotein C) plays an essential part in RNA processing, exerting a remarkably suppressive effect on the transcription of the vitamin D hormone,1,25-dihydroxyvitamin D (1,25(OH)2D) [46]. And HNRNPC has properties of species-specific heterodimerization that functions as an indispensable prerequisite for DNA binding and down-regulation of 1,25(OH)2D-related gene transactivation in osteoblasts [47]. FTO is a primary m6A demethylase that suppresses osteogenic differentiation by demethylating runx2 mRNA, thus accelerating the process of osteoporosis [48]. It has also been found that FTO is a regulator that determines the differentiation of BMSCs by affecting the activation of the GDF11 signaling axis in the bone marrow, promoting Smad$\frac{2}{3}$ phosphorylation to stimulate osteoclastogenesis and inhibit osteoblast differentiation, thus leading to the development of osteoporosis (49–51). The RNA binding motif protein 15 (RBM15/OTT1) and its paralogue RBM15B (OTT3) belong to SPEN family members [52]. Existing studies have confirmed that RBM15 in stress hematopoiesis have a variety of aging-related physiologic changes, including increased DNA damage and NF-κB activation [53], which may serve as important pathological factors in the development of osteoporosis. In addition, study has reported that knockdown of RBM15 and RBM15B impairs XIST-mediated gene silencing [52], which influences osteoblast differentiation in osteoporosis [54]. Therefore, to our knowledge, the seven candidate m6A modulators may play an important part in the occurrence and development of osteoporosis according to previous studies. Existing researches reveal that the dysfunction of T and B ymphocytes may play an essential role in the pathogenesis of PMOP [55]. We found that clusterA was correlated with the immunity of immature B cell and gamma delta T cell while clusterB was related to CD56dim natural killer cell, monocyte, neutrophil and regulatory T cell immunity, indicating that clusterB may be more correlated with PMOP (Figure 7C). Regulatory T cell (Treg) exerts an essential regulatory function in maintaining immune homeostasis and inhibiting the evolution of PMOP [56]. Treg cells negatively regulate osteoclasts in bone metabolism, inhibiting osteoclast formation and differentiation and reducing osteoclast activity [57]. The immune and skeletal systems share many regulatory factors, such as transforming growth factor-β (TGFB1), which inhibits osteoclast function of bone resorption and regulates new bone formation in bone resorption region [58]. Bozec et al [59] found that Treg cells can regulate osteoclastogenesis by secreting cytokines such as TGFB1, IL-10 and IL-4. In this study, we identified two distinct m6A patterns (clusterA and clusterB) on the basis of the 7 significant m6A modulators as well as two distinct m6A gene patterns (gene clusterA and gene clusterB) based on the 90 m6A-associated DEGs. RELB, SPI1, LILRA6, and TGFB1 were enriched in the pathway of osteoclast differentiation according to KEGG enrichment analysis of the 90 m6A-associated DEGs. ClusterB was closely correlated with the regulatory T cell (Treg) immunity and displayed higher expression levels of RELB, SPI1, LILRA6, and TGFB1, suggesting that clusterB may be linked to osteoclast differentiation. Moreover, the m6A scores for each sample between the two distinct m6A patterns or m6A gene patterns were calculated through PCA algorithms for the quantification of the m6A patterns. We found that the clusterB or gene clusterB exhibited higher m6A score than clusterA or gene clusterA. Our RT-qPCR experiments verified that m6A genes FTO, FMR1, YTHDC2, RBM15, WTAP exhibited significantly higher expression levels in PMOP cases than controls (Figure 10), which was consistent with the bioinformatics results and previous studies. Our results confirm the involvement of these m6A regulators in PMOP and provide new clues to their role in the pathogenesis of PMOP, which further verified the possibility that m6A modulators may play an important role in the development of PMOP. To the best of our knowledge, this study is the first time to report m6A-related diagnostic biomarkers of PMOP in the subtype classification of blood monocytes. However, there remain some limitations in our study. This study analyzed the relationship between m6A regulators and immune cell infiltration and briefly validated the expression of key m6A regulators in the samples from PMOP patients, but the underlying regulatory mechanisms in the progression of PMOP have not yet been fully elucidated. In the future, more in vivo, in vitro and clinical experiments are needed to verify the bioinformatics results. ## Conclusion In general, our present study screened seven diagnostic m6A modulators and constructed a nomogram model providing accurate prediction for the prevalence of PMOP. Then, we authenticatd two m6A patterns based on the 7 m6A modulator, and found that clusterB may be more correlated with PMOP. To our knowledge, this study is the first to report m6A-related diagnostic biomarkers of PMOP in the subtype classification of blood monocytes. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. ## Ethics statement The clinical experiments involved in this paper were authorized by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (No. K[2019]129). The patients/participants provided their written informed consent to participate in this study. ## Author contributions PZ, HC, DL, JT, JC, ZL, HR, and XJ contributed to the study conception and design. PZ, HC, BX, WZ, and QS contributed to the bioinformatics analysis and experimental validation. JH, GS, XY, ZZ, GZ, GC, and FY contributed to data analysis, and drafting the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.990078/full#supplementary-material ## References 1. Arceo-Mendoza RM, Camacho PM. Postmenopausal osteoporosis: Latest guidelines. Endocrinol Metab Clin North Am (2021) 50. DOI: 10.1016/j.ecl.2021.03.009 2. 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--- title: Trends in lipid profile and lipid control among survivors of stroke or myocardial infarction among US adults, 2001–2018 authors: - Weiwei Dong - Zhiyong Yang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031105 doi: 10.3389/fendo.2023.1128878 license: CC BY 4.0 --- # Trends in lipid profile and lipid control among survivors of stroke or myocardial infarction among US adults, 2001–2018 ## Abstract ### Background We aim to analyze the change in lipid profile and lipid control among survivors of stroke and/or myocardial infarction among US adults from 2001–2018. ### Methods In total, 3,736 survivors of stroke and/or myocardial infarction from the 2001–2018 National Health and Nutrition Examination Surveys were included in this study, representing a weighted total population of 110,005,898. Trends for lipid concentration and lipid control rate over time were detected via general linear regression analysis and lipid control was compared by sex and race via survey-weighted logistic regression analysis. ### Results The total cholesterol, LDL, and triglyceride concentrations were significantly decreased in survivors from the 2001–2002 survey cycle to the 2017–2018 survey cycle (p for trend < 0.01). Lipid control was defined as total cholesterol < 200 mg/dL. Among survivors, the lipid control rate increased from $56.2\%$ ($95\%$ CI: $43.9\%$, $67.7\%$) in the 2001–2002 survey cycle to $73.2\%$ ($95\%$ CI: $64.8\%$, $80.2\%$) in the 2017–2018 survey cycle (p for trend < 0.01). Women had a higher lipid concentration and were more likely have poor lipid control compared to men. Non-Hispanic White survivors possessed better lipid control than other races survivors. ### Conclusions Lipid concentrations decreased and lipid control improved in stroke and/or myocardial infarction survivors from 2001 to 2018, with heterogeneity observed according to sex and race. ## Introduction Cardio-cerebrovascular disease remains the leading cause of mortality and disability in the U.S. population [1, 2]. Survivors of cardio-cerebrovascular disease are at increased risk of recurrent cardio-cerebrovascular events, with $28\%$ of all strokes and coronary events combined being recurrent events (2–4). Dyslipidemia is a risk factor for the occurrence and recurrence of cardio-cerebrovascular disease [5, 6]. The prevalence of dyslipidemia is increasing due to unhealthy diet and lifestyle [7, 8]. Lipid concentration and lipid control trends have been studied in the general population [9], but not in survivors of cardio-cerebrovascular disease. For survivors of stroke and/or myocardial infarction, experts recommend pharmacological interventions to reduce lipid level to prevent recurrence and prolong survival of the survivors (10–12). Although the benefits of dyslipidemia treatment in survivors of stroke and/or myocardial infarction are clear [13], further information is needed regarding the treatment adequacy. Trends in lipid level and lipid control in survivors of stroke and/or myocardial infarction may provide an important reference for recurrence control and prevention, as well as directions for improved interventions in the future. Therefore, we investigated the changes in lipid level and control, and explored their variation by sex and race in survivors of stroke and/or myocardial infarction among US adults from 2001–2018. ## Study population The National Health and Nutrition Examination Surveys (NHANES) database was established by the Centers for Disease Control to assess the nutritional and health status of the U.S. population. The survey is conducted every 2 years using a complex stratified multi-step sample to investigate the health status of the entire U.S. population. The NHANES investigators collected data, including demographic data, examination data, laboratory data, and questionnaire data, from in-home interviews and study visits conducted in the mobile examination center. We included survivors of stroke and/or myocardial infarction ($$n = 3$$,736, weighted total population of 110,005,898) aged ≥ 20 years in NHANES from 2001 to 2018. Participants who answered “yes” to the question “Has a doctor ever told you that you had a heart attack?” and/or “Has a doctor ever told you that you had a stroke?” were defined as survivors of myocardial infarction and stroke, respectively [14, 15]. The survivors’ data included age, race, sex, education level, marital status, poverty to income ratio (PIR), body mass index (BMI), medication use for dyslipidemia, and smoking status. Because of the small sample size for races other than non-Hispanic White, we divided the races into two groups: non-Hispanic White and other races, including Mexican American and non-Hispanic Black. BMI was calculated by dividing the weight (kg) by the square of their height (m2). The answer to the use of lipid-lowering medications “yes” or “no”, was based on respondents’ self-report. Smoking status was considered “yes” if participants had smoked ≥ 100 cigarettes in a lifetime [16, 17]. ## Outcomes The main outcome included lipid levels (total cholesterol, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and triglycerides), which were examined via laboratory measurement. Further information on NHANES laboratory measurement methods and procedures can be found at http://www.cdc.gov/nchs/nhanes/survey_methods.htm. The secondary outcome was lipid control, which was defined as total cholesterol < 200 mg/dL. ## Statistical analysis Statistical analyses were conducted in accordance with the NHANES analysis recommendations. Considering the complex multi-stage survey design of NHANES, we assigned sample weights and stratified and clustered each participant to calculate the national representative estimate. Categorical variables are presented as survey-weighted percentage ($95\%$ confidence interval [CI]) and continuous variables are presented as survey-weighted mean ($95\%$ CI). Kolmogorov-Smirnov method was utilized to assess data normality. The triglyceride concentration was log-transformed because the distribution was skewed. We evaluated the trends in lipid concentration and lipid control rate over time using a general linear regression analysis, and cross-sectionally analyzed change in lipid control (yes or no) across sex and race subgroups for 4-year survey periods at various times (2001–2004, 2005–2008, 2009–2012, and 2013–2016). Survey cycles were combined to obtain more reliable estimates. To compare cross-sectional lipid control (yes or no) estimates by sex and race, we utilized survey-weighted logistic regression analysis and calculated odds ratio (ORs). All analyses were performed using Empower software (www.empowerstats.com; X&Y solutions, Inc., Boston, MA, USA) and R version 3.4.3 (http://www.Rproject.org, The R Foundation). P-values < 0.05 were considered statistically significant. ## Results A total of 3,736 participants, who responded with history of stroke and/or myocardial infarction, were included in this study, representing a weighted total population of 110,005,898. The mean age of the survivors was 65.0 years ($95\%$ CI: 64.4, 65.6), $54\%$ survivors were male, $74.1\%$ survivors were Non-Hispanic White, and $25.9\%$ participants were other races. The rate of survivors who self-reported taking medications for dyslipidemia increased gradually from $36.1\%$ ($95\%$ CI: $30.7\%$, $41.9\%$; 2001–2002 survey cycle) to $57.1\%$ ($95\%$ CI: $47.4\%$, $66.2\%$; 2017–2018 survey cycle). The weighted baseline characteristic of all survivors of stroke and/or myocardial infarction from 2001–2018 is presented in Table 1. Table 1 \| Variables \| Total \| 2001-2002 \| 2003-2004 \| 2005-2006 \| 2007-2008 \| 2009-2010 \| 2011-2012 \| 2013-2014 \| 2015-2016 \| 2017-2018 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Weighted sample size \| 110005898 \| 9980317 \| 10705869 \| 12181600 \| 12750840 \| 11213789 \| 12235852 \| 12830109 \| 12962766 \| 15144756 \| \| Un-weighted sample size \| 3736 \| 407 \| 372 \| 363 \| 487 \| 439 \| 386 \| 389 \| 418 \| 475 \| \| Age,y a \| 65.0 (64.4,65.6) \| 65.0 (62.8,67.2) \| 64.6 (61.9,67.2) \| 65.8 (63.9,67.6) \| 65.1 (63.4,66.8) \| 64.0 (61.9,66.1) \| 65.1 (63.6,66.7) \| 65.8 (64.4,67.3) \| 64.6 (63.1,66.1) \| 65.0 (63.4,66.7) \| \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| Sex \| \| Man \| 54.0 (51.6,56.3) \| 52.8 (47.2,58.3) \| 51.5 (44.0,58.9) \| 50.6 (43.1,58.0) \| 51.8 (45.5,58.0) \| 59.9 (54.3,65.3) \| 52.2 (45.2,59.1) \| 52.1 (43.4,60.7) \| 58.4 (52.8,63.7) \| 56.1 (45.7,66.0) \| \| Women \| 46.0 (43.7,48.4) \| 47.2 (41.7,52.8) \| 48.5 (41.1,56.0) \| 49.4 (42.0,56.9) \| 48.2 (42.0,54.5) \| 40.1 (34.7,45.7) \| 47.8 (40.9,54.8) \| 47.9 (39.3,56.6) \| 41.6 (36.3,47.2) \| 43.9 (34.0,54.3) \| \| Race \| Race \| Race \| Race \| Race \| Race \| Race \| Race \| Race \| Race \| Race \| \| Non-Hispanic White \| 74.1 (71.4,76.6) \| 78.3 (71.2,84.0) \| 80.7 (72.4,87.0) \| 80.8 (72.5,87.1) \| 75.1 (64.3,83.4) \| 73.9 (65.5,81.0) \| 70.1 (61.6,77.3) \| 75.3 (67.8,81.5) \| 64.8 (54.3,74.0) \| 70.7 (62.2,78.0) \| \| Other races \| 25.9 (23.4,28.6) \| 21.7 (16.0,28.8) \| 19.3 (13.0,27.6) \| 19.2 (12.9,27.5) \| 24.9 (16.6,35.7) \| 26.1 (19.0,34.5) \| 29.9 (22.7,38.4) \| 24.7 (18.5,32.2) \| 35.2 (26.0,45.7) \| 29.3 (22.0,37.8) \| \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| \| <high school \| 27.4 (25.3,29.6) \| 37.6 (31.4,44.1) \| 30.4 (22.9,39.0) \| 30.5 (23.8,38.1) \| 32.6 (27.2,38.5) \| 30.0 (24.1,36.6) \| 27.4 (20.5,35.5) \| 24.5 (18.7,31.3) \| 22.7 (16.8,30.0) \| 16.6 (10.5,25.2) \| \| high school \| 28.7 (26.7,30.9) \| 25.2 (19.4,32.2) \| 26.7 (22.1,31.8) \| 28.7 (21.2,37.8) \| 29.3 (23.5,36.0) \| 27.6 (20.9,35.6) \| 27.3 (19.7,36.6) \| 28.1 (22.7,34.1) \| 27.2 (21.9,33.2) \| 35.9 (30.5,41.6) \| \| >high school \| 43.8 (41.4,46.3) \| 37.2 (31.5,43.3) \| 42.9 (35.8,50.4) \| 40.8 (30.9,51.4) \| 38.1 (30.7,46.0) \| 42.4 (33.6,51.7) \| 45.3 (38.5,52.3) \| 47.4 (41.6,53.4) \| 50.1 (42.6,57.6) \| 47.5 (39.2,56.0) \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Married/Living with partner \| 58.0 (55.8,60.3) \| 57.8 (50.4,64.9) \| 57.9 (47.5,67.6) \| 57.9 (53.1,62.5) \| 59.1 (52.7,65.3) \| 61.3 (54.5,67.7) \| 57.6 (47.3,67.2) \| 56.5 (49.8,62.9) \| 54.7 (49.2,60.0) \| 59.7 (52.4,66.6) \| \| Widowed/Divorced/Separated \| 35.2 (33.1,37.3) \| 36.1 (29.4,43.3) \| 36.2 (27.2,46.4) \| 38.1 (33.1,43.3) \| 33.9 (26.9,41.7) \| 32.2 (27.3,37.6) \| 34.9 (28.2,42.3) \| 35.4 (29.8,41.5) \| 35.5 (30.3,41.1) \| 34.4 (27.9,41.4) \| \| Never married \| 6.8 (5.8,7.9) \| 6.1 (3.8,9.6) \| 5.9 (3.2,10.6) \| 4.0 (2.9,5.6) \| 7.0 (3.9,12.2) \| 6.4 (3.7,11.1) \| 7.5 (3.9,14.0) \| 8.1 (5.7,11.4) \| 9.8 (7.1,13.4) \| 6.0 (3.9,9.0) \| \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| PIR \| \| <1 \| 16.0 (14.4,17.8) \| 13.5 (9.5,18.7) \| 12.7 (9.6,16.5) \| 15.2 (11.0,20.7) \| 17.7 (13.0,23.7) \| 17.7 (13.3,23.1) \| 20.6 (15.8,26.3) \| 17.7 (11.0,27.4) \| 16.2 (10.7,24.0) \| 12.9 (10.0,16.3) \| \| 1-2 \| 27.8 (25.8,29.9) \| 28.9 (22.1,36.8) \| 33.2 (26.2,40.9) \| 25.2 (18.6,33.0) \| 28.1 (25.4,30.9) \| 27.0 (21.5,33.3) \| 24.4 (17.7,32.8) \| 32.1 (24.3,41.1) \| 30.8 (23.7,38.8) \| 22.2 (18.1,26.9) \| \| 2-3 \| 16.0 (14.1,18.0) \| 15.1 (9.2,23.7) \| 16.9 (12.6,22.3) \| 21.7 (14.8,30.7) \| 15.7 (11.6,21.0) \| 12.6 (8.2,18.8) \| 16.3 (10.1,25.4) \| 15.7 (11.4,21.2) \| 12.1 (6.6,21.2) \| 17.3 (13.0,22.5) \| \| 3-4 \| 9.5 (8.1,11.1) \| 8.9 (5.8,13.3) \| 9.7 (4.6,19.4) \| 11.7 (7.6,17.6) \| 5.7 (3.7,8.5) \| 13.8 (10.7,17.8) \| 7.5 (4.1,13.3) \| 10.5 (6.6,16.3) \| 11.7 (6.4,20.5) \| 7.1 (4.7,10.7) \| \| 4-5 \| 7.0 (5.7,8.5) \| 9.1 (5.0,16.0) \| 9.5 (5.0,17.4) \| 6.3 (4.1,9.5) \| 5.7 (3.2,10.0) \| 6.1 (4.0,9.1) \| 9.0 (5.1,15.3) \| 6.6 (3.2,13.1) \| 7.4 (3.1,16.5) \| 4.5 (2.2,9.0) \| \| ≥5 \| 15.3 (13.5,17.4) \| 17.1 (11.0,25.8) \| 13.1 (8.8,18.9) \| 13.2 (8.9,19.1) \| 17.5 (12.0,24.7) \| 17.2 (10.4,27.2) \| 13.6 (9.1,19.8) \| 12.1 (8.2,17.6) \| 11.4 (7.0,18.0) \| 21.9 (15.6,29.8) \| \| Unknown \| 8.3 (7.1,9.7) \| 7.5 (5.1,10.8) \| 5.0 (2.6,9.2) \| 6.7 (3.4,12.8) \| 9.7 (7.5,12.4) \| 5.6 (3.6,8.6) \| 8.5 (5.4,13.2) \| 5.3 (3.3,8.3) \| 10.4 (6.6,16.1) \| 14.2 (8.9,21.9) \| \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| BMI \| \| <25kg/m2 \| 20.1 (18.7,21.6) \| 15.7 (11.9,20.6) \| 24.9 (19.0,31.8) \| 18.1 (14.9,21.8) \| 22.4 (18.4,27.0) \| 21.0 (17.9,24.5) \| 22.3 (18.2,27.1) \| 23.5 (18.4,29.6) \| 19.1 (15.2,23.7) \| 15.0 (11.4,19.5) \| \| 25 to 30kg/m2 \| 29.6 (27.6,31.7) \| 28.9 (21.4,37.7) \| 32.7 (26.3,39.9) \| 33.7 (27.9,40.1) \| 31.2 (27.1,35.6) \| 27.4 (23.1,32.2) \| 27.0 (20.7,34.2) \| 30.4 (24.6,36.9) \| 27.4 (22.1,33.5) \| 28.0 (20.4,37.1) \| \| ≥30kg/m2 \| 40.5 (38.4,42.7) \| 29.3 (21.5,38.6) \| 33.8 (29.2,38.7) \| 39.0 (33.7,44.7) \| 37.9 (32.6,43.6) \| 47.5 (43.2,51.8) \| 42.0 (34.1,50.2) \| 39.8 (31.8,48.4) \| 46.3 (39.4,53.2) \| 45.5 (38.2,52.9) \| \| Unknown \| 9.7 (8.5,11.1) \| 26.1 (19.2,34.4) \| 8.6 (5.8,12.4) \| 9.1 (5.4,14.9) \| 8.4 (5.4,13.0) \| 4.1 (2.9,6.0) \| 8.7 (5.8,12.9) \| 6.2 (3.6,10.5) \| 7.2 (3.6,14.1) \| 11.6 (8.3,15.8) \| \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| Take medication for dyslipidemia \| \| Yes \| 48.6 (46.2,51.0) \| 36.1 (30.7,41.9) \| 35.5 (29.2,42.4) \| 42.3 (35.2,49.7) \| 43.0 (38.3,47.8) \| 41.7 (35.3,48.4) \| 60.2 (51.4,68.4) \| 57.2 (51.4,62.7) \| 56.8 (47.7,65.4) \| 57.1 (47.4,66.2) \| \| No \| 7.0 (6.0,8.2) \| 4.7 (2.8,7.6) \| 6.5 (2.8,14.0) \| 2.7 (1.3,5.5) \| 7.7 (5.5,10.6) \| 4.6 (2.8,7.5) \| 5.6 (3.6,8.7) \| 6.3 (4.2,9.3) \| 8.5 (5.1,13.8) \| 14.3 (10.6,19.1) \| \| Unknown \| 44.4 (42.1,46.7) \| 59.2 (52.2,65.9) \| 58.0 (49.4,66.2) \| 55.0 (47.0,62.8) \| 49.3 (44.6,54.1) \| 53.7 (47.1,60.2) \| 34.1 (26.5,42.7) \| 36.5 (31.0,42.4) \| 34.7 (28.5,41.5) \| 28.6 (22.2,36.1) \| \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| Smoke \| \| Yes \| 63.2 (60.9,65.4) \| 64.6 (56.7,71.8) \| 64.5 (56.2,72.0) \| 67.9 (60.2,74.8) \| 60.8 (54.7,66.7) \| 59.5 (53.8,65.1) \| 63.2 (54.4,71.1) \| 63.9 (59.1,68.4) \| 64.2 (55.0,72.4) \| 60.7 (53.8,67.2) \| \| No \| 36.8 (34.6,39.1) \| 35.4 (28.2,43.3) \| 35.5 (28.0,43.8) \| 32.1 (25.2,39.8) \| 39.2 (33.3,45.3) \| 40.5 (34.9,46.2) \| 36.8 (28.9,45.6) \| 36.1 (31.6,40.9) \| 35.8 (27.6,45.0) \| 39.3 (32.8,46.2) \| ## Trends in lipid concentration in the US adult population with a history of stroke and/or myocardial infarction Because fasting blood tests were not performed in some survivors, our trend analysis of lipid concentrations (total cholesterol, HDL, LDL, and triglycerides) included only those survivors who reported fasting total cholesterol, HDL, LDL, and triglyceride values accordingly. The weighted baseline characteristics of survivors with fasting lipid value of total cholesterol ($$n = 3$$,245, representing a weighted total population of 97,726,894), HDL($$n = 3$$,244, representing a weighted total population of 97,706,175), LDL($$n = 1$$,552, representing a weighted total population of 47,384,996), and triglyceride ($$n = 1$$,605, representing a weighted total population of 48,694,152) are shown in Tables S1–S4. Among survivors of stroke and/or myocardial infarction, total cholesterol was significantly decreased from 197.5 mg/dL ($95\%$ CI: 188.5, 206.5 mg/dL) in the 2001–2002 survey cycle to 177.3 mg/dL ($95\%$ CI: 169.4, 185.2 mg/dL) in the 2017–2018 survey cycle (p for trend < 0.01). We next analyzed the heterogeneity of the total cholesterol level according to sex and race. The results showed that total cholesterol was higher among female survivors than male survivors. The decreased trend in total cholesterol was observed in both male and female survivors, while no obvious decreased trend in heterogeneity was observed according to race (Figures 1A, B). Moreover, we found no significant change trend in HDL level among survivors. Although HDL was higher among female survivors than male survivors, no significant difference was observed between non-Hispanic White and other races (Figures 1C, D). LDL was significantly decreased from 118.2 mg/dL ($95\%$ CI: 109.2, 127.3 mg/dL) in the 2001–2002 survey cycle to 102 mg/dL ($95\%$ CI: 91.6, 112.4 mg/dL) in the 2017–2018 survey cycle (p for trend < 0.01). The sex and race subgroup had a similar decreased trend in LDL level. LDL was higher among women survivors than men survivors, but no significant difference was observed between non-Hispanic White and other races (Figures 1E, F). In terms of triglycerides, the log-transformed triglycerides level was decreased from 5.0 mg/dL ($95\%$ CI: 4.8, 5.1 mg/dL) in the 2001–2002 survey cycle to 4.8 mg/dL ($95\%$ CI: 4.7, 4.9 mg/dL) in the 2017–2018 survey cycle (p for trend < 0.01). The sex and race subgroup had a similarly decreased trend in triglycerides level, and no significant heterogeneity was observed by sex and race (Figures 1G, H). Further, covariates (age, gender, race, educational level, marital status, PIR, BMI, taking lipid-lowering drugs, and smoke) were included and general linear regression analysis was performed to investigate trends of change in lipid concentrations of total cholesterol, HDL, LDL, and triglycerides. The results were consistent after adjusting for covariates. In summary, with the exception of HDL, the levels of lipid (total cholesterol, LDL, and triglycerides) showed a decreasing trend. Figure 1: Total cholesterol, HDL, LDL and triglyceride concertation among survivors of stroke and/or myocardial infarction. (A) Total cholesterol concentration based on sex; (B) total cholesterol concentration based on race; (C) HDD concentration based on sex; (D) HDD concentration based on race; (E) LDD concentration based on sex; (F) LDD concentration based on race; (G) log-transformed triglyceride concentration based on sex; (H) log-transformed triglyceride concentration based on race. Nationally representative estimates of the survivors of stroke and/or myocardial infarction aged ≥ 20 years in NHANES from 2001 to 2018 (total cholesterol n=3,245, representing a weighted total population of 97,726,894; HDL n=3,244, representing a weighted total population of 97,706,175; LDL n=1,552, representing a weighted total population of 47,384,996; triglyceride n=1,605, representing a weighted total population of 48,694,152). Estimates are presented as survey-weighted mean and 95% confidence intervals. ## Trends in lipid control rate in the US adult population with a history of stroke and/or myocardial infarction Among survivors of stroke and/or myocardial infarction, the lipid control rate increased from $56.2\%$ ($95\%$ CI: $43.9\%$, $67.7\%$) in the 2001–2002 survey cycle to $73.2\%$ ($95\%$ CI: $64.8\%$, $80.2\%$) in the 2017–2018 survey cycle (p for trend < 0.01). When adjusted for covariates mentioned above, we also observed increased trend in lipid control among survivors. The sex and race subgroup had a similarly increased trend in lipid control rate. Lipid control rate was higher among male survivors than females, but no significant difference was observed between non-Hispanic White and other races (Figure 2). Figure 2: Lipid control rate among survivors of stroke and/or myocardial infarction. (A) Lipid control rate based on sex; (B) Lipid control rate based on race. Nationally representative estimates of the survivors of stroke and/or myocardial infarction aged ≥ 20 years in NHANES from 2001 to 2018 (n=3,245, representing a weighted total population of 97,726,894). Estimates are presented as survey-weighted percentage and 95% confidence intervals. ## Comparison of lipid control based on sex and race among survivors of stroke and/or myocardial infarction Given that there was a difference in the lipid profile by sex, we next conducted a comparison of lipid control by sex and race. In 2001–2004, the poor lipid control (total cholesterol > 200 mg/dL) was significantly higher for female compared to male survivors (OR = 2.7, $95\%$ CI: [1.4, 5.1]). Similar results were observed in 2005–2008 (OR = 1.3, $95\%$ CI: [0.7, 2.4]), 2009–2012 (OR = 1.3, $95\%$ CI: [0.7, 2.5]), and 2013–2016 (OR = 1.4, $95\%$ CI: [0.8, 2.5]). Overall, female survivors of stroke and/or myocardial infarction were more likely have poor lipid control compared to males (Figure 3). We also conducted a comparison of lipid control by race. In 2001–2004, when compared to non-Hispanic White, the poor lipid control was significantly higher for other races (OR = 2.5, $95\%$ CI: [1.5, 4.1]). The OR value showed a gradual decrease over time. In 2005–2008, 2009–2012, and 2013–2016, the ORs were 1.4, $95\%$ CI: (0.9, 2.4), 1.3, $95\%$ CI: (0.8, 2.0), and 1.0, $95\%$ CI: (0.6, 1.5), respectively. The difference in poor lipid control between non-Hispanic White and other races among survivors of stroke and/or myocardial infarction decreased gradually over time (Figure 4). Figure 3: Association of sex with lipid control among survivors of stroke and/or myocardial infarction. Nationally representative estimates of the survivors of stroke and/or myocardial infarction aged ≥ 20 years in NHANES from 2001 to 2016 (n=3,245, representing a weighted total population of 97,726,894). Poor or good lipid control was defined as total cholesterol > 200 mg/dL or not, respectively. The reference group was men. An odds ratio > 1 indicates a higher rate of poor lipid control. Figure 4: Association of race with lipid control among survivors of stroke and/or myocardial infarction. Nationally representative estimates of the survivors of stroke and/or myocardial infarction aged ≥ 20 years in NHANES from 2001 to 2016 (n=3,245, representing a weighted total population of 97,726,894). Poor or good lipid control was defined as total cholesterol > 200 mg/dL or not, respectively. The reference group was non-Hispanic White race. An odds ratio > 1 indicates a higher rate of poor lipid control. ## Discussion In this study, we explored the lipid profile and control among survivors of stroke and/or myocardial infarction. Our results showed that the proportion of survivors taking medication to control dyslipidemia increased, the total cholesterol, triglycerides, and LDL levels, showed a decreasing trend from 2001 to 2018, and lipid control increased among survivors of stroke and/or myocardial infarction. We also found that men had better lipid control compared to female survivors, and non-Hispanic White survivors had better lipid control than other races; however, the difference in lipid control diminished gradually in individuals of non-Hispanic White race compared to other races. The national trend of better control of dyslipidemia in survivors of stroke and/or myocardial infarction may be related to the guidance of the new guideline. The 2018 American Heart Association (AHA)/American College of Cardiology (ACC) Multisociety Guideline recommends further strengthening the management of blood cholesterol and high-intensity statin therapy for high-risk patients. Patients with atherosclerotic cardiovascular disease should achieve a ≥ $50\%$ reduction in LDL-cholesterol (LDL-C) level, as a primary goal of therapy [18]. For adults aged 40 to 75 years with one or more cardiovascular disease risk factor and an estimated 10-year cardiovascular disease risk of ≥ $10\%$, the US Preventive Services Task Force suggests that clinicians prescribe a statin for the primary prevention [19]. A study has revealed that high dose statin therapy reduced cardiovascular morbidity but not mortality compared to low or moderate dose statin therapy [20]. Moreover, a systematic review for the US Preventive Services Task Force reported that statin therapy was correlated with a decreased risk of cardiovascular events and all-cause cardiovascular mortality and all-cause mortality (risk ratio [RR], 0.86 [$95\%$ CI, 0.80 to 0.93]; stroke (RR, 0.71 [$95\%$ CI, 0.62 to 0.82]); myocardial infarction (RR, 0.64 [$95\%$ CI, 0.57 to 0.71]) [21]. However, high dose statin therapy has been found to be correlated with adverse complications such as myopathy and incident diabetes [22]. The Synopsis of the 2020 Updated U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guideline recommended moderate intensity statin treatment as the foundation of pharmacologic treatment for the secondary prevention for cardiovascular disease [23]. In this study, we found that female survivors of stroke and/or myocardial infarction had high lipid levels and poorer lipid control than males. Similarly, a Chinese study of 1484 acute ischemic stroke in elderly patients (>75 years) from 2005 to 2013, found that women had higher levels of TC, TG, HDL-C, and LDL-C, and were more likely to have dyslipidemia compared to men [24]. In the general population, the total cholesterol level decreased more in males than females from 2001 to 2016 in the US. The average total cholesterol levels for males and females from 2001 to 2004 and 2013 to 2016 were 201 and 188 mg/dL, respectively. Sex differences were also found in dyslipidemia control, with control rates of $51\%$ for women and $63\%$ for men from 2013 to 2016 in the US [25]. Statin efficacy is closely related to medication adherence, and studies have found that female sex was associated with lower statin adherence [26]. Female’s higher rates of visceral fat increase with age compared to males, which may explain the higher dyslipidemia rate among female survivors of stroke and/or myocardial infarction [27]. Studies have found the with increasing age, females are more sedentary than males and are more likely to develop mobility impairments [28, 29]. Overall, differences in healthy lifestyle, medication adherence, and physical function contribute to differences in lipid levels and lipid control between female and male survivors of stroke and/or myocardial infarction. Differences in genetic factors, lifestyle, dietary habits, socioeconomic status, and medical resources may explain the heterogeneity of lipid levels and lipid control in survivors of stroke and/or myocardial infarction by race. In this study, we found that the difference in lipid control diminished gradually between non-Hispanic White races compared to other races. Our study has several limitations. First, lipid parameters were missing in some of the survivors, trend analysis was performed only among survivors with lipid parameters, and we cannot excluded the presence of selective bias that could affect our results. Second, because the small sample size of survivors of other races in this study, we only divided race into non-Hispanic White and other races. Future studies with larger sample sizes may detect differences in lipid levels and lipid control among survivors of different races. Third, the definition of lipid control varies across guidelines, and the definition of lipid control chosen for this study is consistent with that in previous reports [30]. Fourth, when analyzing lipid control based on sex and race, we combined survey cycles at 4-year survey periods to cross-sectionally analyze changes in lipid control across subgroups, and data of the 2017–2018 survey cycle were not included in the analysis. Finally, it is unclear whether the results of this study will continue to be consistent until the 2021–2022 survey cycle, especially under the impact of the new coronary pneumonia epidemic. ## Conclusion In this cross-sectional study, we observed that lipid concentrations decreased in stroke and/or myocardial infarction survivors. Survivors had improved lipid control, however, there was heterogeneity based on sex and race. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. ## Author contributions WD: Conceptualization, Methodology, Software, Investigation, Data curation, Writing-original draft. ZY: Visualization, Supervision, Writing-review & editing. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Myricetin attenuates hypoxic-ischemic brain damage in neonatal rats via NRF2 signaling pathway authors: - Tingting Chen - Yingying Hu - Liying Lu - Qianlei Zhao - Xiaoyue Tao - Bingqing Ding - Shangqin Chen - Jianghu Zhu - Xiaoling Guo - Zhenlang Lin journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10031108 doi: 10.3389/fphar.2023.1134464 license: CC BY 4.0 --- # Myricetin attenuates hypoxic-ischemic brain damage in neonatal rats via NRF2 signaling pathway ## Abstract Introduction: Hypoxic-ischemic encephalopathy (HIE) is a crucial cause of neonatal death and neurological sequelae, but currently there is no effective therapy drug for HIE. Both oxidative stress and apoptosis play critical roles in the pathological development of HIE. Myricetin, a naturally extracted flavonol compound, exerts remarkable effects against oxidative stress, apoptosis, and inflammation. However, the role and underlying molecular mechanism of myricetin on HIE remain unclear. Methods: In this study, we established the neonatal rats hypoxic-ischemic (HI) brain damage model in vivo and CoCl2 induced PC12 cell model in vitro to explore the neuroprotective effects of myricetin on HI injury, and illuminate the potential mechanism. Results: Our results showed that myricetin intervention could significantly reduce brain infarction volume, glia activation, apoptosis, and oxidative stress marker levels through activating NRF2 (Nuclear factor-E2-related factor 2) and increase the expressions of NRF2 downstream proteins NQO-1 and HO-1. In addition, the NRF2 inhibitor ML385 could significantly reverse the effects of myricetin. Conclusion: This study found that myricetin might alleviate oxidative stress and apoptosis through NRF2 signaling pathway to exert the protective role for HI injury, which suggested that myricetin might be a promising therapeutic agent for HIE. ## 1 Introduction Perinatal asphyxia-induced hypoxic-ischemic encephalopathy (HIE) is one of the important causes of neonatal death and nervous system dysfunction (Sun et al., 2017). The nearly half of HIE patients die in the neonatal period, and surviving infants have a high risk of neurological sequelae, which can cause global public health burden (Rocha-Ferreira and Hristova, 2015). HIE is not a single occurrence, but is an ongoing process that leads to neuronal death within hours to days after the initial injury (Jaworska et al., 2017). Despite the application of hypothermia as the reliable standard therapy for neonatal HIE, the treatment time window of hypothermia is very narrow (Sahuquillo and Vilalta, 2007; Goswami et al., 2020). The treatment effects of hypothermia on moderate or severe patients are very limited, and even have many adverse reactions (Michniewicz et al., 2022). Thus, it is imperative to explore more effective treatment strategies to improve the prognosis of neonatal hypoxic-ischemic (HI) brain damage. With in-depth study, researchers found that the pathophysiology of neonatal HIE is a complex evolutionary process. In the initial stage of primary injury, the brain undergoes a series of energy-failure-related processes, such as excitotoxin accumulation, oxidative stress, and inflammation activation (Rodriguez et al., 2020), and the immature neonatal brain is particularly sensitive to these processes (Blomgren and Hagberg, 2006). After several hours, the brain will enter the second stage of injury, during which the cell apoptosis is induced by inflammation and oxidative stress (Krystofova et al., 2018). Meanwhile, the cell apoptosis is a key factor influencing the extent of HI injury and determining the size or shape of the central nervous system (Thornton et al., 2017). Therefore, the target for relieving oxidative stress and neuronal apoptosis may be an effective strategy for HIE treatment. Nuclear factor-E2-related factor 2 (NRF2) is a major regulator against oxidative stress (Zenkov et al., 2017). Under the normally physiological conditions, NRF2 can bind to cytosolic Kelch-like ECH-associated protein 1 (KEAP1), and then be degraded by ubiquitination (Done and Traustadottir, 2016). When cells are stimulated by oxidative stress, NRF2 can dissociate from KEAP1 protein and perform nuclear translocation, which subsequently can enhance the transcriptional expressions of targets NAD(P)H quinone oxidoreductase-1 (NQO-1) and heme oxygenase-1 (HO-1) (He and Ma, 2012; Chatterjee and Bohmann, 2018). Simultaneously, with the stimulation of oxidative stress, cell apoptosis can also be activated. NRF2 is widely expressed in several tissues, and its role in the nervous system is becoming clear. Study in Alzheimer’s disease model had shown that NRF2 could ameliorate cognitive deficits and reduce Aβ deposition in mice (Ma J et al., 2022). In addition, the activation of NRF2 could also attenuate blood brain barrier (BBB) disruption after stroke (Yang et al., 2018). In a recent study, Nrf2-knockout was found to exacerbate oxidative stress and apoptosis in brain of hypoxic-ischemic (HI) injury mice, therefore aggravating the brain injury (Zhang Y et al., 2020). Myricetin, a naturally extracted flavonol compound, is widely found in fruits, vegetables, and berries (Semwal et al., 2016). Studies had shown that it has a variety of biological activities, such as anti-oxidant, anti-inflammatory, and anti-cancer (Salvamani et al., 2014; Stoll et al., 2019). Moreover, due to its excellent safety, myricetin is also used in the research of neonatal disease (Liu et al., 2018; Pluta et al., 2021). It has been reported that myricetin could alleviate intestinal ischemia-reperfusion induced injury through attenuating inflammatory responses and oxidative stress (Sun Y et al., 2018). In addition, myricetin could exert a protective effect on sepsis-associated encephalopathy through modulating inflammasome and apoptotic signaling (Gong et al., 2019). However, there is a lack of research on myricetin in HIE. So far, there is no report to determine whether myricetin has a neuroprotective role against neonatal HI brain injury. In this study, we would investigate myricetin neuroprotective effects on HI brain injury in vivo and in vitro, and try to illuminate the potential mechanism. This study may provide a new therapeutic drug for HI brain injury in neonates. ## 2.1 Neonatal rat hypoxic-ischemic (HI) brain damage model establishment and drug administration Sprague-Dawley (SD) rats were purchased from the Animal Center of the Chinese Academy of Sciences (Shanghai, China), and were maintained in the pathogen-free Laboratory Animal Center. Adult rats had access to standard foods and water as well as freely mated to produce offspring for subsequent studies. All experimental operations and animal feeding were approved by the Ethics Committee of Laboratory Animals of Wenzhou Medical University, and were strictly conducted following the Guidelines for the Care and Use of Laboratory Animals. The modified Rice-Vannucci model was used to simulate hypoxic-ischemic (HI) brain damage in male pups 7 days after birth (P7) (Vannucci and Vannucci, 2005). In brief, the P7 pups were anesthetized with isoflurane, and then the left common carotid artery was isolated and ligated within 5 min. After surgery, the pups were returned to the mother to rest fully for 2 h. The rats were then placed in a hypoxic chamber at a constant temperature with 37°C, $92\%$ N2, and $8\%$ O2 for 2 h. Sham rats were not subjected to arterial ligation and hypoxia. Myricetin was firstly dissolved in DMSO (D8317, Solarbio, Beijing, China), and was then mixed in corn oil. The drug delivery route and dose used in this study were chosen based on previous studies (Sun L et al., 2018; Lin et al., 2020). The pups in HI + Myr group received orally myricetin (HY-15097, MedChemExpress, Monmouth Junction, NJ, United States) at 25 mg/kg dose via gavage starting 1 h after HI, and maintained daily administration until the pups were sacrificed. The myricetin used every day should be prepared fresh. In vivo experiments, we selected two time-points for parameter detection. At 24 h after HI injury, brain tissues were collected to verify the protective effects of myricetin in the acute stage of injury. Besides, the brain tissues were obtained 7 days after injury to assess the long-term neuroprotective effects of myricetin. ## 2.2 Infarct volume evaluation The infarct volume of rat brain tissues was quantified by 2,3,5-triphenyltetrazolium chloride (TTC, T8170, Solarbio, Beijing, China) staining (Chumboatong et al., 2020). At 24 h after HI, the rats were deeply anesthetized and perfused to obtain brain tissues, which were frozen at −80°C for several min and then cut into about 2 mm thick coronal sections. The sections were immersed in $1\%$ TTC solution, reacted at 37°C in the dark for 30 min, and fixed in $4\%$ paraformaldehyde solution (P1110, Solarbio, Beijing, China) overnight. The infarct volume of brain tissues was measured using Image J software (National Institutes of Health, Bethessa, MD, United States). The infarct volume (%) = [(contralateral hemispheric volume − ipsilateral hemispheric stained volume)/contralateral hemispheric volume] ×$100\%$. ## 2.3 Measurement of brain edema As previously described (Jia et al., 2020), we used the dry-wet ratio method to assess the water content of brain tissue. Similarly, we obtained brain tissues at 24 h after HI, and then isolated the left hemisphere and recorded the weight as wet weight. Subsequently, the brain tissues were dried in an oven at 70°C for 72 h for measurement the dry weight. The percentage of moisture content was calculated as [(wet weight − dry weight)/wet weight] ×$100\%$. ## 2.4 Histological staining At 7 days after HI, the rats were deeply anesthetized and the chest cavity was quickly opened to expose the heart. Then, 20 mL normal saline and 20 mL $4\%$ PFA solution were used for cardiac perfusion at a rate of 10–15 mL/min until their livers were cleared of blood. The rat brains were freshly extracted after decapitation, and then the brain tissues were immediately fixed in $4\%$ PFA for 24 h. After gradient dehydration using ethanol and xylene, the brain tissues were embedded in paraffin and cut into 5 μm thick sections, which were used for subsequent hematoxylin-eosin (HE) (G1120, Solarbio, Beijing, China) and Nissl (G1432, Solarbio, Beijing, China) staining according to the manufacturer’s instructions (Zhu J. J et al., 2021). The images of bright field were obtained under the optical microscope (Nikon Corporatin, Tokyo, Japan), and the number of neurons per mm3 was analyzed using Image J software (National Institutes of Health, Bethesda, MD, United States). In addition, 4 images per sample were quantitated ($$n = 4$$ rats per group). ## 2.5 Immunohistochemistry As mentioned above, the brain tissues were obtained at 7 days after HI injury and made into paraffin sections. Immunohistochemistry staining was performed as described previously (Wei et al., 2020). The paraffin-embedded sections were dewaxed and hydrated, and then were boiled in preheated sodium citrate buffer (C1010, Solarbio, Beijing, China) for 20 min and cooled to room temperature for antigen retrieval. The endogenous peroxidase was eliminated by $3\%$ H2O2. After washed with PBS, sections were blocked with $10\%$ goat serum (C0265, Beyotime, Shanghai, China) for 1 h at the room temperature. Then, sections were incubated with primary antibodies (listed in Supplementary Table S1) overnight at 4°C, and followed by the incubation of goat anti-rabbit IgG HRP secondary antibody (1:200, Proteintech, SA00001-2) for 1 h at room temperature. Finally, these slides were visualized with 3, 3′-diaminobenzidine DAB solution under the optical microscope (Nikon Corporatin, Tokyo, Japan). ## 2.6 Immunofluorescence staining For in vivo analysis, we obtained brain tissues 24 h after injury and made them into paraffin-embedded sections. The sections were dewaxed and hydrated for antigen retrieval. In vitro, PC12 cells were fixed with $4\%$ PFA solution for 20 min. Then, they were incubated in $10\%$ goat serum with $0.3\%$ Triton X-100 for 1 h at room temperature. After washed with PBS, they were incubated with primary antibodies (listed in Supplementary Table S1) overnight at 4°C, and followed by the incubation of appropriate secondary antibody FITC-conjugated Goat Anti-Mosue IgG (1:200, Proteintech, SA00003-1), Cy3-conjugated Goat Anti-Mouse (1:200, Proteintech, SA00009-1) or Cy3-conjugated Goat Anti-Rabbit (1:200, Proteintech, SA00009-2) for 2 h at room temperature (Zhang W et al., 2020). Finally, the nuclei were stained with DAPI (S2110, Solarbio, Beijing, China), and images were obtained by a fluorescence microscope (Nikon Corporatin, Tokyo, Japan). ## 2.7 TUNEL staining To analyze apoptosis in brain tissues at 24 h after HI, TUNEL staining was performed using TUNEL Apoptosis Detection Kit (11684817910, Roche, Basel, Switzerland). Briefly, the sections of brain tissues were dewaxed and hydrated, and incubated with the reaction solution in a humid chamber at 37°C for 1 h (Zhu Y et al., 2021). The stained images were captured using a fluorescence microscope, and the number of TUNEL positive cells and DAPI positive staining nuclei were counted using Image J software. ## 2.8 Cell culture and treatment The differentiated PC12 cells were purchased from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). PC12 cells were maintained in dulbecco’s modified eagle medium (DMEM, Gibco, United States) containing $10\%$ FBS (Gibco, United States) at 37°C in a humidified incubator with $5\%$ CO2. Cellular hypoxia was induced by CoCl2 (C8661, Sigma-Aldrich, St. Louis, MO, United States), which is a chemical compound widely used to induce hypoxic-ischemic condition by increasing the generation of ROS (Wang et al., 2000). In vitro, PC12 cells were treated with CoCl2 for 24 h to induce hypoxia. Myricetin was dissolved in DMSO with a 20 mM stock concentration. According to the preliminary concentration gradient experiments, 200 μM myricetin was selected as the optimal concentration to treat the cells. The myricetin group was pretreated with myricetin 2 h before CoCl2 stimulation, while the ML385-treated group was pretreated with NRF2 inhibitor ML385 (HY-100523, MedChemExpress, Monmouth Junction, NJ, United States) (20 μM) for the same time. After the pretreatment, CoCl2 was added to each group with myricetin or ML385 according to the experimental requirements. ## 2.9 Cell viability assay Cell Counting Kit-8 (CCK-8, 40203ES88, Yeasen Biotechnology, Shanghai, China) was used to detect the viability of PC12 cells in different groups. PC12 cells at a density of 1 × 104 cells/well were seeded in 96-well plates. When testing the effect of myricetin, the different doses of myricetin was added into wells 2 h before CoCl2 treatment. Then, CoCl2 was added to the wells with myricetin. After drug treatment or hypoxia, $10\%$ volume of CCK-8 solution per well was added into each well, which was then maintained in the dark for 30 min at 37°C. The optical density (OD) at 450 nm was measured using a microplate reader (Thermo Fisher Scientific, Waltham, MA, United States). ## 2.10 Annexin V and PI assay An Annexin V FITC/PI Apoptosis Detection Kit (556547, Becton Dickinson, Franklin Lakes, United States) was used for the detection of cell apoptosis according to the manufacturer’s instructions. Briefly, cells in different groups were harvested by digestion with trypsin (25200072, Gibco, Grand Island, NY, United States), then washed twice with cold PBS, and resuspended in Binding Buffer. The cells were further stained with the mixture of 5 µL FITC Annexin V and 5 µL PI for 15 min at room temperature in the dark (Park et al., 2021). The apoptosis ratio was quantified with a Flow Cytometer (Beckman Coulter, Breya, California, United States). ## 2.11 Reactive oxygen species (ROS) detection The intracellular ROS levels were determined by a ROS assay kit (S0033S, Beyotime, Shanghai, China) according to the manufacturer’s instructions. In brief, the cells were incubated with diluted DCFH-DA reagent at 37°C for 30 min. Subsequently, the cells were digested with trypsin and harvested (Liao X. et al., 2019). The labeled cells were then detected by the Flow Cytometer (Beckman Coulter, Breya, California, United States). ## 2.12 Mitochondrial ROS detection A MitoSOX Red Mitochondrial Superoxide Indicator (40778ES50, Yeasen Biotechnology, Shanghai, China) was used to analyze mitochondrial ROS levels. The reagent was diluted with Hank’s equilibating solution (H1025, Solarbio, Beijing, China). Then, cells were incubated with the reagent for 30 min at 37°C in the dark (Zhang et al., 2019). Finally, the nuclei were stained with DAPI, and images were taken under a fluorescence microscope (Nikon Corporatin, Tokyo, Japan). ## 2.13 Western blot The brain samples at 24 h or 7 days after HI and cells were homogenized with RIPA buffer (R0010, Solarbio, Beijing, China) containing phenylmethane-sulfonyl fluoride (PMSF, P0100, Solarbio, China) and phosphatase inhibitors (P1260, Solarbio, Beijing, China). Nuclear proteins were extracted using a nuclear protein extraction kit (R0050, Solarbio, Beijing, China) according to the manufacturer’s instructions. Protein concentrations were quantified by the BCA kit (ZJ102L, Epizyme Biotech, Shanghai, China). The equal quantities of proteins were separated using $10\%$–$12\%$ sodium dodecyl sulfate-polyacrylamide (SDS-PAGE) gels and transferred to PVDF membranes. The membranes were blocked with $5\%$ skim milk for 3 h at room temperature and incubated with the corresponding primary antibodies (listed in Supplementary Table S1) overnight at 4°C. On the next day, the membranes were washed with Tris-buffered saline with Tween 20 (TBST) and incubated with HRP-Goat Anti-Rabbit IgG (1:5000, Proteintech, SA00001-2) or HRP-Goat Anti-Mouse IgG (1:5000, Proteintech, SA00001-1) secondary antibodies at room temperature for 2 h. The protein bands were visualized using enhanced chemiluminescence (ECL) reagents (MA0186, Meilunbio, Dalian, China) under the ChemiDoc XRS+ Imaging System (Bio-Rad, CA, United States) and analyzed by Image J software. The test indicators of brain samples 24 h after injury include: HIF-1α, Cleaved Caspase-3, BAX, BCL-2, NRF2, KEAP1, HO-1, NQO-1. The test indicators of brain samples 7 days after injury include: MAP-2 and MBP. ## 2.14 Statistical analysis All experiments were performed at least three times independently. The quantitative data were presented as mean ± SD. Statistical software Graphpad Prism 9.0 (GraphPad Software Inc., CA, United States) was used for statistical analysis. Statistical significance was analyzed by one-way analysis of variance (ANOVA) test followed by Tukey’s test when analyzing more than two groups. Student’s unpaired t-test was used for comparisons of two groups. When p-value < 0.05, the results were considered statistically significant. ## 3.1 Myricetin attenuated brain infarction and brain edema The chemical structure of myricetin (Figure 1A) and the timeline of experiment design for in vivo experiment (Figure 1B) are shown in this study. To investigate whether myricetin could reduce the damage of HI brain in the acute stage, at the 24 h after HI, TTC staining was performed to assess the brain infarction of rats in Sham, HI, and HI + Myr groups (Figure 1C). The quantitative results of infarct volume showed that there was no infarct in Sham group, and myricetin treatment could significantly reduce the brain infarct volume of HI-induced rats (Figure 1D). Notably, compared with Sham group, there was obvious edema in the ipsilateral cerebral in HI group, while myricetin administration could significantly reduce brain edema of HI-induced rats (Figure 1E). Moreover, the quantitative results of brain water content also showed that the Wet-dry ratio in HI group was significantly higher than that of Sham group, but myricetin intervention could remarkably alleviate this trend for HI-induced rats (Figure 1F). These data suggested that myricetin could effectively alleviate the brain injury in neonatal rats at the acute stage of hypoxic-ischemia. FIGURE 1: Myricetin attenuated brain infarction and edema. (A) The chemical structure of myricetin. (B) The timeline of experiment design for in vivo experiments. HIBD: hypoxic-ischemic brain damage. (C) The representative images of TTC staining of coronary brain sections 24 h after HI brain injury. (D) Quantitative analysis of infarct volume based on TTC staining (n = 5 per group). (E) The representative images of the brain from each group 24 h after HI brain injury. (F) The ratio of wet and dry in each group (n = 5 per group). The data are presented as mean ± SD. p < 0.05; **p < 0.001. Scale bar: 0.5 mm. ## 3.2 Myricetin promoted white matter recovery and alleviated brain damage To further assess the long-term neuroprotective effects of myricetin, we observed the brain anatomy at 7 days after HI (Figure 2A). The quantitative results of residual brain volume showed that compared with Sham group, the brain atrophy was obvious in HI group, and myricetin treatment could significantly alleviate the degree of brain atrophy in HI-induced rats (Figure 2B). Microtubule-associated protein-2 (MAP-2) is the main cytoskeletal regulator within neuronal dendrites that serves as a robust somatodendritic marker (DeGiosio et al., 2022). Myelin basic protein (MBP) is a key component of myelin sheath in the central nervous system that serves as an oligodendrocyte marker (Harauz et al., 2004). Western blot (Figure 2C) and immunohistochemical staining of MAP-2 (Figure 2E) in brain cortex and hippocampal CA3 region and MBP (Figure 2F) in callosum and striatum regions were performed to investigate whether myricetin could accelerate axonal repair and remyelination 7 days post-HI injury. The expression trends of MAP-2 and MBP proteins by Western blot were consistent with that of immunohistochemical staining. In addition, the quantitative results of Western blot showed that the expression of MAP-2 and MBP were significantly lower in HI group than that of Sham group, whereas myricetin could significantly reverse this trend (Figure 2D). FIGURE 2: Myricetin alleviated brain atrophy and promoted white matter recovery. (A) The representative images of the brain from each group 7 days after HI brain injury. (B) Quantitative analysis of residual brain volume (n = 6 per group). (C) The protein bands of MAP and MBP were detected by Western blot 7 days after HI brain injury. (D) Quantitative analysis of Western blot bands (n = 4 per group). (E) The immunohistochemical staining of MAP-2 in brain cortex and hippocampal CA3 region 7 days after HI brain injury. Black arrows: indicate the density difference of MAP-2. (F) The immunohistochemical staining of MBP in brain callosum and striatum region 7 days after HI brain injury. Black arrows: indicate the density difference of MBP. The data are presented as mean ± SD. p < 0.01, *p < 0.001. Scale bar: 200 μm. Moreover, the morphology of brain tissue at 7 days after HI was observed by HE staining (Figure 3A). Compared with sham group, the HE staining of brain cortex and hippocampal CA1, CA3, DG regions in HI group showed that the cells were extensively damaged, which were characterized by neuronal shrinkage and nuclear chromatin condensation. However, myricetin could significantly ameliorate these phenomena. Meanwhile, we measured the neuron numbers in those regions by Nissl staining (Figure 3B). The quantitative results revealed that compared with Sham group, a considerable of neurons were lost in the brain cortex (Figure 3C) and hippocampal CA1 (Figure 3D), CA3 (Figure 3E), DG (Figure 3F) regions of HI-induced rats, and these pathological changes were significantly alleviated by myricetin treatment. These results indicated that myricetin could reduce neuronal loss, promote morphological recovery, stabilize microtubule function, and promote myelination after HI brain injury in the long-term stage. FIGURE 3: Myricetin ameliorated the tissue structural damage and reduced the loss of neurons. (A) The representative images of HE staining in brain cortex and hippocampal CA1, CA3, DG regions 7 days after HI brain injury. Black arrows: indicate the morphology of brain tissue and cells. (B) The representative images of Nissl staining in brain cortex and hippocampal CA1, CA3, DG regions 7 days after HI brain injury. Black arrows: indicate the Nissl body integrity. (C) Quantification analysis of neuron numbers in brain cortex (n = 4 per group). (D) Quantification analysis of neuron numbers in hippocampal CA1 region (n = 4 per group). (E) Quantification analysis of neuron numbers in hippocampal CA3 region (n = 4 per group). (F) Quantification analysis of neuron numbers in hippocampal DG region (n = 4 per group). The data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001. Scale bar: 100 μm. ## 3.3 Myricetin inhibited cell apoptosis induced by HI brain injury In order to investigate whether myricetin exists anti-apoptosis effect on rat brain tissues, at 24 h after HI, Western blot was performed to detect the expression of apoptosis-related proteins in different groups (Figure 4A). HIF-1α, a crucial transcription factor in the cellular response to hypoxia, which is involved in diverse processes, such as angiogenesis, cell proliferation, and apoptosis/survival (Semenza, 2012). The quantitative results of Western blot showed that compared with Sham group, the expressions of HIF-1α, Cleaved Caspase-3, and BAX were significantly upregulated as well as the level of BCL-2 was significantly downregulated in HI group, and myricetin treatment could dramatically reverse these trends in HI induced rats (Figure 4B). Meanwhile, the TUNEL staining of brain cortex (Figure 4C) and hippocampal CA3 region (Figure 4E) areas was conducted to further assess the anti-apoptosis effect of myricetin. The quantitative data of TUNEL staining displayed that there was almost no TUNEL-positive cell in brain cortex and hippocampal CA3 region of Sham group, but the proportions of TUNEL-positive cells were noticeably increased in brain cortex (Figure 4D) and hippocampal CA3 region (Figure 4F) areas of HI group, which could be reversed by myricetin treatment. Those findings suggested that myricetin might exert anti-apoptosis effect to alleviate HI induced brain injury through inhibiting anti-oxidant transcription factor HIF-1α expression. FIGURE 4: Myricetin reduced apoptosis induced by hypoxic-ischemic brain injury. (A) The protein bands of HIF-1α, Cleaved Caspase-3, BAX, and BCL-2 were detected by Western blot 24 h after HI brain injury. (B) Quantitative analysis of Western blot bands (n = 4 per group). (C) The representative images of TUNEL staining in brain cortex 24 h after HI brain injury. Nucleus were stained with DAPI (blue). (D) Quantitative analysis of TUNEL staining in brain cortex (n = 4 per group). (E) The representative images of TUNEL staining in hippocampal CA3 region 24 h after HI brain injury. (F) Quantitative analysis of TUNEL staining in hippocampal CA3 region (n = 4 per group). The data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001. Scale bar: 100 μm. ## 3.4 Myricetin reduced glial activation and changed the protein expression of NRF2 pathway The oxidative stress is a well-known mechanism for the pathogenesis of hypoxia-ischemia brain damage (Vasiljevic et al., 2012). In addition, it has been reported that myricetin could produce potent anti-oxidant effects and effectively activate nuclear factor E2-related factor 2 (NRF2) (Liao H. H. et al., 2019). Thus, the expression levels of NRF2 protein in different groups were detected by Western blot (Figure 5A). The quantitative results of Western blot showed that the expressions of total NRF2 (T-NRF2) and nuclear NRF2 (N-NRF2) in HI group were higher than that of Sham group, and myricetin administration further upregulated the levels of T-NRF2 and N-NRF2 in HI-induced rats. Specifically, the ratio of T-NRF2/β-Actin increased from 0.82 ± 0.12 in HI group to 1.08 ± 0.14 in HI + Myr group, and the ratio of N-NRF2/Lamin B increased from 0.51 ± 0.14 to 0.99 ± 0.17. Meanwhile, myricetin treatment also increased HO-1 and NQO-1 expressions in HI-induced rats, which depended on the activity of transcriptional activator NRF2, and downregulated KEAP1 expression (Figure 5B). Moreover, compared with HI group, myricetin also downregulated MDA content (Figure 5C) and increased the content of GSH-Px (Figure 5D) in HI-induced rats. FIGURE 5: Myricetin attenuated excessive oxidative stress (ROS) and reduced glial activation through NRF2 pathway. (A) The protein bands of T-NRF2, N-NRF2, KEAP1, HO-1, and NQO-1 were evaluated by Western blot in brain tissues 24 h after HI injury. (B) Quantitative analysis of Western blot bands (n = 4 per group). (C) The MDA level in brain tissues 24 h after HI brain injury (n = 5 per group). (D) The GSH-px level in brain tissues 24 h after HI brain injury (n = 5 per group). (E) The representative immunofluorescence staining images of IBA-1 (green). Nucleus were stained with DAPI (blue). (F) The representative immunofluorescence staining images of GFAP (red). The data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001. Scale bar: 100 μm. The previous study had confirmed that ROS is also involved in the activation of glia cells, and the NRF2 signaling pathway plays a critical role in the regulation of oxidation/anti-oxidant imbalance in glia cells (Zhu et al., 2022). Therefore, immunofluorescence staining was conducted to detect the expression of IBA-1 that is a surface maker of activated microglia (Figure 5E) and GFAP that is a maker of reactive astrogliosis (Figure 5F) to explore whether myricetin could reduce the activation of glia cells. It was shown that compared with Sham group, the microglia and astrogliosis were markedly activated in HI group, and this trend could be reversed by myricetin administration. These data suggested that myricetin might play neuroprotective effects for HI injury through NRF2 pathway. ## 3.5 Myricetin alleviated CoCl2-induced PC12 injury To further ascertain the potential in vivo mechanism mentioned above, CoCl2 was used to simulate HI model for PC12 cells in vitro. The toxic effect of CoCl2 on PC12 cell viability was assessed by CCK-8 assay. The results showed that PC12 cells treated with various concentrations of CoCl2 (400–2000 μM) for 24 h displayed the dose-dependent decreasing cell viability compared with 0 μM. In addition, the viability of PC12 cells exposed to 800 μM CoCl2 for 24 h was decreased to $40.4\%$ ± $2.93\%$ (Figure 6A). So, the concentrations of 800 μM CoCl2 was chosen for the subsequent experiments. Then, the cytotoxicity of myricetin on PC12 cells was also detected by CCK-8 assay. Myricetin (10–200 μM) had no obvious adverse reaction on PC12 cell viability, while the cell viability decreased at dose of 300 μM (Figure 6B). PC12 cells were pretreated with various concentrations myricetin (10–200 μM) for 2 h before 800 μM of CoCl2 treatment, and the cell viability increased and reached the optimal at dose of 200 μM (Figure 6C). The images of PC12 cells were captured immediately after CoCl2 for 24 h in each group. Shrinkage and turn round of PC12 cells in CoCl2 group were observed through the optical microscope. Myricetin treatment could alleviate these phenomena and the most obvious effect was observed at dose of 200 μM (Figure 6D). FIGURE 6: Myricetin alleviated CoCl2-induced injury and mitochondrial ROS production in PC12 cells. (A) The cell viability of PC12 treated with different dose CoCl2 for 24 h using CCK-8 (n = 4 per group). (B) The safe dose ranges of myricetin to maintain cell viability was determined by CCK-8 assay (n = 4 per group). (C) The dose-dependent effect of myricetin on PC12 cell viability after CoCl2 injury (n = 4 per group). (D) The effects of myricetin at different doses on morphology of PC12 cells after CoCl2 injury. (E) The generation of Mitochondrial ROS in PC12 was detected by MitoSOX (red) and DAPI (blue) staining. (F) The images of Annexin V FITC (green) and PI (red) co-staining to detect apoptosis. The nuclei were labeled with Hoechest (blue). (G) Quantitative analysis of Annexin V and PI positive cells (n = 4 per group). The data are presented as mean ± SD. p < 0.01, *p < 0.001. Scale bar: 200 μm in (E); 100 μm in (F). In addition, the MitoSOX staining was performed to analyze the ROS level in each group. The results showed that CoCl2 could significantly induce the production of mitochondrial ROS (red staining), but myricetin intervention could markedly reduce the mitochondrial ROS level in CoCl2-induced PC12 cells (Figure 6E). Meanwhile, the co-staining of Annexin V and PI was used to detect CoCl2-induced PC12 apoptosis in vitro (Figure 6F). Cells in early apoptosis were PI negative and Annexin V positive, while in late apoptosis were both Annexin V and PI positive. The quantitative results showed that there was almost no Annexin V or PI positive cell in Control group, but CoCl2 could notably increase the production of Annexin V or PI positive cells, and myricetin treatment could significantly reverse this trend (Figure 6G). These data suggested that myricetin could alleviate CoCl2-induced PC12 cell injury through inhibiting mitochondrial ROS production and apoptosis. ## 3.6 Myricetin alleviated CoCl2-induced PC12 injury by activating NRF2 pathway To further explore the potential mechanism of myricetin on CoCl2-induced PC12 injury, the NRF2 typical antagonist ML385 was used to assess the relative proteins expressions of NRF2 pathway by Western blot (Figure 7A). The quantitative results showed that the levels of total NRF2 (T-NRF2) and nuclear NRF2 (N-NRF2) were higher in CoCl2-induced PC12 with the ratios of 0.71 ± 0.19 and 0.63 ± 0.05. In addition, myricetin treatment further increased the levels of T-NRF2 and N-NRF2 in CoCl2-induced PC12 and the ratio increased to 1.21 ± 0.16 and 1.12 ± 0.04. Meanwhile, myricetin treatment also increased HO-1 and NQO-1 levels but decreased KEAP1 level in CoCl2-induced PC12. Moreover, ML385 could significantly reverse the effect of myricetin in CoCl2-induced PC12. The quantitative results showed that after combined use of myricetin and ML385, the ratio of T-NRF2/β-Actin decreased to 0.78 ± 0.15 and the ratio of N-NRF2/Lamin B decreased to 0.83 ± 0.12 (Figure 7B). In addition, the immunofluorescence staining results of NRF2 (Figure 7C) and HO-1 (Figure 7D) were consistent with the Western blot. These data suggested that myricetin could alleviate CoCl2-induced PC12 injury through activating NRF2 pathway. FIGURE 7: Myricetin exerted neuroprotection through the activation of NRF2 pathway. (A) The protein bands of T-NRF2, N-NRF2, KEAP1, NQO-1, and HO-1 were detected by Western blot in PC12 from each group. (B) Quantitative analysis of Western blot bands (n = 4 per group). (C) The representative immunofluorescence staining images of NRF2 (red) in PC12 from each group. Nucleus were stained with DAPI (blue). (D) The representative immunofluorescence staining images of HO-1 (red) in PC12 from each group. The data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001. Scale bar: 10 μm. ## 3.7 Myricetin reduced oxidative stress and apoptosis in CoCl2-induced PC12 by activating NRF2 pathway In order to further demonstrate that myricetin could alleviate CoCl2-induced PC12 injury in vitro through activating NRF2 pathway, the NRF2 inhibitor ML385 was used to detect the oxidative stress and apoptosis in different groups. The MitoSOX staining showed that myricetin administration could significantly decrease the mitochondrial ROS level in CoCl2-induced PC12 cells, but the effect of myricetin combined with ML385 was relatively week (Figure 8A). In addition, the flow cytometry was used to detect the cell ROS levels in different groups (Figure 8B). The quantitative results showed that CoCl2 could obviously increase the level of cell ROS, but myricetin intervention could markedly reduce the cell ROS level in CoCl2-induced PC12 cells. Similarly, ML385 could reverse myricetin effect on CoCl2-induced PC12 (Figure 8C). Moreover, Western blot was conducted to detect the apoptosis-related protein expressions in different groups (Figure 8D). The quantitative results showed that CoCl2 could significantly upregulate the expressions of Cleaved Caspase-3 and BAX and downregulate the expression of BCL-2, but myricetin treatment could dramatically reverse these trends in CoCl2-induced PC12 cells. Similarly, combined with ML385, the effect of myricetin was relatively week (Figure 8E). The flow cytometry of Annexin V and PI assay was performed to detect the CoCl2-induced apoptosis of PC12 in different groups (Figure 8F). The percentages of apoptosis cells were calculated from the early apoptosis (Q3) and late apoptosis (Q2). The quantitative result was consistent with the above cell ROS results (Figure 8G). These findings further indicated that myricetin could attenuate CoCl2-induced cell oxidative stress and apoptosis to exert protective roles through NRF2 pathway. FIGURE 8: Myricetin reduced ROS production and apoptosis in PC12 cells through activating NRF2 pathway. (A) The generation of mitochondrial ROS in each group was detected by MitoSOX (red) and DAPI (blue) staining. (B) The cell ROS in each group was analyzed using flow cytometry after DCF-DA staining. (C) Quantitative analysis of flow cytometry after DCF-DA staining (n = 3 per group). (D) The protein bands of Cleaved Caspase-3, BAX, and BCL-2 were detected by Western blot. (E) Quantitative analysis of Western blot bands (n = 4 per group). (F) The apoptosis in each group was analyzed using flow cytometry after Annexin V FITC and PI co-staining. (G) Quantitative analysis of flow cytometry after Annexin V FITC and PI co-staining (n = 4 per group). The data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001. Scale bar: 200 μm. Based on the above findings, we carefully speculated that myricetin might attenuate oxidative stress and apoptosis through NRF2 pathway, thereby alleviating neonatal HI injury, which suggested that myricetin might be a promising therapeutic agent for HIE. The detail diagram of mechanisms involved in the effects of myricetin on HI injury was shown in Figure 9. FIGURE 9: Diagram of the potential mechanisms involved the neuroprotective effects of myricetin against HI-induced brain injury or CoCl2 induced PC12 injury. ## 4 Discussion Although the medical advances have improved the treatment of neonatal brain injury, hypoxic-ischemic encephalopathy (HIE) remains a concern. Hypothermia is the current standard treatment method for HIE, but it has limited effects on patients with moderate and severe injury, and has a narrow treatment time window (Nair and Kumar, 2018). Thus, there is an urgent need to search more effective therapy approaches for HIE. Currently, study had identified oxidative stress and apoptosis as the important factors for HIE pathological injury (Huang et al., 2019). In this study, we tried to explore the neuroprotective effects of myricetin on HI brain injury in vivo and in vitro, and illuminate the potential mechanism. Myricetin, a polyhydroxyflavonol compound isolated from myria rubra tree bark, has attracted extensive attention due to its beneficial pharmacological effects (Song et al., 2021). In recent years, several studies had shown that myricetin had anti-oxidant, anti-bacterial, anti-inflammatory, immunomodulatory, and anti-cancer effects (Alcaraz and Ferrandiz, 2020; Taheri et al., 2020). Myricetin can alleviate neuroinflammation caused by LPS (Jang et al., 2020), reduce diabetic neuropathy (Ma S et al., 2022), and improve learning and memory functions in Alzheimer’s disease (Ramezani et al., 2016), indicating that it has neuroprotective effects. However, there is no report to demonstrate whether myricetin has the protective effects on HI-induced brain damage. Herein, we tried to verify the protective roles of myricetin on HI-induced brain injury. Our results in vivo showed that myricetin could not only alleviate acute brain injury caused by HI, but also protect the structural integrity of brain tissues in long-term. Our data in vitro displayed that myricetin could promote cell viability and reduce cell apoptosis as well as ROS production in CoCl2-induced PC12. In addition, both in vivo and in vitro results showed that myricetin could upregulate the expressions of NRF2 pathway related indicators, which could be reversed by ML385, the specific inhibitor of NRF2. Thus, we speculated that NRF2 signaling pathway might be involved in the protective mechanisms of myricetin in HI brain injury. The pathogenic processes of HI injury are complex, and its pathological mechanism is classified into three phases. Initially, the reduced cerebral blood flow results in anaerobic metabolism, accumulation of extra-cellular amino acids, and cell swelling (Greco et al., 2020). Then, reperfusion leads to mitochondrial dysfunction, large amounts of ROS production, and the releases of intracellular calcium as well as various inflammatory factors (Arteaga et al., 2017). The imbalance between ROS and antioxidants leads to oxidative stress (Salim, 2017), which causes protein oxidation, lipid peroxidation, and DNA destruction (Hassan et al., 2022). The neonatal brains are more susceptible to oxidative stress due to high rate of oxygen consumption, rich in unsaturated fatty acids and metal ions, and weak antioxidant capacity (Qin et al., 2019). In vivo HI-induced brain injury model, the content of MDA was increased, but the level of antioxidant GSH-Px. Mitochondria plays a key role in the process of oxidative stress, and is the main site for ROS production (Yildiz et al., 2017). The results of MitoSOX fluorescence staining and ROS flow cytometry showed that the mitochondria ROS and the total intracellular ROS were significantly increased after HI injury. Meanwhile, myricetin could upregulate the antioxidant level and reduce the levels of both the mitochondrial and total intracellular ROS. Moreover, cells also can induce endogenous antioxidant production to limit free radical reactions (Patel, 2016). NRF2 (nuclear factor-E2-related factor 2) is a transcription factor that can induce a set of anti-oxidants and detoxication enzymes expressions, and can be regulated by KEAP1 (Sies et al., 2017). In basal condition, NRF2 is present in cytoplasm bond to the KEAP1 which acts as a substrate adaptor for CUL3 ubiquitin ligase-mediated proteasomal degradation of NRF2 (Todorovic et al., 2016). Thus, NRF2 protein degrades rapidly in normal cells, and remains low in abundance. In response to oxidative stress, the structure of KEAP1/NRF2/CUL3 complex begins to change, thereby inhibiting NRF2 ubiquitination to cause NRF2 translocation into the nucleus (Ma, 2013). After entering the nucleus, NRF2 can be heterodimerized with small MAF proteins (sMAF), and then bind to anti-oxidant response element (ARE) to promote the transcription of target genes. Both HO-1 and NQO-1 are belonged to the downstream target genes of NRF2 (Tonelli et al., 2018), which play the anti-oxidative stress role. Consistent with the results of previous studies (Xie et al., 2021; Li et al., 2022), our in vivo and in vitro experiment results showed that the nuclear translocation of NRF2 was promoted in the acute phase of HI induced injury, and the protein expressions of NRF2, HO-1, and NQO-1 were upregulated. Zhang et al. [ 2016] found that myricetin could alleviate cuprizone induced behavioral dysfunction and demyelination in mice by activating NRF2. Similarly, our study found that NRF2 was significantly activated to undergo nuclear transfer after myricetin intervention, and the expressions of endogenous anti-oxidant enzymes were further increased. In addition, NRF2 can also regulate the levels of anti-oxidant enzymes, such as SOD, CAT, and GSH-Px. Myricetin could increase the amount of these enzymes, which is also consistent with experimental results mentioned above. The role of glial cells in HIE should not be ignored, and NRF2 is one of the important factors in regulating the activity of glial cells. Liu et al. [ 2020] had identified that compared with WT mice, HI could trigger more obvious activation of microglia, and evoke more severe proliferation of astroglia in Nrf2 −/− mice. In our study, myricetin could alleviate glial activation, so we hypothesized that it might also act through activating NRF2 pathway. To further verify our hypothesis, the NRF2 specific inhibitor ML385 was used in this study. As expected, ML385 intervention significantly counteracted the anti-oxidant and anti-apoptosis effects of myricetin, reduced NRF2 nuclear translocation, and downregulated anti-oxidant enzyme activities. Mitochondria are the main sites for ROS production, but they are also the main targets for ROS attack, forming a vicious cycle between each other (Jezek et al., 2018). The accumulation of ROS caused by hypoxia can lead to the decrease of mitochondrial membrane potential and the increase of membrane permeability. Then, cytochrome C is released into the cytoplasm and initiates the apoptotic Caspase-$\frac{9}{3}$ activation cascade to fragment DNA (Zhao et al., 2016). This process can be regulated by BCL-2 family proteins, including anti-apoptotic proteins, such as BCL-2, BCL-xl, and MCL-1 as well as pro-apoptotic proteins, such as BAX, BAD, etc. ( Yang et al., 2020; Chen et al., 2022). During the pathological development of neonatal HI brain injury, apoptosis is the more prevalent mode of death, and is more severe in the immature brain (Lai and Yang, 2011). Therefore, apoptosis intervention will effectively improve the prognosis of HIE. Zhang et al. [ 2018] verified that myricetin could attenuate cardiomyocyte apoptosis induced by LPS. In the present study, myricetin treatment could significantly downregulate the expressions of BAX and Cleaved Caspase-3, increase the level of BCL-2, and reduce the numbers of TUNEL-positive cells in neonatal HI brain of rats. The same effects were observed in vitro experiments. There are accumulating evidences that NRF2 is involved in the regulation of apoptosis, and NRF2-deficient cells show increased spontaneous apoptosis (Ma, 2013; Lv et al., 2020). Thus, we wondered whether myricetin could also act against apoptosis through activation of NRF2 pathway. Our results showed that compared with myricetin treatment, ML385 intervention could increase the number of apoptosis and upregulate the expressions of apoptosis proteins in CoCl2-induced PC12 cells. On the one hand, we hypothesized that myricetin can reduce apoptosis by alleviating oxidative stress, but further studies are needed to explore the underlying mechanism. In this study, the current results suggested that myricetin played a neuroprotective role in the neonatal rat brain after HI injury by alleviating oxidative stress and apoptosis through activating NRF2 signaling pathway. However, there are limitations of our study. Firstly, ML385 was only used to detect the protective effect of myricetin in vitro, and the further studies on inhibiting NRF2 in vivo are needed. In addition, we need to measure the motor learning ability of rats in each group to reflect the long-term protective effect of myricetin. Moreover, we should also analyze the kinetics of NRF2 decay to determine whether myricetin affects NRF2 protein stability, which will help to further elucidate the mechanism of myricetin. Finally, although PC12 cells are widely used in nervous system studies, in order to simulate HI conditions in vivo, it is better to extract primary cortical nerve cells for oxygen-glucose deprivation experiment. ## 5 Conclusion In summary, the oral administration of myricetin played neuroprotective roles in HI brain damage rats which could significantly reduce the infarct volume and improve long-term neurological prognosis. Furthermore, myricetin intervention could inhibit oxidative stress and apoptosis. Then, the anti-oxidative stress and anti-apoptosis effects of myricetin might be involved in NRF2 signaling pathway. Taken together, our results suggested that myricetin might serve as a potential therapeutic drug for HI brain injury. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by Ethics Committee of Laboratory Animals of Wenzhou Medical University. ## Author contributions TC designed the research and performed the majority of the study. TC and YH drafted the manuscript. LL helped create the figures and write parts of the manuscript. QZ contributed to the analysis of the data. XT and BD helped create parts of the figures. SC designed part of this study. JZ, XG, and ZL contributed to critical revisions of the manuscript. All authors contributed to the article and approved the final version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1134464/full#supplementary-material ## References 1. Alcaraz M. J., Ferrandiz M. L.. Relevance of Nrf2 and heme oxygenase-1 in articular diseases. Free Radic. Biol. Med. (2020) 157 83-93. 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--- title: 'Association of arterial stiffness with all-cause and cause-specific mortality in the diabetic population: A national cohort study' authors: - Cun Liu - Huachun Pan - Fanliang Kong - Shumin Yang - Quazi T. H. Shubhra - Dandan Li - Siwei Chen journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031114 doi: 10.3389/fendo.2023.1145914 license: CC BY 4.0 --- # Association of arterial stiffness with all-cause and cause-specific mortality in the diabetic population: A national cohort study ## Abstract ### Background Estimated pulse wave velocity (ePWV) has been proposed as a potential alternative to carotid-femoral pulse wave velocity to assess the degree of aortic stiffness, and may predict cardiovascular disease (CVD) outcomes and mortality in the general population. However, whether arterial stiffness estimated by ePWV predicts all-cause and cause-specific mortality in patients with diabetes mellitus (DM) has not been reported. ### Methods This was a prospective cohort study with data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014 and followed up until the end of December 2019. 5,235U.S. adults with DM (age≥20years) were included in the study. Arterial stiffness was estimated by ePWV. Survey-weighted Cox proportional hazards models were performed to assess the hazard ratios (HRs), and $95\%$ confidence intervals (CIs) for the associations of ePWV with all-cause and cause-specific mortality. Meanwhile, the generalized additive model was used to visually assess the dose-dependent relationship between ePWV and mortality. As a complementary analysis, the relationship between mean blood pressure (MBP) and risk of mortality was also examined. Multiple imputations accounted for missing data. ### Results For the 5,235 DM patients, the weighted mean age was 57.4 years, and $51.07\%$ were male. During a median follow-up period of 115 months (interquartile range 81-155 months; 53,159 person-years), 1,604 all-cause deaths were recorded. In the fully adjusted Cox regression model, every 1 m/s increase in ePWV was associated with $56\%$ (HR 1.56; $95\%$ CI, 1.44 to 1.69) increase in the risk of all-cause. In addition, a nonlinear relationship between ePWV and all-cause mortality was observed (P for non-linear=0.033). Similar results were obtained after subgroup analysis and multiple imputations. Besides, the risk of most cause-specific mortality, except for accident and renal disease-specific mortality, increased from $53\%$ to $102\%$ for every 1 m/s increase in ePWV. ### Conclusions In the diabetic population, ePWV is independently associated with all-cause and most cause-specific mortality risks. ePWV may be a useful tool for assessing mortality risk. ## Graphical abstract 41,597 US adults enrolled in NHANES 1999-2014, with follow-up until the end of December 2019. After excluding non-diabetics, pregnant women, people with cancer and people without ePWV data, 5235 diabetics were finally included in the analysis. Arterial stiffness was estimated by ePWV. The results showed a non-linear positive association between ePWV and the risk of all-cause mortality(P for non-linear=0.033). When ePWV was<7.2m/s, the risk of all-cause mortality increased by 2.1-fold (HR 3.10; $95\%$ CI, 1.54 to 6.25; $$P \leq 0.002$$) with an increase in ePWV of 1 m/s; when ePWV was≥7.2m/s, the risk of all-cause mortality increased by $54\%$(HR 1.54; $95\%$ CI, 1.44 to 1.64; $P \leq 0.001$). In addition, the risk of cause-specific mortality increased from $53\%$ to $102\%$ for every 1m/s increase in ePWV. Graphical abstract created with BioRender.com. NHANES, National Health and Nutrition Examination Survey. ePWV, estimated pulse wave velocity. ## Introduction Atherosclerosis is a widespread chronic inflammatory disease of the arterial wall and manifests mainly as lesions and plaque accumulation in the intimal layers of the arterial wall [1]. Numerous studies have shown that diabetes and atherosclerosis coexist in multiple pathophysiological pathways, and diabetes mellitus (DM) accelerates the progression of atherosclerosis [1, 2]. Plaque formation in a large artery (e.g., the aorta) causes the narrowing of the lumen and stiffening of the arterial wall and exposes the target organ to potentially damaging hemodynamic forces [3]. Arterial stiffness is a clinical hallmark of arterial aging. Measures of aortic stiffness can reflect the severity of vascular stiffness and capture the vessel’s age. This further provides additional insight into cardiovascular disease (CVD) risk beyond the traditional CVD risk factors [4]. Currently, carotid-femoral pulse wave velocity (cfPWV) is the gold standard and a widely used method in epidemiological studies to assess central arterial stiffness [5, 6]. However, the measurement of cfPWV requires specific equipment, specialized technical proficiency, and special procedures that limit its clinical application. For this reason, a potential alternative to cfPWV, the estimated pulse wave velocity (ePWV), has been proposed [7]. ePWV, as calculated by age and blood pressure, can predict CVD outcomes and mortality in the general population, independently of or beyond traditional CVD risk factors [8]. In addition, several studies have shown that ePWV is robustly associated with cardiovascular events (e.g. coronary heart disease, stroke) (9–11). This suggests that ePWV may be an important predictor of the risk of developing disease and mortality. However, the long-term prognostic potential of ePWV in diabetic patients remains to be elucidated. Therefore, in this prospective study, we analyzed national sample data from the National Health and Nutrition Examination Survey (NHANES) to investigate whether arterial stiffness estimated by ePWV predicts all-cause and specific-cause mortality in individuals with DM. ## Study design and population This prospective cohort study was conducted using the national data from the NHANES from 1999 to 2014 and followed up until the end of December 2019. NHANES is a nationally representative survey of the civilian, non-institutional population in the United States. The survey includes interviews, physical examinations at home or mobile examination centers (MEC), and laboratory tests and is administered by the National Center for Health Statistics using a complex, stratified, multi-stage probability design. The survey is conducted every two years. Detailed sampling methods and the process of data collection have been published earlier [12]. NHANES was implemented by the National Center for Health Statistics of the US Centers for Disease Control and Prevention (CDC) and approved by the National Center for Health Statistics Institutional Review Board. All participants provided written informed consent. A total of 41,597 adults (age ≥20 years) from the USA participated in the eight survey cycles of the NHANES spanning 1999 to 2014. The diagnosis of diabetes was based on self-reported (as told by a medical doctor), use of insulin, or oral hypoglycemic drugs, fasting glucose≥7.0 mmol/L (126mg/dL), random blood glucose or two-hour oral glucose tolerance test blood glucose≥11.1 mmol/L or glycohemoglobin HbA1c≥$6.5\%$. Finally, 6,762 individuals with diabetes were identified; ePWV data of 6,349 individuals were available; participants with cancer or pregnancy ($$n = 878$$; self-reported) were further excluded. Concurrently, to reduce potential reverse causality bias, participants who died within two years of follow-up ($$n = 236$$) were excluded. Ultimately, 5,235 individuals, representing 19,472,771 individuals with diabetes in the U.S., were included in the analysis (Figure 1). Figure 1: The study flow. ## Calculation of estimated pulse wave velocity The ePWV is calculated using the following formula [8]. In this formula, age is in years, and mean blood pressure (MBP) is calculated by diastolic blood pressure (DBP)+0.4×[systolic blood pressure (SBP)−DBP]. Participants were placed in a quiet sitting position for 5 min, and then a trained examiner used a uniform sphygmomanometer to measure the blood pressure. The blood pressure values were the average of at least three determinations. The technique for measuring blood pressure is as per the then latest recommendations of the American Heart Association Human Blood Pressure Determination using sphygmomanometers. Detailed information about the quality assurance & quality control process has been described in the Physician Section of the MEC Operations Manual [13]. ## Outcome definition The primary outcome of this article was all-cause mortality. The secondary outcomes were the nine cause-specific and residual-specific mortality reported by NHANES. Mortality for all-cause, cause- and residual-specific, was ascertained by linkage to the National Mortality Index until 31 December 2019. All-cause mortality included deaths from all the causes. Residual mortality is all the deaths except for the nine cause-specific mortality. Cause-specific mortality was coded by the International Classification of Diseases version 10 (ICD-10). The ICD codes for the nine cause-specific mortality are shown in Table S1. ## Other variables of interest In this study, a set of some others variables were taken into consideration. Information on age, gender, race, level of education, family income, smoking and drinking status, history of diseases, and medication use was collected from home interviews and mobile examination centers utilizing standardized questionnaires. Health status/medical history was obtained through a self-reported face-to-face interview. The questionnaire section is usually modelled on medical conditions questionnaire section of the U.S. National Health Interview Survey. These questions were asked by trained interviewers in the home using the computer assisted personal interviewing system. Take CVD for example. The interviewers would ask the participants the following questions: Have you ever been told by a doctor or other health professional that you have CVD?/how old were you when you were first told that you had CVD? In this paper, CVD was defined as a composite of diseases including coronary heart disease, congestive heart failure, heart attack, stroke, and angina. The participants had at least one of these conditions and were assumed to have CVD. Biochemical parameters were measured through a rigorous process, details of which can be found in the NHANES Procedures Manual for Laboratory/Medical Technologists [13]. The participant’s blood pressure, weight, and height were measured at the mobile examination center. To facilitate data integration, we further classified the following variables: ## Statistical analysis In the data analysis process, appropriate weights (MEC weights) were selected to account for oversampling, nonresponse, and noncoverage and to provide nationally representative estimates. Detailed weighting methods are available on the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/tutorials/Module3.aspx). In demographic baseline characteristics, continuous variables are represented by means (standard error, SE), and categorical variables are represented by unweighted counts (weighted %). Levels of ePWV, as a continuous variable, were divided into quartiles, and Schoenfeld residuals verified the proportional hazard assumption. Kaplan-Meier analyses with a log-rank test were employed to analyze the cumulative survival of the diabetic population with different ePWV levels during the observation period. Survey-weighted Cox proportional hazards models were performed to assess the hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for the associations of ePWV with all-cause and cause-specific mortality. To reduce collinearity between age and ePWV, age is transformed into a categorical variable as it is directly involved in the calculation of ePWV. In addition, according to the classification of confounding covariates, they were progressively added to different models (Model 1-6). The fully adjusted model was conducted including all the baseline characteristics. Subgroups analyses were conducted stratified by the following clinical characteristics: gender (male, female), age (<45,45-64,≥65 years), BMI (<30,≥30 kg/m2), diastolic blood pressure (<90, ≥90mmHg), systolic blood pressure (<140, ≥140mmHg), race (non-Hispanic white, non-Hispanic black, Mexican American, and others), asthma (no/yes), arthritis(no/yes), CVD (no/yes), hypertension (no/yes), chronic bronchitis (no/yes),and chronic kidney disease (CKD) (no/yes), and the P-values for interaction were obtained. Besides, the generalized additive model (GAM) was used to assess the dose-response relationship between ePWV and mortality visually, and the P-value for non-linear was obtained using the log-likelihood ratio test. As a complementary analysis, the relationship between MBP and the risk of mortality was assessed. GAM was performed using both log-transformed and untransformed methods. Log (HR) was converted to HR by taking antilog. A log (HR) of 0 and 1 implies a HR of 1 (not significant) and 2.71-fold, respectively [16]. Threshold-effect analysis was employed to assess changes in the risk of all-cause mortality and other cause-specific mortality with increases in ePWV and MBP specific units. If a non-linear association was observed, the two-piecewise linear regression model was conducted to calculate the inflection point at which the ratio of ePWV and mortality changed markedly in the smoothed curve [17]. For the missing variables, multiple imputations were performed based on the five-repetition predictive mean matching algorithm and Markov Chain Monte Carlo method, and the pooled results by the Cox regression model were considered as a form of sensitivity analysis [18]. All analyses were conducted through the statistical packages R (http://www.R-project.org, The R Foundation) and EmpowerStats (Version 4.2.0, www.R-project.org, X&Y Solutions, Inc., Boston, MA). Two-tailed P-values less than 0.05 were considered statistically significant. ## Results The current study analyzes the NHANES data from 1999 to 2014. A total of 5,235 individuals with DM were included. Their weighted mean age was 57.4 years, and $51.07\%$ of them were male. The weighted overall demographic characteristics are mentioned in Table 1. During a median follow-up period of 115 months (interquartile range 81-155 months; 637,911 person-years), 1,604 all-cause deaths were recorded. Table 1 \| ePWV \| 9.26 (0.04) \| \| --- \| --- \| \| Demographic \| Demographic \| \| Age (years) \| 57.4 (0.29) \| \| 20-44 \| 752 (18.37) \| \| 45-64 \| 2,353 (48.54) \| \| ≥65 \| 2,130 (33.09) \| \| Male \| 2,677 (51.07) \| \| Race \| Race \| \| Non-Hispanic white \| 1,780 (58.90) \| \| Non-Hispanic black \| 1,388 (16.22) \| \| Mexican American \| 1,195 (10.08) \| \| Other races \| 872 (14.80) \| \| Poverty income ratio \| 2.67 (0.04) \| \| Parameters \| Parameters \| \| Body mass index \| 32.77 (0.17) \| \| Waist \| 109.74 (0.40) \| \| Systolic blood pressure \| 70.61 (0.32) \| \| Diastolic blood pressure \| 71.37 (0.16) \| \| Glycated hemoglobinA1c \| 7.23 (0.04) \| \| Creatinine \| 83.96 (0.88) \| \| Estimated glomerular filtration rate \| 85.75 (0.42) \| \| Total cholesterol \| 5.03 (0.03) \| \| High-density lipoprotein cholesterol \| 1.22 (0.01) \| \| History of diseases \| History of diseases \| \| Cardiovascular diseases \| 1,231 (22.37) \| \| Chronic kidney disease \| 2,030 (36.15) \| \| Asthma \| 734 (14.61) \| \| Chronic bronchitis \| 392 (8.14) \| \| Hypertension \| 3,777 (69.34) \| \| Arthritis \| 2,160 (40.97) \| \| Medication \| Medication \| \| Antihypertensives \| 3,777 (69.34) \| \| Glucose-lowering drugs \| 3,165 (59.25) \| \| Lifestyle \| Lifestyle \| \| Smoking \| Smoking \| \| Never \| 2,600 (49.53) \| \| Former \| 1,695 (32.14) \| \| Current \| 934 (18.33) \| \| Drinking \| Drinking \| \| Never \| 911 (16.44) \| \| Former \| 1,504 (27.32) \| \| Mild/Moderate \| 1,373 (31.64) \| \| Heavy \| 1,107 (24.60) \| ## ePWV and all-cause mortality A graded positive association was observed between increasing quartiles of ePWV and the risk of all-cause mortality, as shown by Kaplan-Meier curves, either in the crude (Q4 vs. Q1: 7.98; $95\%$ CI:6.32 to 10.07; log-rank test $P \leq 0.001$) or multivariate-adjusted models (Q4 vs. Q1: 3.59; $95\%$ CI:2.15 to 5.98; $P \leq 0.001$) (Figure 2A). In the unadjusted Cox regression model, the risk of all-cause mortality increased by $46\%$ (HR 1.46; $95\%$ CI, 1.41 to 1.52; $P \leq 0.001$) with an increase of 1m/s in ePWV (Table 2). Similarly, this hazard risk persisted after adjustment for age and blood pressure (HR 1.56; $95\%$ CI, 1.46 to 1.66; $P \leq 0.001$). Meanwhile, in the model fully adjusted for confounders, with every 1 m/s increase in ePWV, the risk of all-cause mortality increased correspondingly by $56\%$ (HR 1.56; $95\%$ CI, 1.44 to 1.69; $P \leq 0.001$), which was comparable to the results after multiple imputations (HR 1.55; $95\%$ CI, 1.46 to 1.66; $P \leq 0.001$) (Table S2). Figure 2: (A) Kaplan-Meier survival curves for all-cause mortality, according to ePWV quartile levels. Participants’ ePWV levels were grouped into quartiles, and crude and multivariate-adjusted model results were expressed as HRs and $95\%$ CIs. Multivariate-adjusted model adjusted for the following variables: age, systolic blood pressure, diastolic blood pressure, gender, race, poverty income ratio, body mass index, waist, glycated hemoglobinA1c, total physical activity, creatinine, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives and glucose-lowering drugs, smoking, drinking. HRs, hazard ratios. CIs, confidence intervals. (B) Associations between ePWV with the risk of all-cause mortality. Results were expressed as HRs and $95\%$ CIs. In the generalized additive model, these following variables were fully adjusted: age, systolic blood pressure, diastolic blood pressure, gender, race, poverty income ratio, body mass index, waist, glycated hemoglobinA1c, total physical activity, creatinine, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives and glucose-lowering drugs, smoking, drinking. HRs, hazard ratios. CIs, confidence intervals. (C) Associations between mean blood pressure levels with the risk of all-cause mortality. Results were expressed as HRs and $95\%$ CIs. In the generalized additive model, these following variables were fully adjusted: age, gender, race, poverty income ratio, body mass index, waist, glycated hemoglobinA1c, total physical activity, creatinine, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, arthritis, antihypertensives and glucose-lowering drugs, smoking, drinking. HRs, hazard ratios. CIs, confidence intervals. TABLE_PLACEHOLDER:Table 2 In the subgroup analyses, the positive association between ePWV and risk of all-cause mortality was consistent across most strata ($$p \leq 0.061$$-0.954), and the risk of all-cause mortality increased by $43\%$-$114\%$ with the increase of 1m/s ePWV (Figure 3). However, interactions were observed in CVD and hypertension groups (P for interaction<0.05), suggesting that the strength of the association between ePWV and all-cause mortality risk may differ in these groups. The positive association remained between ePWV and risk of all-cause mortality regardless of grouping. Figure 3: Stratified analyses of the associations (hazard ratios, 95% CIs) between ePWV values and all-cause mortality in patients with DM. HRs were fully adjusted by the following covariates including age, systolic blood pressure, diastolic blood pressure, gender, race, poverty income ratio, body mass index, waist, glycated hemoglobinA1c, total physical activity, creatinine, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, arthritis, antihypertensives and glucose-lowering drugs, smoking, drinking. ## ePWV and cause-specific mortality In the unadjusted Cox regression model, there was a positive association between ePWV and the risk of cause-specific mortality (Table 2). With increasing levels of 1m/s ePWV, the increased risk of special-cause mortality ranged from 21-$97\%$. However, after adjustment for age and blood pressure levels, ePWV remained positively associated with most cause-specific mortality except for renal disease and accident-specific mortality. Meanwhile, after fully adjusting for confounding, each 1m/s increase was associated with a $53\%$ (HR 1.53; $95\%$ CI, 1.27 to 2.83), $91\%$ (HR 1.91; $95\%$ CI, 1.30 to 2.80), $65\%$ (HR 1.65; $95\%$ CI, 1.03 to 2.65), $102\%$ (HR 2.02; $95\%$ CI, 1.15 to 3.57), $56\%$ (HR 1.56; $95\%$ CI, 1.19 to 2.05),$54\%$ (HR 1.54; $95\%$ CI, 1.22 to 1.96), $98\%$ (HR 1.98; $95\%$ CI, 1.10 to 3.55), and $54\%$ (HR 1.54; $95\%$ CI, 1.27 to 1.86) increase in CVD, cerebrovascular disease, respiratory disease, Alzheimer’s disease, DM, cancer, influenza and pneumonia, and residual-specific mortality, respectively (Table 2). Likewise, comparable results were obtained after multiple imputations (Table S2). ## Dose-dependent relationship between ePWV levels and risk of all-cause and cause-specific mortality A positive nonlinear association existed between ePWV levels and the risk of all-cause mortality (P for non-linear=0.033) (Figure 2B). In addition, threshold effect analysis showed a 2.1-fold (HR 3.10; $95\%$ CI 1.54 to 6.25; $$P \leq 0.002$$) increase in the risk of all-cause mortality for every 1 m/s increase in ePWV when ePWV was<7.2 m/s. There was a $54\%$ (HR 1.54; $95\%$ CI 1.44 to 1.64; $P \leq 0.001$) increase in the risk of all-cause mortality for every 1 m/s increase in ePWV when ePWV was ≥7.2 m/s (Table 3). A linear relationship between ePWV and the risk of all-cause mortality was also reported, as the P for non-linear was very close to 0.05. For each 1 m/s increase in ePWV, the risk of all-cause mortality increased by $55\%$ (HR 1.55; $95\%$ CI 1.44 to 1.65; $P \leq 0.001$). Table 3 \| Inflection-point \| HR \| p-value \| p for non-linear \| Unnamed: 4 \| HR.1 \| p-value.1 \| p for non-linear.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| ePWV (1m/s increase) \| ePWV (1m/s increase) \| ePWV (1m/s increase) \| \| Mean blood pressure (5 mmHg increase) \| Mean blood pressure (5 mmHg increase) \| Mean blood pressure (5 mmHg increase) \| \| All-cause Mortality \| \| \| 0.033 \| All-cause Mortality \| 1.01 (0.99-1.03) \| 0.302 \| 0.131 \| \| <7.2 \| 3.10 (1.54-6.25) \| 0.002 \| \| \| \| \| \| \| ≥7.2 \| 1.54 (1.44-1.64) \| <0.001 \| \| \| \| \| \| \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| Cause-specific Mortality \| \| Cardiovascular diseases \| \| \| 0.038 \| Cardiovascular diseases \| \| \| 0.029 \| \| <7.09 \| 7.23 (1.25-41.72) \| 0.027 \| \| <97mmHg \| 0.95 (0.89-1.02) \| 0.166 \| \| \| ≥7.09 \| 1.54 (1.36-1.74) \| <0.001 \| \| ≥97mmHg \| 1.08 (1.01-1.16) \| 0.018 \| \| \| Cerebrovascular diseases \| \| \| 0.001 \| Cerebrovascular disease \| 1.06 (0.96-1.17) \| 0.282 \| 0.157 \| \| <7.94 \| Inf. (0.0-Inf.) \| 0.550 \| \| Cancer \| 0.92 (0.87-0.98) \| 0.010 \| 0.443 \| \| ≥7.94 \| 2.03 (1.51-2.74) \| <0.001 \| \| Influenza and pneumonia \| 1.16 (1.00-1.34) \| 0.049 \| 0.168 \| \| Cancer \| \| \| 0.030 \| Respiratory diseases \| 0.93 (0.83-1.04) \| 0.20 \| 0.288 \| \| <8.72 \| 1.71 (1.41-2.06) \| <0.001 \| \| Alzheimer's disease \| 1.01 (0.88-1.16) \| 0.890 \| 0.479 \| \| ≥8.72 \| 0.67 (0.26-1.70) \| 0.396 \| \| Diabetes mellitus \| \| \| 0.050 \| \| Influenza and pneumonia \| \| \| 0.046 \| <94 mmHg \| 1.22 (1.03-1.44) \| 0.021 \| \| \| <12.83 \| 1.34 (1.06-1.69) \| 0.013 \| \| ≥94 mmHg \| 0.97 (0.87-1.09) \| 0.590 \| \| \| ≥12.83 \| 3.70 (2.22-6.19) \| <0.001 \| \| Residual mortality \| 1.04 (0.99-1.09) \| 0.085 \| 0.140 \| \| Respiratory diseases \| \| \| 0.011 \| \| \| \| \| \| <11.37 \| 2.32 (1.46-3.69) \| <0.001 \| \| \| \| \| \| \| ≥11.37 \| 0.98 (0.57-1.72) \| 0.957 \| \| \| \| \| \| \| Alzheimer's disease \| 2.31 (1.55-3.43) \| <0.001 \| 0.069 \| \| \| \| \| \| Diabetes mellitus \| 1.47 (1.19-1.82) \| <0.001 \| 0.353 \| \| \| \| \| \| Residual mortality \| 1.47 (1.27-1.69) \| <0.001 \| 0.093 \| \| \| \| \| Similarly, non-linear positive associations were observed between ePWV levels and risk of CVD (P for non-linear=0.038), cerebrovascular disease (P for non-linear=0.001), cancer (P for non-linear=0.03), influenza and pneumonia (P for non-linear=0.046), and respiratory disease mortality (P for non-linear=0.011) (Table 3; Figure S1–S7). However, ePWV levels were linearly positively associated with the risk of Alzheimer’s disease (P for non-linear=0.069), DM (P for non-linear=0.353), and residual mortality (P for non-linear=0.093) (Table 3). In addition, we additionally analyzed the association of MBP with mortality. MBP was not significantly associated with the risk of all-cause and most cause-specific mortality (all p value > 0.05), except for CVD and DM mortality (Table 3; Figure 2C; Figure S1–S7). MBP was nonlinearly associated with the risk of CVD mortality (P for non-linear=0.029); when MBP was<97 mmHg, MBP was not significantly associated with the risk of CVD mortality (HR 0.95; $95\%$ CI 0.89 to 1.02; $$P \leq 0.166$$); however, when MBP was ≥97 mmHg, the risk of CVD mortality increased by $8\%$ (HR 1.08; $95\%$ CI 1.01 to 1.16; $$P \leq 0.018$$) for every 5 mmHg increase in MBP (Table 3). Similarly, we observed a non-linear relationship between MBP and the risk of DM mortality (P for non-linear=0.050); when MBP was<94 mmHg, there was a corresponding $22\%$ (HR 1.22; $95\%$ CI 1.03 to 1.44; $$P \leq 0.021$$) increase in the risk of DM mortality for every 5 mmHg increase in MBP (Table 3). ## Discussion The current findings suggest that ePWV is an independent risk factor for all-cause and most cause-specific mortality in patients with DM, except for accident- and renal disease-specific mortality. ePWV was nonlinearly and positively associated with the risk of all-cause mortality. In addition, among cause-specific mortality, ePWV was nonlinearly positively associated with the risk of CVD, cerebrovascular disease, cancer, influenza and pneumonia, and respiratory disease mortality. It showed linearly positive association with the risk of Alzheimer’s disease, DM, and residual-specific mortality. These positive associations were independent of age and blood pressure levels. In addition, MBP was not significantly associated with the risk of all-cause and most cause-specific mortality and was only non-linearly associated with mortality from CVD and DM. Within a specific range of MBP (MBPCVD≥97mmHg, and MBP Diabetes<94mmHg), MBP was positively associated with the risk of CVD and diabetes mortality. Several studies have recently shown that ePWV can predict mortality risk in the general population and patients with CVD or with an increased risk of CVD (7, 8, 19–21).ePWV is also suggested to be associated with subsequent risk of mortality and cardiovascular morbidity, independent of systemic coronary risk assessment and the Framingham risk score. Hefferman et al. analyzed a cohort of NHANES from 1999 to 2006 and found that in the general population with no CVD, with an increase in ePWV at 1 m/s, the risk of all-cause and CVD mortality increased by $50\%$ and $47\%$, respectively [8].In addition, Laugesen et al. followed 25,066 patients with stable angina for 8.5 years and found a $13\%$ increase in the risk of all-cause mortality with a1 m/s increase in ePWV levels [20]. In a multicenter study of 107,599 healthy individuals, Vishram-Nielsen et al. observed a $13\%$ increase in the risk of all-cause mortality for every 1 m/s increase in ePWV levels but no significant association with the risk of CVD mortality [21]. They also showed a considerable variation in the additive prognostic information for high and low ePWV between European countries, suggesting a strong cohort dependence in the association between ePWV and mortality. Current studies have focused on the assessment of ePWV with all-cause and CVD mortality, while other cause-specific mortality has not been well studied. In the present study, we found a $55\%$ increase in all-cause mortality for every 1 m/s increase in ePWV in the US diabetic population. In addition, the risk of mortality increased from $53\%$ to $102\%$ with a 1m/s increase in ePWV for most cause-specific mortality, except for accident and renal disease. Compared with previous studies, ePWV was associated not only with a higher risk of all-cause mortality but also with a higher risk of most cause-specific mortality, suggesting that “cohort-generality” and “cohort-dependence” of ePWV on mortality risk may objectively present. Therefore, there is a need to evaluate this in a larger number of models and populations. These results also suggest that atherosclerosis is prevalent throughout the pathophysiology of death and drives the onset of death. Thus, ePWV may serve as a proxy for vascular age, reflecting the aging of the whole organism and providing additional risk assessment for specific mortality in a way that traditional risk factors cannot. Advanced age is an essential factor in natural human aging. Arterial stiffness progresses with aging, which directly affects both blood pressure and pulse pressure. The ePWV is calculated based on age and blood pressure levels. Therefore, in general, the older the person, the higher the ePWV value. In the multivariate Cox regression model, we adjusted multiple factors: age and blood pressure, traditional risk factors (such as BMI, blood lipid levels), poor lifestyle habits, history of chronic disease and medication use, and other potential risk factors, with consistent results before and after multiple imputation. Moreover, stratified analyses provided further evidence of the robustness of these findings. Interestingly, in our analysis of MBP, we observed no significant association for MBP with all-cause mortality and most cause-specific mortality, except for CVD and diabetes mortality. These findings provide further evidence that the association of ePWV with mortality independent of blood pressure levels captures additional risks of mortality, while solidly low levels of MBP also confer additional benefits. Several factors can trigger the development of arterial stiffness, including endothelial dysfunction, reduced nitric oxide bioavailability, inflammation, oxidative stress, and hormone deficiency (e.g., estrogen) (22–25). In addition, metabolic abnormalities, poor lifestyle and diet, age, and genetic factors can lead to structural remodeling of the blood vessels (26–28) resulting in arterial stiffness. Arterial stiffness is strongly associated with cause-specific mortality. Several mechanisms may be involved. Changes in atherosclerosis include thickening, fibrosis, fragmentation, and loss of elastin fibers, which can lead to structural changes in the arterial wall and stiffening [29]; on the other hand, arterial stiffness can make the intima more susceptible to damage and atherosclerosis [30], leading to cardiovascular and cerebrovascular events. In addition, DM affects endothelial function [31], which increases the risk of arterial stiffness, and arterial stiffness also affects the function of the peripheral circulation, increasing the incidence of diabetic complications. Arterial stiffness is positively correlated with the severity of airway obstruction [32, 33].Hence, the more severe the arterial stiffness, the worse is the lung function, increasing the incidence or even worsening respiratory diseases. On the other hand, respiratory diseases are often associated with systemic inflammation, sympathetic activation, and chronic hypoxic states, which further contribute to arterial stiffness [24].The link between arterial stiffness and cancer may be due to many co-existing risk factors, such as smoking and inflammation. Meanwhile, the use of anti-cancer drugs can in turn lead to arterial stiffness [34].Besides, arterial stiffness and the development of Alzheimer’s disease may be associated with small vessel disease, stroke, and brain atrophy [35]. In conclusion, the relationship between arterial stiffness and mortality from these specific causes is usually bidirectional, with different risk factors interacting. ## Perspectives Our findings indicated that ePWV was positively and nonlinearly associated with the risk of all-cause mortality, whereas MBP was not significantly associated with the risk of all-cause mortality. Although the level of ePWV is highly dependent on blood pressure and age, ePWV can predict the risk of mortality in DM patients independently of these two parameters. The results of this study highlight the added benefit of steadily lowering blood pressure in people with DM, rather than lowering blood pressure per se. Furthermore, the measurement of arterial stiffness has a positive impact on the assessment of mortality risk in patients with DM. Recently, it has been shown that there is a strong association between ePWV and CfPWV ($r = 0.7$) [36]. Similarly, a recent study by Heffen et al. showed that ePWV is associated with established indicators of vascular ageing such as carotid thickness, carotid stiffness and augmentation dilatation index [37]. Therefore, ePWV may be a useful tool in the assessment of vascular aging. In conditions where cfPWV cannot be measured, ePWV could be considered as an assessment of arterial stiffness for mortality risk stratification. Currently, there is no reasonable quantitative method to distinguish between normal and abnormal ePWV levels. Future studies could consider grouping by age and blood pressure level to quantify the predictive value of ePWV as a parameter for mortality risk. ## Strength and limitation As far as we know, this is the first study to examine ePWV and the risk of all-cause and cause-specific mortality in a cohort with DM, complementing the current field of study. Additionally, this study is a large prospective cohort design with a long follow-up period and a nationally representative sample, which helps to generalize our findings. Inevitably, this study has a few limitations. First, due to the design of this observational study, causal effects, and exclusion of the impact of residual confounders are not well obtained. Second, the cohort observed here is a diabetic population from the US and does not distinguish between diabetes subtypes. The results cannot be generalized to other specific populations. Third, the participants’ diagnosis of chronic disease history was primarily self-reported and may have been subjectively potentially biased. Fourth, due to the limitations of the current data, we are not able to make a definitive classification of glucose-lowering drugs. Although adjustments have been made for glucose-lowering drugs, the effect of differences in the type of glucose-lowering drug on the results remains unclear. ## Conclusion In the diabetic population, ePWV is independently associated with all-cause and most cause-specific mortality risks. ePWV may be a useful tool for assessing mortality risk. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. ## Ethics statement The studies involving human participants were reviewed and approved by National Health and Nutrition Examination Survey. The patients/participants provided their written informed consent to participate in this study. ## Author contributions CL and SC designed this topic. CL and HP drafted, analyzed, and interpreted this study. CL, HP, FK, SY, QS, DL and SC critically reviewed the study. All authors read and approved the submitted manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1145914/full#supplementary-material ## References 1. Poznyak A, Grechko AV, Poggio P, Myasoedova VA, Alfieri V, Orekhov AN. 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--- title: Evaluation of knowledge of risk factors and warning signs of stroke – An observational study among future health care professionals authors: - Wajid Syed - Omaimah A. Qadhi - Amal Barasheed - Ebtesam AlZahrani - Mahmood Basil A. Al-Rawi journal: Frontiers in Public Health year: 2023 pmcid: PMC10031120 doi: 10.3389/fpubh.2023.1131110 license: CC BY 4.0 --- # Evaluation of knowledge of risk factors and warning signs of stroke – An observational study among future health care professionals ## Abstract ### Background and objective The role of healthcare professionals in society is unique since they are providers of health information and medication counseling to patients. Hence, this study aimed to evaluate Knowledge of Risk Factors and Warning Signs of Stroke among undergraduate health care Students (UHCS) at King Saud University (KSU), Riyadh, Saudi Arabia. ### Methodology An online cross-sectional study was conducted among UHCS at KSU, Riyadh, Saudi Arabia from September to November 2022, using self-administered 34-item questionnaires divided into five sections to assess participants' knowledge of stroke risk factors, warning signs, and management and source of information about the stroke. The Statistical Package for the Social Sciences version 26 was used to analyze the data (SPSS). ### Results Of the 300 questionnaires distributed, 205 students completed the questionnaires, giving a response rate of $68.3\%$. Of whom 63 ($30.7\%$) were pharmacy, 81 ($39.5\%$) were nursing and 61 ($29.8\%$) were emergency medical services (EMS) Students. One hundred and eighty-two ($88.8\%$) of the students agreed that stroke affects bodily movement. With regards to risk factors, students identified high blood pressure 182 ($88.8\%$), followed by heart disease 175 ($85.4\%$), advanced age 164 ($80\%$), previous Stroke history 158($77.1\%$), and lack of physical activity 156 ($76.1\%$). Difficulty in speaking or slurred speech 164 ($80\%$), dizziness, and loss of balance 163 ($79.5\%$) were identified as the warning signs of stroke. In this study, 41.3 % of the pharmacy students reported a good level of knowledge than nursing and EMS students. However, $32.2\%$ ($$n = 66$$) of the healthcare undergraduates reported good knowledge. The knowledge score was significantly associated with the year of study, and educational degree ($$p \leq 0.0001$$). Furthermore, there were no differences between parents working in healthcare settings ($$p \leq 0.99$$). ### Conclusion In conclusion, the knowledge of stroke among healthcare students at King Saud University varied. The reported knowledge gap mostly relates to stroke risk factors and warning signs. Therefore, increasing public awareness of potential risk factors and stroke warning signs needs to receive more attention. ## Introduction A stroke is often described as a brain attack and a cerebral accident [1]. It's a medical emergency that occurs when the blood supply to the brain is interrupted. Brain cells start to degenerate in minutes [1]. Stroke is a chronic disease that affects people of all races and all generations [1, 2]. It is currently regarded as a worldwide health problem that causes functional impairment and mortality. Stroke prevalence has risen recently on a regional and international level, becoming a major public health concern that is anticipated to keep getting worse [1, 2]. The WHO estimates that $70\%$ of strokes, and $87\%$ of stroke-related deaths and disability-adjusted life years, occur in low- and middle-income countries [2]. Strokes were challenging to recover from strokes can be severely disabling. It is evidenced that the incidence of stroke-related complications increases treatment costs, repeated visits to clinics, disability, and early mortality [3, 4]. Stroke has become a significant and growing problem mostly due to unhealthy food habits, lack of physical activity, uncontrolled urbanization, and sedentary western lifestyles all of which contribute to multiple comorbidities [2, 3]. In Saudi Arabia, studies indicated that the number of stroke-related fatalities is on the rise, with an estimated number of Saudis dying each year from stroke [3, 4]. According to the WHO, stroke is the second leading cause of stroke-related impairments globally [3, 4]. In recent years the prevalence of stroke has been increasing and emerging as a major health problem, and it is estimated that the mortality rate resulting from stroke would be doubled by 2030, in Saudi Arabia [5, 6]. These numbers indicate that stroke will have a great economic burden in Saudi Arabia in the future. Earlier literature in Saudi Arabia revealed that hypertension and smoking age were the most common risk factors for stroke [5, 6]. Besides its complications, the prevalence of stroke is proliferating in both developed and developing countries worldwide [7]. It is evidenced that the incidence of stroke can occur in people over the age of 65 and they can occur in much younger ages (7–10). Healthcare students must be aware of the clinical presentation of various diseases since this information may be useful to them when they begin practicing after graduation. It was evidenced that healthcare undergraduates reported variation in their knowledge about stroke (11–13). For example, earlier findings revealed that nursing students have good knowledge of some aspects of warning signs and risk factors for stroke [11]. Similarly, another recent study among university students reported an incomplete understanding of the risk factors of stroke [12]. On the other hand, a previous study among Saudi medical students in Saudi Arabia revealed sub-optimal knowledge of ischemic stroke [13]. Although it is generally known that today's undergraduates would become tomorrow's professionals, a thorough awareness of the clinical facts related to diseases will help them in their work and help them to give their patients the best care possible. ( 14–19). Additionally, awareness of stroke would have a significant impact on morbidity and mortality rates, as well as contribute to the promotion of healthy habits. To increase students' knowledge, attitude, and practice (KAP) regarding stroke, a more extensive education program is required. Earlier studies have examined students' understanding of and awareness of stroke up to this point (11–13). To the best of our knowledge, there is a dearth of literature about clinical presentations and awareness of stroke among the UHCS in Riyadh Saudi Arabia. Hence, such a study was required and would help in future research. This study aimed to evaluate Knowledge of Risk Factors and Warning Signs of Stroke among UHCS in KSU, Riyadh, Saudi Arabia. ## Study design and settings We conducted a cross-sectional paper-based survey study among male students in healthcare colleges at KSU, including the College of Pharmacy, Nursing, and Emergency medical services (EMS) between July 2022 and October 2022, over four months. The male undergraduates aged >18 years and older, who were willing to complete the questionnaires and currently enrolled in the courses, and undergraduates with regular visits to college were included. Before accessing the survey, a disclosure statement followed by consent and agreement to use filled-out information for publication purposes was highlighted. We excluded students from other disciplines. Furthermore, the study was approved by the ethics committee of the College of Medicine at King Saud University. Before data collection, informed consent was obtained from the participants. Respondent's anonymity and confidentiality were ensured throughout the study. ## Sample size estimation There were ~350 residential students currently enrolled in Pharmacy, Nursing, and emergency medical services in the third and fourth years of courses at the KSU campus. Similar to the previous studies we calculated the required sample size using an online calculator (20–23) (http://www.raosoft.com/samplesize.html) with a $95\%$ CI and a pre-determined margin of error of $5\%$. Because we were unaware of the potential results for each question, we assumed that the response distribution for each question would equal $50\%$ [22]. Although the sample size was projected to be 184, we opted to poll at least 300 students to assure greater reliability. ## Questionnaire design In this study, we developed a questionnaire based on previous research about the knowledge of stroke risk factors and warning signs among undergrads at health colleges (11–13). The questionnaire consisted of 34 questions divided into five categories. In the first section, there were a total of six questions about the student's background, including the type of health, college, year of study, and knowledge of stroke (3 items). The second and third sections include knowledge of risk factors and warning signs of strokes. All these questionnaires were graded on a three-point scale (Yes/No/I don't know), and the fourth section discusses the management of stroke with a total of 5-items, assessed on a binary scale (Yes/No), the last section of the study questionnaires ask participants about the sources of information for stroke on a multiple choice. With the assistance of two prominent professors, the questionnaires underwent accuracy and content checks after initial compilation (one from the college of pharmacy and one professor from the college of nursing). An anonymous sample of students ($$n = 30$$) was surveyed for a pilot study. Pilot study results were not included in the final analysis. The reliability of the questionnaires was calculated by assessing the Cronbach's Alpha value (0.75) of the questionnaires, indicating it was reliable to carry out the study. In the survey, nursing, EMS, and pharmacy students who were regular students at the colleges were included. During lecture periods, a researcher who was designated to collect data visited the students in their classrooms. A brief presentation was given to explain the purpose of the study and to assure students that their responses would be kept confidential. The students provided written informed consent. The questionnaire was given to participants with sufficient time to complete it. Data was collected using convenience sampling. Students who did not complete more than half of the study questionnaires were considered to have incomplete responses and were therefore excluded from the study, whereas students who did not complete 2 or 3 items in the survey were considered to have a treatable response and were thus included in the study. Non-respondents were students who did not return their questionnaires. The stroke knowledge score was computed by assigning a score of ‘1' for the correct answer, and a score of ‘0' for the wrong answer, likewise the total knowledge score was designed by computing the total knowledge items, which was further divided into good knowledge scores (who score of >$50\%$) while poor knowledge score (a score < $50\%$) of the total score. ## Statistical analysis An evaluation of the data was conducted using the Statistical Package for Social Sciences (SPSS) version 26.0 software. Descriptive analysis such as frequencies (n) and percentages (%) were assessed. The knowledge score and standard deviations (SD) were calculated and presented in the form of tables and graphs. In addition, the associations between categorical variables were determined by performing chi-square and Fisher exact test. A p-value < 0.05 was considered statistically significant. ## Demographic information Of all participating subjects ($$n = 205$$), 63 ($45.3\%$) were pharmacy students, 81 ($54.6\%$) were nursing students and 61 ($29.8\%$) were EMS Students. Most of the respondents were between 18 and 22 years of age. Only 27.3 % of students' parents work in a healthcare setting. Hundred and eighty-two ($88.8\%$) of the students agreed that stroke affects bodily movement, and the majority $93.7\%$ of pharmacy $88.9\%$ of nursing, and 91.7 % of EMS students, reported that stroke happens when blood flow to the brain stops. While One-third ($38.5\%$) of the students reported, the window period of thrombolysis was between 0 and 4.5 h. The detailed responses were presented in Table 1. Table 1 \| Variables \| Frequency n (%) \| \| --- \| --- \| \| Educational degree \| Educational degree \| \| Pharmacy \| 63 (30.7) \| \| Nursing \| 81 (39.5) \| \| EMS \| 61 (29.8) \| \| Do any of your parents work in a healthcare setting \| Do any of your parents work in a healthcare setting \| \| Yes \| 56 (27.3) \| \| No \| 149 (72.7) \| \| Level of education \| Level of education \| \| Third year \| 108 (52.7) \| \| Fourth-year \| 97 (47.3) \| \| A stroke or brain attack happens when blood flow to your brain \| A stroke or brain attack happens when blood flow to your brain \| \| is stopped \| is stopped \| \| Yes \| 188 (91.7) \| \| No \| 05 (2.4) \| \| I don't know \| 12 (5.9) \| \| A stroke affect ability to move eat and other body function \| A stroke affect ability to move eat and other body function \| \| Yes \| 182 (88.8) \| \| No \| 08 (3.9) \| \| I don't know \| 15 (7.3) \| \| What is the window period of thrombolysis in hours? \| What is the window period of thrombolysis in hours? \| \| 0–4.5 h \| 79 (38.5) \| \| 4.5–6 h \| 78 (38.0) \| \| 12–24 h \| 33 (16.1) \| \| >24 h \| 15 (7.3) \| ## Knowledge of risk factors and warning signs among participants (n = 205) Of the participants, most of them ($88.8\%$) identified high blood pressure as one of the most common risk factors for stroke, followed by heart disease ($85.4\%$), advanced age ($80\%$), previous Stroke history ($77.1\%$) and lack of physical activity ($76.1\%$). Taking each college separately, high blood pressure was more prevalent among EMS, pharmacy, and nursing students (93.4, 90.5, and $84.5\%$ respectively). A large majority of all groups of pharmacy, nursing, and EMS (90.5, 84, and $82\%$) students identified heart disease as one of the risk factors for stroke. While 85.2 % of EMS, $84.1\%$ of pharmacy, and $72.8\%$ of nursing students reported older age as the risk factor for stroke. Interestingly, only a small percentage of all group students reported that gender is also a risk factor for stroke. More details can be found in Table 2. In this study, 41.3 % of the pharmacy students reported a good level of knowledge than nursing ($33.3\%$) and EMS ($21.3\%$) students. The detailed descriptions of the individual knowledge score among the healthcare undergraduates were given in Figure 1. Regarding the warning sign of stroke, the majority of the respondents 164 ($80\%$) identified difficulty in speaking and understanding or slurred speech, while 163 ($79.5\%$) identified dizziness and loss of balance. On the other hand, 159 ($77.6\%$) students, followed by chest pain or heart palpitations and difficulty in walking respectively, reported blurred vision. Focusing only on one of the highest warning signs of stroke, when students were compared on that basis, pharmacy students represented the highest proportion of $85.7\%$, while others were EMS and Nursing (80.3 and $75.3\%$) (Table 3). Additionally, when the same approach was used concerning the loss of balance, pharmacy, dental, and medical students' results were comparable, whereas nursing students had the lowest proportion of $72.8\%$. While $86.9\%$ of EMS students had a higher proportion of dizziness and a similar percentage was found in both groups pharmacy and nursing (~$76.5\%$). More details can be found in Table 4. In the case of a suspected case of stroke, the majority of the students ($70.2\%$) agreed that they would call an ambulance. On the other hand, $40.5\%$ of them agreed to take the patient to the hospital immediately when the patient is suffering from a stroke. Detailed information about the management of stroke among undergraduates was given in Table 4. With regards to the source of information about stroke hundred and fourteen 114 ($55.6\%$) reported physicians followed by lectures and presentations 74 ($36.1\%$) and textbooks 73 ($35.6\%$) respectively. More detailed information about the source of information for the stroke was given in Figure 2. Figure 2: Source of information. Table 5 shows the Association between the knowledge score of the participants concerning demographic characteristics of participants. We did not find any significant association between knowledge score and educational degree ($$p \leq 0.057$$). Similarly, the knowledge score of the undergraduates was not significantly associated with parents working in healthcare settings ($$p \leq 0.992$$). However, there was a significant association between knowledge score and year of study ($$p \leq 0.020$$) as shown in Table 5. Table 5 \| Participants characters \| Number of respondents \| Knowledge score \| Knowledge score.1 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Participants characters \| Number of respondents \| Good (N = 66; 32.2%) \| Poor (N = 139; 67.8%) \| P-value \| \| Educational degree \| Educational degree \| Educational degree \| Educational degree \| 0.049 \| \| Pharmacy \| Count \| 26 \| 37 \| \| \| Pharmacy \| % Within Educational degree \| 41.3% \| 58.7% \| \| \| Pharmacy \| % Within knowledge levels \| 39.4% \| 26.6% \| \| \| Nursing \| Count \| 27 \| 54 \| \| \| Nursing \| % Within Educational degree \| 33.3% \| 66.7% \| \| \| Nursing \| % Within knowledge levels \| 40.9% \| 38.8% \| \| \| EMS \| Count \| 13 \| 48 \| \| \| EMS \| % Within Educational degree \| 21.3% \| 78.7% \| \| \| EMS \| % Within knowledge levels \| 19.7% \| 34.5% \| \| \| Do any of your parents work in a healthcare setting \| Do any of your parents work in a healthcare setting \| Do any of your parents work in a healthcare setting \| Do any of your parents work in a healthcare setting \| 0.992 \| \| Yes \| Count \| 18 \| 38 \| \| \| Yes \| % Within parent's work in healthcare settings \| 32.1% \| 67.9% \| \| \| Yes \| % Within knowledge levels \| 27.3% \| 27.3% \| \| \| No \| Count \| 48 \| 101 \| \| \| No \| % Within parent's work in healthcare settings \| 32.2% \| 67.8% \| \| \| No \| % Within knowledge levels \| 72.7% \| 72.7% \| \| \| Level of education \| Level of education \| Level of education \| Level of education \| 0.020 \| \| Third year \| Count \| 27 \| 81 \| \| \| Third year \| % within the Level of education \| 25.0% \| 75.0% \| \| \| Third year \| % within knowledge levels \| 40.9% \| 58.3% \| \| \| Fourth-year \| Count \| 39 \| 58 \| \| \| Fourth-year \| % within the Level of education \| 40.2% \| 59.8% \| \| \| Fourth-year \| % within knowledge levels \| 59.1% \| 41.7% \| \| ## Discussion This survey assessed the knowledge of Risk Factors and Warning Signs of Stroke among future healthcare professionals (pharmacy, nursing, and EMS) at KSU, Riyadh Saudi Arabia. The current findings reported $32.2\%$ of the UHCS from KSU found knowledge about the stroke, while the majority of them reported poor knowledge. On the other hand, data on the health care undergraduates about the clinical presentation of stroke is limited, however, some studies exist on this issue, but most of the studies were conducted in other populations [11, 12, 24, 25]. This study would add a significant contribution to enhancing the health care professionals, patients, and individuals' knowledge about stroke, thereby helping in some aspects of the management of the diseases in Saudi Arabia, and other countries and would serve as a reference for the much-needed upcoming studies. The research findings may also be used by healthcare and educational organizations to create effective training programs to increase the clinical presentation of stroke understanding by healthcare workers. The current findings were inconsistent with previous findings (11–13, 24). For example, the previous study by Kankaya and Yesilbalkan among Nigerian nursing undergraduates reported $53.2\%$ of the studied population were knowledgeable about risk factors, while $53.2\%$ of them were knowledgeable about warning signs ($53.8\%$) of stroke [11]. However, another similar study reported $62.6\%$ of the students were knowledgeable about various aspects of the stroke [24]. It is commonly known that practicing healthcare professionals would be found to have good knowledge, followed by student professionals or the public. Even though knowledge may vary from study to study and may be influenced by many factors including the study method, the nature of respondents, and demographics. A previous study by Alam et al. among university students of Dhaka evaluated the awareness about stroke and reported that $74.2\%$ of the students identified stroke as a brain disorder [12]. While in our findings $91.7\%$ of the UHCS identified stroke as a brain disorder. Similarly, in another recent study in the United States, $50.1\%$ of the students recognized stroke as a brain disorder [26]. With regards to the meaning or definition of stroke, 91.7 % of UHCS in the current study recognized correctly as stroke occurs when blood flow to the brain is stopped. On the other hand, a similar previous study reported $36.7\%$ of the medical students correctly identified that both thrombotic and hemorrhagic represent a stroke [13]. Healthcare students' awareness of such important disease knowledge during their graduation would help at their practice site, which could save the lives of individuals who suffered or admired with history of stroke. Additionally, this research revealed that there were discrepancies in students' understanding of certain aspects of stroke, demonstrating the need for additional educational initiatives to raise students' awareness of various chronic diseases and their pathophysiology. The current findings identified hypertension ($88.8\%$) as a risk factor for stroke, followed by heart disease ($84.5\%$), older age ($80\%$), and history of prior stroke ($77.1\%$). These results were similar to many previous studies conducted around the world [13, 24, 27]. For instance, a previous study by al-Malki et al. identified high blood pressure followed by high cholesterol and smoking as the risk actors of stroke [13]. In contrast, a study in southwestern Nigeria among undergraduate students concluded that hypertension ($82.6\%$), old age ($74.9\%$), hypercholesterolemia ($42.8\%$), diabetes ($35.9\%$) and smoking ($27\%$) were the commonly identified stroke risk factors [24]. Conversely, a recent study by Mirghani et al. [ 27] in Saudi Arabia reported that $90.4\%$ of female medical students and 88.8 % of male medical students identified hypertension as a risk factor for stroke [27]. Previous studies conducted among the public identified hypertension as the most common stroke risk factor in line with our findings [25]. Furthermore, the American heart association and WebMD reported that constant stress is also another potential risk factor for stroke. The stress causes hypertension, which may cause constant strain on the heart arteries. When blood vessels are overinflated, too much force damages the walls of the arteries and makes them weaker. High blood pressure makes both main types of stroke more likely. Diabetes, high cholesterol, obesity, and older age were the vital factors that can cause a stroke [28, 29]. Increased awareness about early signs of stroke can improve overall disease diagnosis and treatment, morbidity, and death rates. In our study, according to students' perception toward warning signs of stroke, commonly identified symptoms were difficulty in speaking/understanding or slurred speech, dizziness and loss of balance, blurred vision, chest pain or heart palpitations, and difficulty in walking respectively. These findings were consistent with similar studies conducted in Saudi Arabia and other countries [11, 27, 30]. Other reports revealed numbness or weakness, difficulty in understanding speech, trouble in speaking or seeing, and walking and headache are important signs and symptoms of stroke [31]. According to our findings, more than half of the students believed that physicians were the most knowledgeable healthcare professionals to provide the source of information about stroke. While a previous study showed that textbooks ($37.0\%$) and the internet ($18.5\%$) were the most commonly cited resources [11]. It is known that the best source of information about stroke and better interventions can only be provided by the physician. Lastly, the action to be taken in case of stroke is vital during emergency cases. However, in a study by Almalki et al. [ 13] study, more than two-thirds ($69.7\%$) of the students would call an ambulance and this was followed by driving to the nearest hospital ($51.8\%$) and telling the patient's family member ($47.8\%$). In addition, calling an ambulance ($95\%$) was also the prime action among the nursing students which was reported by Kanaya et al. [ 11]. Furthermore, a previous study conducted among university students reported (85.7 %) would take a patient to a hospital for any potential stroke, [12] while our study reported $70.2\%$ reported calling an ambulance would be the first appropriate action. In this current research, we emphasize the importance of further studies that can evaluate the perceptions of HealthCare students. This study provides a good platform for others to conduct research within the domains. In this study, the knowledge level of the stroke is significantly associated with the course, being pursued and the year of study. Studies examining the variation between knowledge of stroke and characteristics of UHCS are currently lacking. There have been some studies about the evaluation of knowledge of clinical presentations of stroke among prospective students (11–15), but those earlier studies did not look at the relationship between the knowledge score and the characteristics of undergraduates. The fact is that senior undergraduates consistently demonstrate a higher level of theoretical knowledge than juniors. Additionally, prior exposure to clinical knowledge during the graduation process (through a course, congress, seminar, etc.) may have affected this circumstance. However, the current study has some limitations. Firstly, the results were based on a self-completed questionnaire, Secondly, the results were derived from a single institute in Saudi Arabia, therefore, the findings of this study cannot be generalized to the whole Saudi Arabia, studnets population. Thirdly, the study did not involve junior students as it was conducted among senior healthcare students of the university, given the more accessible access to students found while spreading the questionnaire. Despite these limitations, our study lays more emphasis on increasing the awareness toward knowledge of risk factors and warning signs/symptoms of stroke and its complications to make them more competent in raising public health. ## Conclusion This study depicts that one-third of the undergraduate healthcare students were found to have good knowledge. The knowledge score was significantly higher among pharmacy undergraduates compared to nursing and EMS healthcare students. Furthermore, the knowledge was significantly associated with the year of study whereas there were no significant differences between educational degrees. Thus, health education programs might help the students to understand clinical presentations of stroke. Incorporating more advanced topics about stroke and various chronic diseases in clinical practice will undoubtedly enhance treatment outcomes, reduce adverse medication effects, and have a favorable impact on patient care in the future. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. ## Author contributions WS, OQ, AB, EA, and MBAA: conceptualization of research and editing and review of manuscript. WS: data collection. OQ, AB, EA, and MBAA: data analysis and drafting the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AB declared a shared affiliation with the authors to the handling editor at the time of review. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'Prioritization of public health financing, organization, and workforce transformation: a Delphi study in Canada' authors: - F. Antoine Dedewanou - Sara Allin - Ak’ingabe Guyon - Jasmine Pawa - Mehdi Ammi journal: BMC Public Health year: 2023 pmcid: PMC10031161 doi: 10.1186/s12889-023-15373-9 license: CC BY 4.0 --- # Prioritization of public health financing, organization, and workforce transformation: a Delphi study in Canada ## Abstract ### Background The increased scrutiny on public health brought upon by the ongoing COVID-19 pandemic provides a strong impetus for a renewal of public health systems. This paper seeks to understand priorities of public health decision-makers for reforms to public health financing, organization, interventions, and workforce. ### Methods We used an online 3-round real-time Delphi method of reaching consensus on priorities for public health systems reform. Participants were recruited among individuals holding senior roles in Canadian public health institutions, ministries of health and regional health authorities. In Round 1, participants were asked to rate 9 propositions related to public health financing, organization, workforce, and interventions. Participants were also asked to contribute up to three further ideas in relation to these topics in open-ended format. In Rounds 2 and 3, participants re-appraised their ratings in the view of the group’s ratings in the previous round. ### Results Eighty-six public health senior decision-makers from various public health organizations across Canada were invited to participate. Of these, $\frac{25}{86}$ completed Round 1 ($29\%$ response rate), $\frac{19}{25}$ completed Round 2 ($76\%$ retention rate) and $\frac{18}{19}$ completed Round 3 ($95\%$ retention rate). Consensus (defined as more than $70\%$ of importance rating) was achieved for 6 out of 9 propositions at the end of the third round. In only one case, the consensus was that the proposition was not important. Proposition rated consensually important relate to targeted public health budget, time frame for spending this budget, and the specialization of public health structures. Both interventions related and not related to the COVID-19 pandemic were judged important. Open-ended comments further highlighted priorities for renewal in public health governance and public health information management systems. ### Conclusion Consensus emerged rapidly among Canadian public health decision-makers on prioritizing public health budget and time frame for spending. Ensuring that public health services beyond COVID-19 and communicable disease are maintained and enhanced is also of central importance. Future research shall explore potential trade-offs between these priorities. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15373-9. ## Background The COVID-19 pandemic has brought significant attention on public health (PH) systems globally. PH systems can be broadly defined as the complex networks of governmental organizations, departments, agencies and associations which are planning, managing and delivering PH services [1, 2]. The COVID-19 crisis has accelerated already engaged critical reflections on PH systems for the 20th century on their infrastructure, scope, processes, outcomes and performance, with Canada being one country particularly active in these reflections since the SARS crisis [3]. Several provincial, territorial, and federal government actors are currently undertaking consultations and reflections to prepare the future of PH systems post-COVID-19 [2, 4–6]. These consultations are yet to produce their full effects in terms of reforms to the Canadian PH systems, as they compete with emerging areas of focus in public health (e.g., public health and climate action) or in healthcare (e.g., funding and surgery backlogs) [7, 8]. Public health systems are understandably complex [9]. In Canada, differing PH responsibilities lie at the federal, provincial/territorial, regional and municipal levels [10]. PH functions include population health assessment, health surveillance, disease and injury prevention, health promotion and health protection [11]. Daily PH interventions at a local or regional level include health needs assessments, direct actions targeted towards populations, indirect actions targeted towards third parties involved in public health response such as support, collaboration and advocacy, as well as planning and evaluation [12]. With pandemic inquiries currently examining PH systems across Canada, multiple priorities will be proposed to allow for concrete actions for renewal of PH systems. Health systems are particularly known to be slow and incremental to reform, before windows of opportunity for large-scale, substantial reforms open [13]. The COVID-19 pandemic provides this kind of window of opportunity for major reform to PH systems, with both opportunities and risks. Collecting data from PH senior decision-makers in this setting, and using a method designed to build consensus where possible and identifying consistent needs across jurisdictions, is thus a particularly timely endeavor. Given the underdeveloped field of PH systems and services research [14], and despite several policy reports on reforming PH systems, PH decision makers’ priorities on PH systems are seldom systematically investigated and documented. This study provides a first step in this direction and will help highlight priorities for both PH policies and intervention research. Our objectives are to understand PH decision makers’ priorities for PH systems reform across Canada, and ultimately generate evidence on PH decision-makers expectations for future PH systems’ reorganizations. Precisely, our study aims to identify if a consensus can emerge on priorities for public health financing, organization, interventions, and workforce across senior PH decision makers located in several jurisdictions and organizations across Canada. ## Research design We developed and administered a Delphi survey to collect data from senior PH decision makers. The Delphi approach is a method for organizing and sharing opinions among a panel of experts with the aim to identify areas of consensus [15–17]. Experts are asked to make judgments, usually via a rating of importance, on a list of ideas which are called propositions in the Delphi terminology. The Delphi approach is iterative, and the rating of importance is repeated by one expert on several occasions called rounds. Because the purpose is potential consensus building, the experts are presented with the ratings of the group between each round [18–20] and asked to reconsider their ratings in the next rounds based on this new information. We used a type of real-time Delphi in an online survey characterized by participants being able to participate and edit their responses anytime during a specific timeframe [21]. The first step of a Delphi is thus to develop a list of propositions. We started by conducting a literature review to identify key components of public health infrastructures [22–24] and we consulted with a working group of PH experts, including PH academic, PH practitioners and clinicians, and PH managers, to develop an initial list of propositions summarizing important characteristics of PH infrastructure in Canada. We then refined our list of propositions in three consultations with this working group of PH experts and ran a pilot test of the first round of the Delphi survey with 3 participants. We then aggregated our proposition lists from 16 to 9 propositions due to overlap in themes. The 9 propositions cover the domains of PH financing, organization, workforce, and interventions, all well-identified as key dimensions in the literature of PH systems [22–24]. These specific propositions were also consistent with recent PH systems reorganizations in various Canadian jurisdictions [1] and with the key messages that emerged in the recent Canadian Chief Public Health Officer’s report [2]. Additionally, we added an option for participants to provide open-ended comments on up to three priorities, to be analyzed separately. We included this option to provide nuance to the findings of the Delphi. The second step was to determine the number of Delphi rounds, a debated issue in the literature [20, 25]. On one hand, some recommend that the appropriate number of rounds should be determined once the panelists show a level of stability in their individual responses [19, 20, 25]. On the other hand, some scholars argue that practical constraints such as time, cost, and expert availability require a set number of Delphi rounds. Since three rounds are generally considered sufficient to reach group consensus [18, 20, 26], we elected for a fixed three-round data collection given the expected constraint on PH experts’ availability. The Delphi process was conducted in an online format, as all real-time Delphi, and owing to the COVID-19 pandemic context. Note that online Delphi performs similarly to in person [18, 21, 27, 28]. ## Survey instruments Participants were prompted to identify their preferences for changes to public health financing and structures in order to determine emerging priority areas and help inform the future changes for public health systems in Canada. They were asked to rate the nine propositions on a 5-point Likert scale (“not at all important”, “slightly important”, “moderately important”, “very important”, “extremely important”). Propositions in the financing domain include provincial or territorial PH budget, sources of PH financing, and time frame for spending. For the domain of PH organization, propositions concern centralization or decentralization of public health system, integration of public health with other health sectors, and the creation of public health structures with specialized public health functions. Further, we assess participants’ opinions regarding the disciplinary skill-mix of PH human resources. Our last domain covers PH interventions related to the COVID-19 pandemic and those which are not related to the COVID-19 pandemic. We provide the full description of our questionnaire in the Supplementary Materials. In Round 2, we show individual and aggregate responses to each participant and invite them to re-rate the nine propositions with the possibility to re-consider their initial responses. They receive the same instructions during the Round 3. The Delphi survey was developed online using Qualtrics. The survey was initially developed in English and then translated in French by our bilingual team members: one researcher translated the survey to French, and another translated it back to English to ensure equivalence. Only minor adjustments were made to the initial French translation. An important feature of our survey design is that, after round 1, each respondent receives an individualized survey. Indeed, in round 2, each respondent receives the results summarizing both their own response and the overall responses on the importance of each of the 9 propositions at the end of the first round. This is with the view to minimize survey recall bias and inform the respondent of the collective priorities. With this new information, the respondents are invited to re-rate the 9 propositions with the possibility to re-consider their initial responses. The same process applies in Round 3, displaying the individual and group responses of the second round. ## Participants’ recruitment The literature on the number of participants required in a Delphi exercise suggests many acceptable ranges, from 5 to 50 participants [29–31]. The lower end of these ranges have been used in empirical studies [32–34] and most recently, Vogel et al. [ 35] argued that 12 respondents is a sufficient minimum. In line with Belton et al. [ 18] and Rowe and Wright [20], we invited 86 PH decision makers to participate in the Delphi survey. We aimed for 15 to 20 respondents in the last round and our number of first invitations account for potentially high initial non-response and for some attrition between rounds. Our target population was of individuals in senior position in a Canadian public health organization (e.g., provincial public health organization, local public health unit) or a health organization with a public health mandate (e.g., Ministry of Health, regional health authority). The rationale for focusing on senior decision-makers was for them to be able to directly influence or act on priorities for PH infrastructure and to address gaps in the literature in understanding this perspective. This rationale led us to exclude academics from our target population. Senior roles include medical officers of health, senior manager, or senior policy advisors in these organizations. We aimed for a national coverage and invited respondents from all provinces and territories. We invited respondents working in provincial/territorial and regional authorities to account for the multi-level nature of PH systems. We built a directory from publicly available information, supplemented by input from knowledge users for those harder to locate. We adopted a non-probability purposive sample strategy to ensure invited participants met our inclusion criteria. Respondents were invited to participate by email sent to their professional address in April 2021. To increase participation, we asked our knowledge users to champion the Delphi in their province/territory by sending a pre-invitation email highlighting our invitation was coming. The data collection occurred from April to June 2021, only those who received the individual invitation could access their personalized survey, and only respondents who replied to the preceding round received invitation for the next one. ## Reaching consensus There is no agreed position in Delphi literature concerning the way that researchers must define and operationalize consensus among the participants [19, 26, 36–38]. Some recommend using both stability and consensus on a round-by-round basis and continuing until acceptable levels of both are achieved [18, 19]. Consensus criteria can include the same or similar opinion being reported by $70\%$ [35, 36, 39, 40], $75\%$ [29, 41] or $80\%$ of experts [31, 42]. In this paper, we define consensus as a minimum of $70\%$ of participants’ agreement on the importance of the proposition. We selected this lower bound given the heterogenous participants coming from various organizations and geographic levels with goals potentially not aligned. Participants are deemed to agree on a proposition’s importance if they are at least $70\%$ to judge it as very/extremely important. They agree on a proposition being not important if they are at least $70\%$ to rate it as not/slightly/moderately important. ## Data analysis We perform descriptive statistics to describe respondents’ characteristics and group responses to each proposition in all three Delphi rounds. Analyses are conducted using STATA SE 16. Open-ended comments are synthesized using thematic analysis [43]. Themes were organized through discussion, summary tables, and mapping. ## Participants description Of the 86 participants invited to take part the Delphi study, 25 experts completed Round 1 ($29\%$ response rate), 19 of 25 completed Round 2 ($76\%$ retention rate) and 18 of 19 completed Round 3 ($95\%$ retention rate). The dropout rate between each round is thus consistent with the health related Delphi literature [35, 39]. Table 1 presents the demographic characteristics of participants in each round. At the beginning of the survey $20\%$ of participants received the French version of the questionnaire while $80\%$ participated in English. Respondents of all the parts of Canada were represented, roughly proportional to the population and with slightly fewer responses from the territories, as expected. Most participants are non-MOH executive, who responded consistently across rounds, while the share of CMOH and MOH decreased from the first to the last round. Respondents are slightly more represented in Ministries of Health, and relatively evenly spread between Public Health Agencies and Regional Health Authorities. Table 1Characteristics of the participantsRound 1Round 2Round 3n = 25n = 19n = 18 Province/Territory British Columbia (BC)$32\%$$31.6\%$$27.8\%$Atlantic (NB, NL, NS, PEI)$16\%$$10.5\%$$11.1\%$Ontario (ON)$16\%$$21\%$$22.2\%$Prairies (AB, MB, SK)$12\%$$10.5\%$$11.1\%$Quebec (QC)$16\%$$21\%$$22.2\%$Territories (NT, NU, YK)$8\%$$5.3\%$$5.5\%$ Survey language English$80\%$$73.7\%$$72.2\%$French$20\%$$26.3\%$$27.8\%$ Current Role CMOH, MOH and equivalents$44\%$$47.4\%$$44.5\%$Executive non-MOH$56\%$$52.6\%$$55.5\%$ Organization Ministry of Health$40\%$$47.4\%$$44.4\%$Public Health Organization$36\%$$31.6\%$$27.8\%$Local or Regional Health Authority$24\%$$21\%$$27.8\%$Note: AB: Alberta; MB: Manitoba; NB: New Brunswick; NL: Newfoundland-Labrador; NS: Nova Scotia; NT: Northwest Territories; NU: Nunavut; PEI: Prince Edward Island; SK: Saskatchewan; YK: Yukon ## Delphi main findings Table 2 presents a summary of the importance ratings for all Delphi propositions across the three rounds of data collection. Recall ratings as “very important” or “extremely important” are aggregated in the “important” category, while the other levels of the Likert scale fall in the “not important” aggregated category, and we provide more details of the full distribution of the ratings for Round 3 in Fig. 1. The numbers in Table 2 reports the percentage of respondents who judge the proposition as important or not important, and figures in bold indicate that the consensus has been reached for the proposition. The number of propositions where consensus was achieved increased for several propositions from Round 1 to Round 3. In Round 1, consensus was achieved for 2 of the 9 propositions. In Round 2, consensus was achieved for 4 of the 9 propositions and this rose to 6 out of 9 in Round 3. By Round 3, consensus was achieved on the importance of: public health budget ($89\%$ judge it important in round 3); time frame for spending ($72\%$); public health structures with specialization of public health functions ($83\%$); public health interventions related to the COVID-19 pandemic ($78\%$); and PH interventions not related to the COVID-19 pandemic (e.g., environmental health protection, prevention and control of other infectious diseases) ($89\%$). The proposition pertaining to the source of PH financing is the only one where the consensus led to a rating of non-importance ($78\%$ of respondents judge it unimportant). Turning to Fig. 1, it appears that among propositions where consensus was reached, majority judgment of extreme importance was only attained for PH budgets and PH interventions not related to COVID-19. The other consensual propositions (time frame for spending, PH specialization, COVID-19 PH interventions) were rated as very important. Table 2Responses to the propositions across the three roundsRound 1 ($$n = 25$$)Round 2 ($$n = 19$$)Round 3 ($$n = 18$$)Important %Not important %Important %Not important %Important %Not important %1. Public health budget in your province or territory (e.g., increase, decrease or stability) 88 12 94.7 5.3 88.9 11.12. Source of public health financing (e.g., mostly federal or mostly provincial or mostly municipal)445647.352.722.2 77.8 3. Time frame for spending (e.g., restricted to a fiscal year or possible to use beyond a fiscal year)6436 84.2 15.8 72.2 27.84. Centralization or decentralization (e.g., less or more public health structures in your province or territory)683252.647.450505. Integration of public health with other health sectors (e.g., with primary care)524857.842.266.733.36. Creation of public health structures with more specialized public health functions (e.g., some focus on surveillance, other on promotion)604068.431.6 83.3 16.77. Disciplinary skill-mix of public health human resources (e.g., concentration of the workforce mostly in some disciplines or workforce trained in more disciplines)485242.157.944.455.68. Public health interventions related to the COVID-19 pandemic (e.g., surveillance, case and contact management, infection prevention and control, risk communication)6436 73.7 26.3 77.8 22.29. Public health interventions not related to the COVID-19 pandemic (e.g., environmental health protection, prevention and control of other infectious diseases) 88 12 94.7 5.3 88.8 11.2Bold % denotes that $70\%$ consensus was achieved Fig. 1Participants’ responses during the third round of the Delphi survey ## Synthesizing open-ended comments provided Free-text suggestions were solicited during Round 1 of the Delphi process and most participants provided comments. Most of the comments touched on governance and decision-making, the importance of financing/resourcing and human resources. Less common comments related to data and information systems. In sum, in addition to the domains covered in the survey (i.e., financing and workforce), two other themes emerged (i.e., governance and information system).“Ongoing committed long-term funding rather than responsive funding to address each emerging public health crisis. This allows long-term planning and optimization as well as better prevention.” Regarding PH financing, survey respondents noted the need to increase (and protect) the PH budget over the long-term, to increase funding for and consolidation of public health laboratories, to allow for flexibility within PH budgets in terms of the different PH functions, and to consider equity in the funding formulas to reflect differences in local populations’ health and vulnerabilities. Key priorities noted within the PH workforce domain included the need to expand and “upskill” the PH workforce and investing in training and increased compensation. “The expert public health workforce has been demoralized during the pandemic response, decimating available expertise. Investment in training, improved compensation, and most importantly basic respect for front line public health expertise is urgently required.” Many of the open-text comments related to public health governance. Some of the comments touched on aspects of governance and decision making around financing and workforce, such as the need to develop a strategy and specific objectives for PH to use to then clarify and justify the budgets and resources, as well as the need to develop a workforce strategy. Multiple individuals commented on the need for increased clarity of public health objectives and the inherent challenges given the nature of the work. Some reflected on the need to prioritize more non-communicable disease work (over communicable disease) and continue to work towards prioritizing prevention/promotion over treatment, an aspect covered in the survey. Other aspects of governance noted in the comments included maintaining or increasing the independence of PH actors, with some noting the importance of maintaining independence from the acute care system, as well as supporting intersectoral partnerships and including multiple stakeholders (e.g., from social sectors) in decision making. Some indicated a lack of respect for PH expertise, particularly that of local/regional PH leadership. “Governance of public health and its independence from the administrative functions of government. Accountability of public health with respect to funding, organization of services and personnel.” A last and smaller group of comments pertained to the importance of data management and information systems. They reflected on epidemiologically driven nature of public health and the need for improved availability of data for PH to continue advancing its work and the science. ## Key findings Using a Delphi methodology, we investigated PH senior level decision-makers priorities for the future of PH infrastructure in Canada. The development of the Delphi survey in close consultation with PH knowledge users brought us to focus on PH financing, organization, workforce, and interventions. The relevance of this focus was recently corroborated by priority research areas identified by the CIHR Institute of Population & Public Health [44]. Consensus was reached on six out of nine propositions. One of the most important findings is the immediate consensus in the first round of the Delphi, and agreement on extreme importance among PH decision-makers on the importance of PH budget and PH non-COVID-19 interventions. Several have observed cuts in PH budgets [45, 46], even if there are variations across Canada [47]. The need for sufficient financial means to support their work is thus a top priority, and PH decision-makers are also mindful that the COVID-19 pandemic shall not crowd-out all the other necessary PH interventions. With regards to non-COVID public health interventions needed to be prioritized, multiple comments specifically pointed at the importance of upstream and health promotion interventions. Indeed, public health institutions have faced a recurring challenge in simultaneously addressing population health needs and public health emergencies [6, 48]. Nevertheless, PH decision-makers also agree that PH interventions designed to tackle the COVID-19 pandemic are important, perhaps recognizing that the implications of the pandemic will be far reaching. Among other propositions reaching consensus, the time to spend the PH budget is important but the source of financing is not. While the source of financing could reflect a division of power in the way the money is spent, most jurisdictions receive funding mostly from provincial and federal governments. The main exception is the province of Ontario, where the municipal level provides a nonnegligible share of the funding [49]. Hence, the little variation they experience in the source of financing may explain why PH decision-makers from across the country do not see this as a priority. On the other hand, the time frame to spend the PH budget may be related to the long-term planning and implementation horizon of many PH interventions. Creating PH structures with more specialized PH functions also reached consensus. Most provinces and territories do not have regional PH expertise centers (e.g., BC Centre for Disease Control, Public Health Ontario, Institut National de la Santé Publique du Québec) [49–52], and the consensus may reflect a desire to protect or expand those centers. Where consensus was not reached, it was for the centralization/decentralization of PH, PH integration and the skill-mix of the PH workforce. Canadian health systems, including PH, have increasingly recentralized from the mid-2000s, but there is no clear evidence on the right level of centralization/decentralization [53]. Integration of PH with other healthcare sectors, particularly primary care, is recognized as a way to improve population health [54]. However, only some provinces like Quebec have been moving in this direction [55]. Moreover, while there are clearly established core competencies for PH in Canada [56], little is known on the size and composition of the Canadian PH workforce [49, 51]. The dearth of evidence on these matters may help explain why consensus was not reached, even though some comments in the open text pertained to skill-mix. Overall, free-text comments provided by participants in the first round of the Delphi process provided useful and complementary insights, particularly on the critical importance of public health governance. PH governance can be understood as “the ways in which public, non-governmental, or private actors work together to support communities in preventing disease and achieving health, wellbeing, and health equity” [57]. Functions of PH governance include resource stewardship [58], which emerged as an important theme in the comments. ## Strengths and limitations Our study has several strengths. We focused on PH senior decision makers rarely involved in anonymous, consensus-building processes. This approach should limit issues that may occur in group consultations where authority, personality or reputation may affect priority-setting. Obtaining the perspective of individuals likely to be making or influencing policy decisions is an important contribution. We further achieved a good coverage of multiple level of responsibilities, from local to provincial. Furthermore, given its reasonable time-demand, the online Delphi may have been the most realistic strategy to survey PH decision-makers during a pandemic. We used a bilingual survey, ensuring representation of linguistic minority in Canada, and covered the country coast to coast to coast. Our study also has limitations. We obtained an uneven initial response rate across jurisdictions, which may be explained by differential timing of COVID waves across the country. However, we achieved high retention rate among the respondents. We aimed for a short list of propositions to maximize the survey uptake, hence not all areas of PH infrastructure were addressed. Further, we did not change the propositions between each Delphi round. We aimed to mitigate this by offering the option for open-ended comments, which helped reveal the importance of public health governance as well as data management and information systems. Lastly, the formulation of our propositions did not allow us to know if changes related priorities identified by participants were already being implemented. Future research could delve into those priorities with more extensive qualitative methods to consolidate and refine concrete policy options. This could be done by expanding the investigation to larger PH community (e.g., academics, PH staff). It would also be useful to know if these priorities resulted in actual system changes. Additionally, a quantitative study could help clarify the potential trade-offs among priorities. ## Conclusion Public health systems renewal is now at the top of many policy agendas. Ensuring that their redesign is informed by public health communities will not only increase the readiness of the systems for future public health challenges, but they will also increase their acceptability by those in charge of transforming the public health systems. Despite the difficulty of obtaining participation from public health leaders in the middle of the COVID-19 pandemic, we attained the required number of responses for the Delphi methodology and achieved a geographic coverage of Canada. The top priorities of PH senior decision-makers are PH budget and ensuring non-COVID PH interventions are not forgotten because of the pandemic. Future research can help assess if and how PH decision makers are willing to accept trade-offs between these priorities. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. 1.Canadian Public Health Association. 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--- title: 'A prospective analysis of the long-term impact of the COVID-19 pandemic on well-being and health care among children with a chronic condition and their families: a study protocol of the KICK-COVID study' authors: - Petra Warschburger - Clemens Kamrath - Stefanie Lanzinger - Claudia Sengler - Susanna Wiegand - Julia M. Göldel - Susann Weihrauch-Blüher - Reinhard W. Holl - Kirsten Minden journal: BMC Pediatrics year: 2023 pmcid: PMC10031163 doi: 10.1186/s12887-023-03912-7 license: CC BY 4.0 --- # A prospective analysis of the long-term impact of the COVID-19 pandemic on well-being and health care among children with a chronic condition and their families: a study protocol of the KICK-COVID study ## Abstract ### Background There is consistent evidence that the COVID-19 pandemic is associated with an increased psychosocial burden on children and adolescents and their parents. Relatively little is known about its particular impact on high-risk groups with chronic physical health conditions (CCs). Therefore, the primary aim of the study is to analyze the multiple impacts on health care and psychosocial well-being on these children and adolescents and their parents. ### Methods We will implement a two-stage approach. In the first step, parents and their underage children from three German patient registries for diabetes, obesity, and rheumatic diseases, are invited to fill out short questionnaires including questions about corona-specific stressors, the health care situation, and psychosocial well-being. In the next step, a more comprehensive, in-depth online survey is carried out in a smaller subsample. ### Discussion The study will provide insights into the multiple longer-term stressors during the COVID-19 pandemic in families with a child with a CC. The simultaneous consideration of medical and psycho-social endpoints will help to gain a deeper understanding of the complex interactions affecting family functioning, psychological well-being, and health care delivery. ### Trial registration German Clinical Trials Register (DRKS), no. DRKS00027974. Registered on 27th of January 2022. ## Background In Germany, around $10\%$ of children and adolescents are affected by a chronic physical health condition, such as obesity, diabetes, or rheumatic disease [1–3]. Although children and adolescents seem to be less often and less severely affected by the coronavirus disease 2019 (COVID-19) compared to adults [4–9], children and adolescents with a chronic health condition (CC), such as diabetes, obesity, or inflammatory rheumatic disease, are assumed to be at higher risk of a more severe course of an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [6, 10, 11]. Type 1 diabetes, for instance, ranks among the strongest risk factors for severe illness and hospitalization when infected with the SARS-CoV-2 virus [10], depending on the metabolic control [12], which often deteriorates in adolescence [13, 14]. Also, obesity belongs to the strongest risk factors for hospitalization with the SARS-CoV-2 virus. Various influence factors constitute this risk, most of all chronic inflammation, impaired immune response, and underlying cardiopulmonary disease [15]. Evidence regarding rheumatic diseases or a consequential therapeutically induced immunosuppression as a risk factor for severe infection with the SARS-CoV-2 virus show conflicting results [16–19]. At the same time, altered daily structure and, therefore, altered health behavior can impair the underlying rheumatic disease or obesity [15, 20]. In addition, these changes result in a higher prevalence of several chronic conditions, such as obesity [21]. Also, for diabetes, an increased prevalence has been observed during the COVID-19 pandemic [22]. Also in times without a pandemic, children and youth with CCs as well as their parents are at increased risk of developing mental health problems, such as anxiety, depression, and impaired health-related quality of life [23–29]. During the COVID-19 pandemic, children with CCs and their families are confronted with unique challenges in their disease management routines. The federal states in Germany have implemented swift, wide-ranging public health emergency measures that have included a national lockdown with social restrictions (e.g., stay-at-home orders) and quarantines to reduce interpersonal contacts. On March 13, 2020, all federal states in Germany closed kindergartens and schools; nearly all colleges and universities followed. School closures substantially disrupt the lives of children and their families and may have consequences for child health [30–34] and parental well-being [35–39]. In addition, Kindergartens and schools provide an essential source of meals and nutrition, health care, including behavioural health supports, physical activity, social interaction, support for students with special education needs and disabilities, and other vital resources for healthy development [40]. It is important to note that childhood and adolescence are periods of life characterized by heightened sensitivity to social stimuli and the increased need for peer interaction. The physical distancing measures have radically reduced opportunities to engage in social contacts outside of the household. Consequently, social deprivation during this sensible developmental period might have caused far-reaching consequences [41]. In addition, parents may have faced economic insecurity, had to educate their children at home as a substitute for school attendance and had to deal with an uncertain outlook into the future. Working at home and living with preschool-aged children has particularly influenced the extent of mental distress during the pandemic [42, 43]. Therefore, the impact of lockdowns implemented in response to COVID-19 on mental health has raised concerns [44, 45]. There is increasing empirical evidence underpinning the negative effects of the pandemic and the associated containment measures on the psychosocial well-being of children [33, 34, 46] and their parents [38, 39] in the general population. However, so far only few studies focused on the psychosocial situation of children and adolescents with CCs [47, 48]. Given that preexisting mental and physical health problems are associated with higher levels of anxiety and depression during the COVID-19 pandemic [49], it can be assumed that the COVID-19 pandemic represents an additional risk that will be more pronounced among those children and adolescents and their families who are facing preexisting physical, mental or social vulnerabilities [36, 50]. With respect to regular medical care, which is essential for children and adolescents with CCs, there were major changes. During the lockdown, in most medical institutions across the country, routine consultations took place alternatively by telephone or video contact. Socially and educationally disadvantaged populations might face more problems using telehealth services [36]. It is well known that threat appraisals (rating of the subjective vulnerability and severity of a disease risk) affect the adoption of health-protective behaviors and health care use [51]. In childhood, research has shown that in addition to the risk perceptions of the children themselves, parental threat appraisals also influence their intention to engage in preventive or intervention efforts [52, 53]. Reduced access to health care can be detrimental to pediatric health, and children with special needs are potentially at higher risk of severe illness due to a lack of health care than their healthy peers. ## Objectives Based on a comprehensive understanding of the complex interplay between medical condition, health care use, and environmental and specific context factors in CC (see Fig. 1 [54];), the overarching aim of the so-called KICK-COVID study is to examine the longer-term effects of the COVID-19 pandemic on both medical as well as psychosocial outcomes in children and adolescents with a CC and their families. Fig. 1Conceptual model of the proposal considering the ICF structure (modified according to Cieza and Stucki [54]) Based on the biopsychosocial model, the specific study objectives are to examinethe impact of the COVID-19 pandemic on the care of children and adolescents with a CC by analyzing deficiencies, interruptions and unmet needs in care and treatment,the impact of the COVID-19 pandemic on body function and clinical outcomes (e.g., disease specific medical endpoints, physical and mental health), activities and participation of children and adolescents with different chronic diseases (diabetes, obesity, and rheumatic disease),the psychosocial resources and risk perceptions of children and their parents facing the Corona pandemic and their influence on child’s psychosocial adjustment, mental and physical health,the interplay of the physical and mental health of the children,the course of physical and mental health over time,the impact of a potential COVID-19 infection on these variables, anddisease-specific differences and similarities with respect to the impact of the COVID-19 pandemic. ## Study design The ongoing prospective observational study started recruitment in June 2021. A two-step approach (see Fig. 2) has been implemented: In the first step, additional COVID-19-specific questions have been added to the already existing surveys for the regular check-ups. Parents and adolescents provide data on their own psychosocial situation. In addition, parents assess as proxies the situation of their children under 12 years. Since this assessment is part of a comprehensive medical examination, only short economic instruments can be used in order to prevent exhaustive strain on the families. To get a deeper insight into the relevant psychological processes, a smaller voluntary subsample is asked to fill-in an extended online survey and forward it to their children (> 9 years) as well. After one-year, follow-up assessments will take place. Fig. 2Illustration of the study design and recruitment process ## Study participants Parents and their underage children (aged up to 18 years) who are already taking part in any of the three patient registries for diabetes (German Diabetes Prospective Follow-up Registry, DPV [55]), obesity (German Obesity Prospective Follow-up Registry, APV [56]), or rheumatic disease (National Pediatric Rheumatology Database, NPRD [57]) are eligible for inclusion. ## Recruitment Recruitment takes place during the regular check-ups within the participating clinical facilities. Clinicians ask their adolescent patients and their accompanying parents to fill in a two-page short questionnaire during their waiting time; for children younger than 12 years, the questionnaire is only completed by the accompanying parent. After the completed questionnaires are handed over to the treatment team in the healthcare facility, the next step is to ask whether the relatives would also be willing to take part in a further, more extensive survey. If they agree, the families receive a flyer with information and a barcode that leads directly to the online questionnaire. At the end of the questionnaire, consent is obtained from the parents to be contacted again after 1 year. In addition, the parents are asked for parental consent for the participation of the underage child (from about 9 years of age). If the parents agree, the children also receive an online questionnaire at the e-mail address provided and can then fill out the children’s questionnaire themselves. At the end of the respective questionnaires, parents and their children can download quizzes, an audio-guided relaxation training [58], small puzzles and colouring books, or take part in a computer game on risk-taking behavior as an incentive. In order to recruit a high number of eligible participants, it is planned to realize the whole recruitment process over a period of 1 year. The whole recruitment process is depicted in Fig. 3.Fig. 3Recruitment flow chart ## Study measures This survey uses several questionnaires and self-constructed items to fulfil the primary study objectives stated above. ## Basic survey An overview of the study measures applied in the basic surveys is given in Table 1.Table 1Overview of corona-specific study measures in the basic surveyStudy Measures: Basic QuestionnairesParentsaParentsbTeenagersEmotions and psychological distress during the COVID-19 pandemic COVID-19 pandemic-specific impact on parental well-being and everyday lifecxx COVID-19 pandemic-specific impact on well-being and everyday life of the afflicted childcxdxPsychosocial adjustment Health-related straincxxx Perceived stress of the afflicted childcxx Perceived loneliness of the afflicted childcxx Media usagex Well-Being Index WHO-5xdx General Anxiety Disorder-Scale GAD-7x Patient Health Questionnaire PHQ-9x School experiencecxDisease-specific healthcare use Corona-specific utilization of health services in the last 12 monthscxxaParents of afflicted children younger than 12 yearsbParents of afflicted children aged 12 years and oldercself-constructed questionnairedproxy-report ## Basic survey: Common measures for parents and children Several measures are administered for the data collection. While parents are asked to fill out the questionnaire for their children under the age of 12, they answer an additional separate questionnaire when their afflicted child is older than 12. The following assessments are identical for the parents of these two age groups. ## COVID-19 pandemic-specific impact on well-being and everyday life Based on experience from a previous study [59], a self-constructed 6-item scale is administered to assess how the COVID-19 pandemic has impacted the well-being of afflicted children and their families in different aspects of their every-day life. Parents are asked to rate the quality of several life domains in light of the COVID-19 pandemic: the care for the child at home, the treatment of the child’s condition, the occupation, the financial situation, and the partnership; with scales ranging from 0 “totally bad” to 10 “totally good”. Furthermore, participants are asked to indicate how stressed they feel due to the COVID-19 pandemic with a scale ranging from 0 “not stressed at all” to 10 “totally stressed”. ## Health-related strain Two self-constructed items [59] are used to assess health-related strain in the light of the COVID-19 pandemic. On two 11-point Likert scales, participants are asked to (i) state how dangerous they consider a COVID-19 infection in their afflicted child, ranging from 0 “totally harmless” to 10 “totally dangerous”, and (ii) rate the intensity of their fear for the child’s health, with 0 indicating “no fear at all” and 10 indicating a very intense fear. ## Disease-specific health care use A total of 10 self-constructed items are implemented to assess the corona-specific utilization of health services in the past 12 months. Parents are asked to provide information about the frequency of disease-specific health care visits; medical appointment cancellations and their causes; and the provision and quality of alternative virtual office hours. ## Basic survey: Common measures for children and adolescents For the questionnaires aiming at the data collection in children under the age of 12 and adolescents aged 12 years or older, the following measures are applied. ## Perceived stress A self-constructed item aims to assess how stressed the child feels because of the COVID-19 pandemic with an 11-point numerical rating scale ranging from 0 “not stressed at all” to 10 “totally stressed”. As described above, for children under the age of 12, the accompanying parent is asked to rate this item for their child. ## Perceived loneliness To measure how lonely a child feels due to the COVID-19 pandemic, a self-constructed item on a Likert scale ranging from 0 to 10 is applied. Older children are asked to select a response ranging from “not stressed at all” to “totally stressed”, and the parents answer this item for younger children. ## COVID-19-specific well-being in everyday life A self-constructed 5-item scale based on Warschburger et al. [ 59] aims to assess the child’s well-being across several life domains. Children from 12 years are asked to rate the current state of their: family, education/vocational education/occupation, friends, leisure time, and illness. The scales range from 0 “totally bad” to 10 “totally good”. These items are posed to the parents of children when they are younger than 12 years. The German version of the World Health Organization Five Well-Being Index (WHO-5) is additionally administered to measure the current general well-being (e.g., “I was happy and in a good mood”) of children and adolescents during the past 2 weeks [60, 61]. The WHO-5 has a 5-item Likert scale ranging from 0 “at no time” to 5 “all of the time” and has an internal consistency of Cronbach’s α =.89 to Cronbach’s α =.92 [60, 62]. ## School experience Two items have been constructed for the purpose of assessing the educational experiences of adolescents during the COVID-19 pandemic. One multiple-choice question aims to assess the teaching method that the students encountered during the last 2 weeks, with possible answers comprising home-schooling, classroom teaching and frequency, holidays, or having finished school. The other item asks, if applicable, how students have succeeded in learning at home, with a Likert scale ranging from 0 “totally badly” to 10 “totally well”. ## Perceived risk The risk perception of adolescents is measured with three self-constructed items [39]. Adolescents are asked to state how dangerous they consider a COVID-19 infection to be for (i) themselves, and (ii) others on an 11-point Likert scale. Furthermore, adolescents indicate (iii) their perceived risk of a coronavirus infection, on a scale ranging from 0 (“totally unlikely”) to 10 (“very likely”). ## Media usage A 5-point Likert scale established by another survey [63, 64] is administered to assess the electronic media usage of teenagers. The adolescents are asked to report how much time they spend on average during the day with watching TV/videos, a gaming console, using the computer/internet, listening to music, their phone; answers range from “not at all” to “more than 4 hours”. ## Anxiety Anxiety symptoms (e.g., not being able to stop or control worrying) during the past 2 weeks are measured with the German version of the General Anxiety Disorder-Scale (GAD-7) [65, 66]. The 4-point Likert scale consisting of seven items ranges from 0 “not at all” to 3 “nearly every day”. The GAD-7 has an internal consistency of Cronbach’s α =.79 to Cronbach’s α =.91 [67] and studies reported a successful usage in adolescents [68, 69]. ## Depression To detect depressive symptoms during the past 2 weeks (e.g., feeling down, depressed, or hopeless), the German version of the 9-item Patient Health Questionnaire (PHQ-9) [70] is administered as a screening inventory. The 4-point Likert scale of the PHQ-9 ranges from 0 “not at all” to 3 “nearly every day” and has an internal consistency of Cronbach’s α =.88 [71]. ## Web-based extended psychosocial survey In addition to the baseline survey, parents and with their consent also the children (aged > 9 yrs.) are invited to fill a more comprehensive psychosocial web-based survey. Table 2 summarizes the questionnaires used in the parental and child assessment. Table 2Overview of study measures in the web-based surveyStudy Measures: Web-based, extended QuestionnairesParentsTeenagersSocio-demographic and medical assessment General socio-demographic factorsbxx Factors related to the chronic conditionbxx MacArthur Scalex Winkler-IndexaxEmotions and psychological distress during the COVID-19 pandemic COVID-19 pandemic-specific impact on parental well-being and everyday lifebxx Corona-specific burdenaxxPsychosocial adjustment Self-Assessment Manikin SAMxx Well-Being Index WHO-5x Patient Health Questionnaire PHQ 4x Perceived-Stress Scale PSS-4x De-Jong-Gierveld-Skalaxx Strengths and Difficulties Questionnaire SDQxaxa,c Child Health Questionnaire CHQx KIDSCREEN-27axCoping and resources Coping orientation to problems experienced Brief COPEx General self-efficacy scale (ASKU)x Scale for the assessment of internal and external sense of control (IE-4)x OSLO social support scale OSLOx Child’s copingx Child’s ressourcesbx Coping with a disease questionnaire CODIx Questionnaire of Resources in Childhood and Youth FRKJ-8-16ax Social integrationax Corona-specific self-efficacyxFamily relations and interactions Potentially harmful parenting behaviorx Family-related life questionnaire FLQax Brief Parental Burnout scale BPBSx Parental-Representation-Screening-Questionnaire EBF-KJax Corona-specific parental supportbxControl Variables Social desirability scale (SEA-K)xamodifiedbself-constructedcchild-version ## Sociodemographic and medical assessment Two self-constructed items are implemented asking the participants about their age and gender. The child questionnaire includes a third item assessing the adolescents’ living situation (“Where do you currently live?”), while the parental questionnaire incorporates three additional questions regarding the child’s year of birth, weight and height, and location of their child’s treatment. Furthermore, items adapted from Warschburger [59] measure CC-specific medical variables. On a 5-point scale, participants are asked to rate the subjective severity, long-term effect of the CC, and impact of the COVID-19 pandemic on the child’s CC. A fourth item, included in the child questionnaire assesses how well the child is coping with the CC in general, with a scale from 0 = “not at all “to 4 = “very well“. ## COVID-19-specific questions COVID-19-specific questions are included to assess the COVID-19 related impact and burden experienced by the participants. Two self-constructed items adapted from Warschburger et al. [ 59] ask the children and parents to indicate whether they have faced a COVID-19 infection and rate its severity on a 4-point scale ranging from “symptom-free” to “severe”. The parental survey includes 30 additional self-constructed items, assessing, e.g., if their afflicted child suffered from the long-COVID syndrome, or whether they and their child received COVID-19 vaccinations. Modified items based on Calvano et al. [ 39] further measure the subjective burden on several areas of everyday life caused by the COVID-19 pandemic, such as the children’s education or the parents’ work. On a 5-point Likert scale, participants are asked to rate how burdensome they experienced COVID-19 with higher ratings indicating a higher burden. In addition, participants are asked to assess what has caused the most stress and anxiety and what has changed positively during the pandemic. ## Psychosocial adjustment: Overall well-being Two picture-based items using the Self-Assessment Manikin [72] are implemented assessing how the parents and children feel in general, and how stressed they are. ## Psychosocial adjustment: Loneliness The 6-item short version of the De-Jong-Gierveld-Skala [73] is administered to measure the children’s and parents’ overall loneliness. Statements relating to social and emotional loneliness are evaluated on a 4-point scale, with higher scores indicating more frequent loneliness. The questionnaire has an internal consistency of Cronbach’s α =.71 to Cronbach’s α =.76 [73]. ## Psychosocial adjustment: Child behavior To assess the children’s emotional and behavioral problems during the last 12 months, the Strengths and Difficulties Questionnaire (SDQ) is utilized. On a 3-point Likert scale ranging from 0 “not true” to 2 “certainly true”, children are asked to rate problems relating to the subscales hyperactivity (with Cronbach’s α =.87) and conduct problems (α =.73) [74]. The questionnaire for the parents includes a proxy-report of their children’s emotional symptoms, conduct problems, hyperactivity, and peer problems with Cronbach’s α of.66,.60,.76, and α =.58, respectively [75]. ## Sociodemographic assessment The parental socioeconomic status (SES) is measured using the MacArthur Scale [76] and Winkler-Index. The German version of the single-item MacArthur Scale [76] is applied to measure the parental subjective SES. Participants are asked to indicate their social rank on a 10-rung ladder, with the bottom representing people with the lowest education and least money and the top representing people with the highest education and most money. Three items of the Winkler-Index [77] are applied to measure the parent’s socioeconomic status by asking about the highest secondary education, professional degree, and employment status. The family’s SES is derived from an overall score, ranging from 3 to 21. ## Psychosocial adjustment In addition to the measures described above, the extended online survey for parents includes several other questionnaires to measure their psychosocial adjustment. As previously applied in the basic questionnaires, the WHO-5 measures the parents’ overall well-being. Further characteristics of the questionnaire are discussed in section “COVID-19-specific well-being in the everyday life”. To measure the child’s health related quality of life (HRQoL), 16 items of the KIDSCREEN-27 [92] are implemented. The participants are asked to indicate their HRQoL on 5 dimensions. The subscale “physical well-being “comprises 5 items (e.g., “Thinking about the last week, have you felt fit and well?“), 4 items are assigned to the dimension “social support and peers “(e.g., “Thinking about the last week, have you spent time with your friends?“). The internal consistency for each subscale of the KIDSCREEN-27 is Cronbach’s α >.70 [92]. Furthermore, the Kidscreen-10 index is applied to determine a global HRQoL score. The global score is derived from 10 items and has a good internal consistency of Cronbach’s α =.82 [92]. ## Psychosocial adjustment: Anxiety and depression To assess parent’s anxiety and depression levels, the German version of the PHQ-4, the short version of the PHQ-9 and GAD-7 used for adolescents in the basic survey, is applied as screening instrument. Four Items are to be rated on a 4-point Likert scale ranging from 0 “not at all” to 3 “nearly every day”. An internal consistency of Cronbach’s α =.82 has been reported [78]. ## Psychosocial adjustment: Perceived stress The frequency of distress experienced by the subject will be measured using four items with the German version of the Perceived-Stress Scale (PSS-4). On a 5-point Likert scale, parents are asked to indicate how often they felt stressed during the last year (e.g., “In the last 12 months, how often have you felt difficulties were piling up so high that you could not overcome them?“), with a higher score indicating higher chronic stress. The PSS-4 has an internal consistency of Cronbach’s α =.72 [79]. ## Psychosocial adjustment: Quality of life This study applies 13 items of the German version of the Child Health Questionnaire Parent Form (CHQ) to assess the impact of the child’s health on the parental quality of life across several domains. On a 5-point scale, the emotional impact is assessed with 3 items (e.g., “How much emotional worry or concern did each of the following cause for you: your child’s physical health?“), the impact on the family with 6 items (e.g., “How often has your child’s health or behavior limited the types of activities you could do as a family?“), and family cohesion with one item (e.g., “In general, how would you rate your family’s ability to get along with one another?“). On a 4-point Likert scale, 3 items measure the impact of the child’s health on the leisure time (e.g., “Were you limited in the amount of time you had for your own needs because of your child’s emotional well-being?“). The German version of the CHQ reports an internal consistency of Cronbach’s α =.76 for parental emotional impact, and α =.81 for parental time impact. For the family impact subscale, an internal consistency of Cronbach’s α =.83 has been reported [80]. ## Coping and resources The following instruments are used to assess the coping strategies and the resources and burdens of the parents: ## Coping and resources: Parental disease management To assess the parental coping style, the German short version of the coping orientation to problems experienced (Brief COPE) inventory [81] will be utilized. The questionnaire consists of 14 subscales with 2 items each, namely, active coping; planning; positive reframing; acceptance; humour; religion; emotional support; instrumental support; self-distraction; denial; venting; substance use; behavioural disengagement; and self-blame. Each subscale is represented by two items, measured on a four-point Likert scale ranging from “not at all “to “very much“. Higher scores indicate a greater use of the respective coping style. All scales reached Cronbach’s alpha α >.60. ## Coping and resources: Self-efficacy The German version of the General self-efficacy scale (in German: Allgemeine Selbstwirksamkeitsskala (ASKU)) [82] is applied to measure the parental expectations of being competent to deal with daily difficulties and obstacles. The inventory consists of 3 items (e.g., “I can rely on my own abilities in difficult situations“), answered on a 5-point scale ranging from 0 = “doesn’t apply at all” to 4 = “applies completely”. Findings indicate a good reliability of McDonalds’s omega =.81 to.86 [82]. ## Coping and resources: Locus of control Measuring the internal and external sense of control, a four-item scale for the assessment of locus of control (In German: Internale-Externale-Kontrollüberzeugung-4 (IE-4)) [83] is administered. Parents are asked to rate the items (e.g., “If I work hard, I will succeed“) on a 5-point Likert scale ranging from 0 = “doesn’t apply at all” to 4 = “applies completely”. The IE-4 has a McDonalds Omega of.53 to.71 [84]. ## Coping and resources: Social support The 3-item OSLO social support scale (OSS-3) is implemented to measure the level of social support that parents perceive. The questions assess how many close confidents parents have, the amount of concern they receive from others, and how accessible practical help is from their neighbours. The sum score of 3-8 is categorized as poor social support, from 9 to 11 as moderate social support, and 12-14 as strong social support. In a representative German population, the OSS-3 showed an internal consistency of Cronbach’s α =.64 [85]. ## Coping and resources: Child’s coping and resources If their child cannot or does not want to participate themselves, parents are asked to rate the child’s coping and resources. Three items, taken from the COVID-19 Snapshot Monitoring questionnaire [86], are implemented. On a 7-point scale, reaching from 0 = “totally disagree “to 6 = “totally agree”, the proxy-report items assess whether the child suffers from not seeing their friends, whether it is happy to spend more time with the family, and whether it is overall coping well with the changes. Additionally, 6 self-constructed items based on the scales of the Resource Questionnaire for Children and Adolescents (in German: Fragebogen zu Ressourcen im Kindes- und Jugendalter (FRKJ 8–16)) [87] are presented on a 4-point scale to measure the child’s resources as a proxy-report. Ranging from “never true “to “aways true“, the parents are asked to rate their child’s empathy, self-efficacy, confidence, coherence, optimism, and locus of control. ## Family relations and interactions The following tools are used to consider the potential impact at family level. ## Family relations and interactions: Parenting behaviour To measure the risk of harmful parenting, 4 items originating from a study by Clemens et al. [ 88] is implemented. The items range on a 7-point Likert scale from 0 “does not apply at all “to 6 “applies very strongly“, asking the parents to indicate whether they are yelling more at the child, are more impatient, are more scared of slapping the child, or are more scared that their partner will slap the child since the beginning of the COVID-19 pandemic. ## Family relations and interactions: Family-related quality of life The survey includes 12 items assigned to two of three subscales of the family-related life questionnaire (FLQ) developed by Tröster [89]. Four items are ascribed to the subscale “relief from stress and self-fulfilment “and 8 items measure the subscale “social support within the family“. The 5-point scale ranges from 0 = “never/almost never “to 4 = “very frequently”, and the FLQ has an overall good internal consistency of Cronbach’s α =.93 to.94 [89]. ## Family relations and interactions: Parental burnout The 5-item Brief Parental Burnout scale (BPBs) is applied to measure the risk of parental burnout. Parents are asked to rate the frequency of feelings related to burn-out (e.g., “I’m so tired out by my role as a parent that sleeping doesn’t seem like enough.“) on a 3-point response scale reaching from daily to more seldom/never. The BPBs reports an internal consistency of Cronbach’s α =.81 to.84 [90]. ## Control variables The short form of the German Scale for Detecting Test Manipulation through Faking Good and Social Desirability Bias (in German: Skala zur Erfassung von Testverfälschung durch positive Selbstdarstellung und sozial erwünschte Antworttendenzen (SEA-K)) [91] is implemented to control for the social desirability bias. On a 4-point Likert scale, parents are asked to rate how much they agree with statements closely related to social desirability (e.g., “I would never talk behind my employer’s or colleague’s back“). The SEA-K has an internal consistency of Cronbach’s α =.59 [91]. ## Coping and resources: Coping Children’s coping strategies are assessed with the Coping with a disease questionnaire CODI [93]. Children indicate how frequently they use six coping styles on a 5-point Likert scale ranging from “never “to “always“. The items are assigned to the 6 subscales: acceptance, avoidance, cognitive-palliative, distance, emotional reaction, and wishful thinking, with internal consistencies ranging from α =.69 to α =.83 [93]. ## Coping and resources: Resources To assess the children’s personal and social resources, the Questionnaire of Resources in Childhood and Youth (in German: Fragebogen zu Ressourcen im Kindes und Jugendalter (FRKJ-8-16)) is applied. The current study uses the subscales “sense of coherence“, “optimism“, “self-efficacy“, and “parental support“, comprising 6 items each (e.g., “I make sense of my life“). Responses are measured on 4-point Likert scales ranging from “never true “to “always true“. The internal consistency of the subscales varies between Cronbach’s α = 0.69 to α = 0.89 [87]. Based on self-constructed items from another study [59], 6 items are implemented to measure children’s corona-specific self-efficacy. Children are asked to rate the items (e.g., “I always know how to behave during the corona crisis”) on a 5-point Likert scale ranging from “not true at all” to “absolutely true”. ## Coping and resources: Social integration The survey includes 4 items based on self-constructed items from another study [59] to measure the amount of time children spend with their peers during and before the pandemic. On a 5-point Likert scale ranging from “very rarely “to “very often“, children are asked to answer how often they are meeting their friends, how often they met before the pandemic, how often they communicate digitally, and how often they used to communicate before the pandemic. ## Family relations and interactions: Quality of child-parent relationship The German version of the Parental-Representation-Screening-Questionnaire (PRSQ), in German “Elternbildfragebogen für Kinder und Jugendliche “(EBF-KJ), is administered to assess how the children rate the relationship with their parents. This survey uses two modified scales of the EBF-KJ, namely “autonomy “and “overprotection“, asking children to answer 8 items on 5-point Likert scales. The internal consistency of the subscales ranges from Cronbach’s α =.72 to.85 for patients [94]. ## Family relations and interactions: COVID-19-specific social support To assess the perceived support during the COVID-19 pandemic, 8 self-constructed items are applied. On a scale ranging from 0 “completely disagree “to 4 “completely agree“, children are asked how much they have talked about the coronavirus with their parents (e.g., “My parents have explained to me what COVID-19 is“) and their friends (e.g., “I have talked to my friends about COVID-19″). ## Sample size With respect to the baseline survey, we will implement a convenience sampling procedure: That means that all facilities taking part in one of the three patient registries have been informed about the ongoing KICK-COVID study via newsletters and regular meetings of the collaborators. We aim to include (and re-assess) as many participants as possible in order to get sufficient data sets to make generalizable statements. Therefore, we intend to recruit at least 1000 parents of either children or adolescents per CC. In addition, we assume that around 500 adolescents (> 12 yrs) per CC will fill in the baseline survey. For the web-based psychosocial assessment, the primary goal is to get a deeper understanding of the assumed psychosocial processes that can explain the level of mental and physical strain experienced. Therefore, we aim to include a subsample of participants of the baseline survey. To apply structural equation modeling (SEM) required sample sizes are dependent on various data characteristics such as data distribution, the number of missing data, or parameters that have to be computed. According to Kline [95], the sample size should fall above the number of 200 although sample sizes with at least 100 participants might be sufficient. Since drop-out rates vary between 0 and $54\%$ [96] we aim to include between 300 and 400 parents and about 100 adolescents. ## Statistical analyses In accordance with common guidelines, we will only include online survey data with a high data quality indication but a “realistic” completion time of the questionnaire (a relative speed indicator higher than 2 and a total completion time of at least 5 minutes [97]). With respect to missing data and drop-outs, multiple imputations via fully conditional specification implemented will be performed [98]. We will apply multivariate ANOVAs and (logistic, hierarchal) regression analyses to analyze disease-specific differences with respect to health care use, risk perception and mental health. Sociodemographic variables such as age and sex of the child, disease severity, and risk perception will be included as predictors, as well as the date the questionnaire is completed as indicator of the stringency of current containment measures. To analyze the interplay between physical and mental health and the impact of corona-related stressors, SEM will be applied. All analyses will be conducted using SPSS, SAS; R or Mplus. ## Discussion The COVID-19 pandemic has led to far-reaching changes in everyday life for everyone worldwide. Families with underage young people in particular are and were affected in many ways: be it the closure of schools and daycare centers with the resulting need for homeschooling or home care, far-reaching contact restrictions and lockdowns, or the changed working situation of parents with home offices or part-time work, to name just a few measures that aimed to curb the further spread of the contagious virus. These measures have not spared the health system with the (partial) closure of outpatient clinics, limited contact times, or the establishment of video consultation hours. It is precisely these restrictions that affect those who are most in need of regular medical and psychosocial care: people with CCs. Since the outbreak of the pandemic, numerous studies have examined the consequences for society. There is now meta-analytical evidence on the psychological consequences for children and adolescents [33, 46, 99] and their parents [100]. The psychosocial effects of the COVID-19 pandemic on children and their parents have also been and are being extensively examined for the German-speaking area, e.g., with the COPSY study [101] or the LIFE Child study [102]. In contrast, KICK-COVID focuses explicitly on the group of children with a chronic illness and their parents. With the help of three patient registers, not only medical outcomes but also psychosocial outcomes and their interplay can be examined comprehensively. ## Strengths and limitations To the best of our knowledge, no other national study focuses on the group of children and adolescents with a chronic illness during the COVID-19 pandemic. The KICK-COVID study and its expected results must be viewed in light of its strengths and weaknesses. A major strength of our approach is the large expected sample size. The short economic questionnaire aims to reach as many of those affected as possible. The large sample size makes it possible to make statements that are as representative as possible, not only in relation to the respective underlying physical disease, but also across the disease groups in order to identify generic and condition-specific patterns. In addition, the extended psychosocial web survey will enrich our database by providing more detailed information on the psychosocial situation and coping strategies of the families. Of note, a follow-up over 1 year will allow us to examine short and longer-term effects and different trajectories over time. With respect to the limitations of the study, it should be mentioned that a self-selection of interested clinicians and patients cannot be ruled out. Thus, clinicians might not approach certain patients, either because they may perceive these persons as too distressed or because they believe that the afflicted patients do not have any problems. Especially, a bias regarding the inclusion of the high-risk group in the obesity sample can be expected for the basic survey and the extended survey in particular. Due to organizational reasons, we are not able to implement the questionnaires as obligatory, and we could not offer monetary compensation for the clinics and patients to mitigate this effect. However, we are aiming for a large sample that may be able to compensate for this effect. Furthermore, the data already available from the registers allow us to characterize our sample in relation to the total population. Of course, self-selection could be even more pronounced when parents are invited to fill in the questionnaire. Due to ethical reasons and German data protection law regulations, we have to ask the parents to forward the children’s questionnaire with their consent to their children. Taken together, the results of the proposed study will provide empirical data on how to support families with a child suffering from a CC and contribute to a more successful and sustainable health system. 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--- title: 'Strengthening primary care for diabetes and hypertension in Eswatini: study protocol for a nationwide cluster-randomized controlled trial' authors: - Michaela Theilmann - Ntombifuthi Ginindza - John Myeni - Sijabulile Dlamini - Bongekile Thobekile Cindzi - Dumezweni Dlamini - Thobile L. Dlamini - Maike Greve - Harsh Vivek Harkare - Mbuso Hleta - Philile Khumalo - Lutz M. Kolbe - Simon Lewin - Lisa-Rufaro Marowa - Sakhile Masuku - Dumsile Mavuso - Marjan Molemans - Nyasatu Ntshalintshali - Nomathemba Nxumalo - Brianna Osetinsky - Christopher Pell - Ria Reis - Fortunate Shabalala - Bongumusa R. Simelane - Lisa Stehr - Fabrizio Tediosi - Frank van Leth - Jan-Walter De Neve - Till Bärnighausen - Pascal Geldsetzer journal: Trials year: 2023 pmcid: PMC10031170 doi: 10.1186/s13063-023-07096-4 license: CC BY 4.0 --- # Strengthening primary care for diabetes and hypertension in Eswatini: study protocol for a nationwide cluster-randomized controlled trial ## Abstract ### Background Diabetes and hypertension are increasingly important population health challenges in Eswatini. Prior to this project, healthcare for these conditions was primarily provided through physician-led teams at tertiary care facilities and accessed by only a small fraction of people living with diabetes or hypertension. This trial tests and evaluates two community-based healthcare service models implemented at the national level, which involve health care personnel at primary care facilities and utilize the country’s public sector community health worker cadre (the rural health motivators [RHMs]) to help generate demand for care. ### Methods This study is a cluster-randomized controlled trial with two treatment arms and one control arm. The unit of randomization is a primary healthcare facility along with all RHMs (and their corresponding service areas) assigned to the facility. A total of 84 primary healthcare facilities were randomized in a 1:1:1 ratio to the three study arms. The first treatment arm implements differentiated service delivery (DSD) models at the clinic and community levels with the objective of improving treatment uptake and adherence among clients with diabetes or hypertension. In the second treatment arm, community distribution points (CDPs), which previously targeted clients living with human immunodeficiency virus, extend their services to clients with diabetes or hypertension by allowing them to pick up medications and obtain routine nurse-led follow-up visits in their community rather than at the healthcare facility. In both treatment arms, RHMs visit households regularly, screen clients at risk, provide personalized counseling, and refer clients to either primary care clinics or the nearest CDP. In the control arm, primary care clinics provide diabetes and hypertension care services but without the involvement of RHMs and the implementation of DSD models or CDPs. The primary endpoints are mean glycated hemoglobin (HbA1c) and systolic blood pressure among adults aged 40 years and older living with diabetes or hypertension, respectively. These endpoints will be assessed through a household survey in the RHM service areas. In addition to the health impact evaluation, we will conduct studies on cost-effectiveness, syndemics, and the intervention’s implementation processes. ### Discussion This study has the ambition to assist the Eswatini government in selecting the most effective delivery model for diabetes and hypertension care. The evidence generated with this national-level cluster-randomized controlled trial may also prove useful to policy makers in the wider Sub-Saharan African region. ### Trial registration NCT04183413. Trial registration date: December 3, 2019 ### Supplementary Information The online version contains supplementary material available at 10.1186/s13063-023-07096-4. ## Diabetes and hypertension in Eswatini Diabetes and hypertension are a rapidly growing public health problem in the Kingdom of Eswatini. Approximately a quarter of the population aged 15–69 have hypertension and almost one fifth live with diabetes or prediabetes [1]. Globally, diabetes and hypertension are among the leading drivers of disability, being responsible for the majority of strokes, kidney failure, blindness, and lower limb amputation [2]. Population aging will likely further increase these numbers over the coming years [3]. Before the start of this study in 2019, the Eswatini Ministry of Health identified three cross-cutting barriers that prevented people living with diabetes or hypertension from benefiting from prompt, sustained access to appropriate healthcare. First, there was a low detection rate because at-risk clients were not systematically screened for diabetes and hypertension. Eighty to ninety percent of people living with diabetes or hypertension were not aware of having these conditions [1]. Second, access to care for diabetes and hypertension was poor. All diabetes and hypertension care was provided by physician-led teams in 13 tertiary care facilities, which were concentrated in urban areas. Less than $10\%$ of the population lived within 2 h of travel time to the nearest public hospital [4]. Third, the lack of standardized treatment guidelines for diabetes and hypertension meant that the quality of disease management was often low and varied significantly between facilities and regions. ## Study objectives WHO-PEN@Scale is a nationwide cluster-randomized controlled trial with the objective of comprehensively evaluating two diabetes and hypertension health service provision models at the community and household levels. First, we will estimate the causal impact of these models on diabetes and hypertension control at the population level. Second, comprehensive implementation and process evaluations will identify implementation challenges and enablers, including the acceptability of the interventions among healthcare workers. Third, we will endeavor to identify syndemic processes involving diabetes, hypertension, and other diseases such as human immunodeficiency virus (HIV). Fourth, we will conduct detailed cost analyses both from the the government and client perspective. While all these research components may yield valuable results on their own, we believe that their interaction and complementarity will allow us to draw a comprehensive and overarching picture of the government’s health care reform. Here we detail the study protocol for the first and third objectives (impact assessment and syndemics analysis), whereas study protocols for the other objectives (implementation and acceptability studies, cost analysis) will be reported elsewhere. ## Methods / design We used the SPIRIT reporting guidelines in the development of this study protocol (Fig. 1) [5].Fig. 1SPIRIT + figure. Example template of recommended content for the schedule of enrolment, interventions, and assessments* ## Study setting The Kingdom of Eswatini is a country in Southern Africa with a population of approximately 1.2 million [6]. Although only $11\%$ of the adult population aged 45 to 69 years reported currently smoking, and $18\%$ having drunk alcohol in the past 30 days, over $60\%$ of adults were overweight or obese in 2014 [1]. Over one in two adults in this age range had raised blood pressure or took hypertension medication and among these less than $10\%$ had achieved control of their blood pressure (defined as a systolic blood pressure less than 140 mmHg and diastolic blood pressure less than 90 mmHg). Among all adults aged 15 to 69 years with hypertension, $68\%$ reported to have ever had their blood pressure measured, $37\%$ to have ever been diagnosed with hypertension, $21\%$ to be taking hypertension medication, and $7\%$ had a controlled blood pressure [7]. Almost a tenth of adults aged 45 to 69 years had impaired fasting glycemia and one in four adults had raised blood glucose [1]. Eswatini has an HIV prevalence of $28\%$ among adults aged 15–49, which is one of the highest prevalence estimates in the world [8]. In 2019, the Eswatini government allocated $7\%$ of its budget to health care expenditure [9]. These figures illustrate the vast unmet need for diabetes and hypertension care, which, at the start of this project, was primarily provided by physicians in hospitals (Table 1). To successfully address the high health burden associated with diabetes and hypertension, lower levels of care, such as nurses in primary care clinics and community health workers in communities, could represent a highly valuable resource. Table 1Health system dataPopulation1,160,000Health facilitiesTertiary care levelHospitals4National Referral Hospitals3Regional Hospital7Public Health Units8Primary care levelPrimary care clinic (public)118Health workforcePhysicians126Nurses1505Community health workers (Rural Health Motivators) > 3000Sources: Eswatini Ministry of Health: Human resources for health strategic plan [2019-2023], unpublished and UN population estimates [6] ## WHO-PEN The World Health Organization (WHO) Package of Essential Non-Communicable Disease (NCD) Interventions (WHO-PEN) is a collection of cost-effective interventions at the primary care level in low-resource settings. It includes guidelines on the screening, diagnosis, and treatment (medical as well as lifestyle counseling) of several NCDs including diabetes and hypertension. The goal of WHO-PEN is to address the unmet need for high-quality NCD care in low- and middle-income countries (LMICs) by increasing service coverage. In the case of our study, we aim to improve access to care through the extension of services offered as well as shifting care from tertiary facilities to health personnel at primary care clinics. Eswatini’s public sector cadre of community health workers, who regularly visit households, could further support individuals in obtaining and adhering to care. The WHO-PEN@Scale project aims to evaluate the scale-up of these changes in health service provision. ## Study arms Standard of care WHO-PEN@Scale consists of two treatment arms and one control arm. The standard of care (SoC) arm is the control arm and consists of diabetes and hypertension care as it is currently provided across the country. Until 2020, care for uncomplicated and complicated cases was primarily provided at tertiary care facilities by physician-led teams. There was no systematic screening of at-risk clients, but the screening was mainly conducted when clients presented at hospitals with other severe conditions or during antenatal care at primary care facilities. Treatment for diabetes and hypertension was exclusively initiated at tertiary care facilities, and drug refills were also mainly obtained there. No standardized treatment nor lifestyle counseling guidelines for diabetes and hypertension care were available. At the end of 2019, the Eswatini Ministry of Health started the decentralization of health services according to the WHO-PEN guidelines in the clinics that we randomly allocated to the two treatment arms. Health care personnel at primary care facilities were trained on the screening and treatment of diabetes and hypertension and facilities were equipped with blood glucose and digital blood pressure measurement devices. Nurses started providing health services for diabetes and hypertension according to the developed Standard Operating Procedures guidelines (SOPs, see below for more details). When the COVID-19 pandemic began in early 2020, the Eswatini government initiated an “emergency decentralization” to decongest tertiary care facilities and to protect people living with diabetes and hypertension, who are at high risk of severe outcomes from COVID-19 [10]. The emergency decentralization encompassed a nationwide scale-up of the WHO-PEN intervention originally envisioned to be implemented only in the two treatment arms of the WHO-PEN@Scale project. As such, care for diabetes and hypertension was shifted from physicians in tertiary facilities to nurses in all public primary care clinics across the country in April 2020. Thus, what had initially been planned to be the health service decentralization to be evaluated under the WHO-PEN@Scale study, became the new standard of care. In all public primary care clinics across Eswatini, nurses were trained on systematic screening, treatment initiation, lifestyle counseling, and follow-up guided by the SOPs (Eswatini Ministry of Health: Clinic level non-communicable diseases case management desk guide, unpublished). Nurses were instructed to screen all clients for diabetes and hypertension if they have a past or family history of cardiovascular disease, diabetes, or hypertension; are aged 40 years or older; or have risk factors such as overweight, tobacco use, lack of exercise, or an unhealthy diet. Uncomplicated cases are to be initiated, treated, and followed up by the nurses in the primary care clinics whereas complicated cases (“complex clients”, see Table 2 for the definition) are referred to tertiary care facilities. Table 2Definition of complex clientsHypertensionaDiabetesaAge < 40Age < 40Severe hypertension (≥ $\frac{180}{110}$ mmHg)Severe hypertension (≥ $\frac{180}{110}$ mmHg)Multiple severe risk factorsMultiple severe risk factorsMultiple comorbiditiesMultiple comorbiditiesElderly clientsElderly clientsPregnant womenPregnant womenSymptoms of cardiovascular diseaseSymptoms of cardiovascular diseaseKidney diseaseKidney diseaseVisual problems (retinopathy)Visual problems (retinopathy)Suspected type 1 diabetesSuspected type 1 diabetesSuspected secondary hypertensionSuspected secondary hypertensionRecurrent hypoglycemiaDiabetic ketoacidosisHyperosmolar hyperglycemic stateaA client is classified as “complex” if any one of the criteria listed in the table applies Diabetes and hypertension care follow a stepwise standardized procedure that guides nurses through care depending on the client’s disease status (Tables 3 and 4). Prescriptions are given to the client on a monthly basis. The SOPs also include a detailed step-by-step guide on how to structure counseling sessions, behavioral risk factor modification advice messages, and details on drugs and their correct dose. These SOPs were developed during a successful decentralization pilot in the Lubombo region (one out of four regions in Eswatini), conducted between 2014 and 2016, and adapted for the WHO-PEN@Scale project based on the pilot’s results [11].[2]Treatment arm 1: Differentiated service delivery modelsTable 3Stepwise management of hypertensionEntry pointManagementStep 1: Community clinic level Mild hypertension:Lifestyle modification if client is committed BP 140–$\frac{159}{90}$–99 mmHgIf not controlled after 1–3 months, go to step 2If multiple risk factors present, go to step 2For complex clients, refer to physician-led serviceStep 2: Community clinic level Failure at step 1 ORLifestyle modification Moderate hypertension: + Hydrochlorothiazidea 12.5 to 25 mg daily until target BP reached BP 160–$\frac{179}{100}$–109 mmHgStep 3: Initiation at hospital level. Follow up at clinic level when stable Failure at step 2 ORLifestyle modification Severe hypertension:b + Hydrochlorothiazide 25 mg daily BP ≥ $\frac{180}{110}$ mmHg + ACE inhibitor (e.g., Captopril) ORCalcium channel blocker (e.g., Nifedipine SR)Step 4: Initiation at hospital level. Follow up at clinic level when stable Failure at step 3Lifestyle modification + Hydrochlorothiazide 25 mg daily + ACE inhibitor (e.g., Captopril) + Calcium channel blocker (e.g., Nifedipine SR)Step 5: Hospital level Failure at step 4Clients who have failed at step 4 should be managed by physician-ledservices and managed according to the physician’s best judgmentAbbreviations: BP blood pressureaHydrochlorothiazide can cause elevated blood sugar. When initiating in diabetic or pre-diabetic clients, monitor blood glucose at least twice per week for the first 2 weeksbFor clients not already on treatment, if BP ≥ $\frac{180}{110}$, give a stat dose of Captopril 12.5 mg by the nurse before referring to the doctor-led service immediatelyTable 4Stepwise management of type 2 diabetesEntry pointManagementStep 1: Community clinic level Prediabetes:Inform client they may develop diabetes in future FBG 5.6–6.9 mmol/lAdvise lifestyle modification RBG 7–11 mmol/lCheck blood glucose every 6 monthsStep 2: Community clinic level Diabetes:Lifestyle modification if client is committed FBG 7–10 mmol/lIf not controlled after 1–3 months go to step 3 RBG ≥ 11.1–17 mmol/lIf FBG > 10 mmol/l or RBG > 17 go to step 3For complex clients refer to doctor-led serviceStep 3: Initiation at hospital level. Follow up at clinic level when stable Failure at step 2 ORLifestyle modification + FBG > 10 mmol/lMetformin RBG > 17 mmol/lStep 4: Initiation at hospital level. Follow up at clinic level when stable Failure at step 3Lifestyle modification + Metformin + SulphonylureaStep 5: Hospital level Failure at step 4Consider insulinFollow up at hospitalAbbreviations: FBG fasting blood glucose, RBG random blood glucose The first treatment arm, the differentiated service delivery (DSD) arm, adapts models developed initially for HIV care. The objective of the DSD models is to improve access and retention to HIV care and reduce the burden placed on the health system [12]. In Eswatini, these models have been implemented since 2016 and are viewed by the government as crucial in the HIV response [13]. In the DSD arm, three mutually exclusive models are introduced for diabetes and hypertension care: (i) the facility-based fast-track model, (ii) the facility-based treatment club model, and (iii) the community-level advisory groups. The eligibility criteria for the models are detailed in Table 5. Clients can choose whether they want to be enrolled in a DSD model and if so, in which. Table 5Eligibility criteria for differentiated service delivery modelsAge ≥ 18 yearsMonths on medication ≥ 12 monthsBlood pressure/blood glucoseBlood pressure < $\frac{140}{90}$ mmHg and/orHbA1c < $6.5\%$ or fasting blood glucose < 7.0 mmol/lPrior clinic visits for diabetes/hypertension care ≥ Two clinic visits for diabetes/hypertension careAdditional criteria if comorbid HIV ≥ 12 months on ART, undetectable viral load or CD4 cell count > 500 cells/mm3, no TB, ≥ two clinic visits for ART, not pregnant nor breastfeedingAbbreviations: ART antiretroviral therapy, HbA1c hemoglobin A1C, HIV human immunodeficiency virus, TB tuberculosis In the facility-based fast-track model, clients are given 3-month prescriptions and an appointment for their medication collection (Eswatini Ministry of Health: Standard operating procedures for NCD Fast-Track Model, unpublished). Both reduce the travel and waiting time a client has to spend to obtain diabetes and hypertension care. When a client arrives at the clinic, they do not have to queue with the other clients but rather is received by the health care personnel according to a pre-scheduled appointment. If no health concerns arise, the client collects their medication from a designated fast-track dispensing point at the facility. Because six-monthly visits and thorough health check-ups are required for clients with diabetes or hypertension, each fast-track visit is followed by a normal clinic visit 3 months later during which standard of care treatment guidelines are followed. After these visits, the client is given a 3-month drug prescription and a date for the next fast-track appointment. This model mainly targets the working population living and/or working near a facility. The facility-based treatment club (FTC) DSD model involves groups of at most 20 clients with diabetes and/or hypertension (Eswatini Ministry of Health: Standard operating procedures for FTCs, unpublished). During bi-monthly treatment club meetings, the facilitator (usually the nurse) provides general health counseling on diabetes, hypertension, nutrition, exercise, HIV, and cervical and prostate cancer screening. Furthermore, the blood pressure, blood glucose, and weight of all participants are measured and clients receive medication for the next 2 months. Every 6 months, the clients receive a full health check-up at the clinic after the club meeting. If a client is unwell during any of the meetings, they are referred either to the clinic or a tertiary care facility for an immediate check-up. This model mainly targets clients living near a facility. The third model, community advisory groups (CAGs), are groups of a maximum of six clients with diabetes and/or hypertension who meet monthly in their respective community (Eswatini Ministry of Health: Standard operating procedures for Community-based Adherence Groups, unpublished). Groups are composed of members of an existing social network, such as family members, friends, or colleagues. Groups are equipped with a digital blood pressure monitor and a point-of-care glucometer to monitor their blood pressure/glucose during each meeting. Members are trained on the use of these devices, screening each other for tuberculosis, and referral procedures. Team members take turns in collecting the medication for the entire group from the clinic. This ensures that clients receive the recommended health check-up at least every 6 months and obtain their medication without having to visit the clinic each month. Each group is assigned a nurse that can be contacted in case of challenges and questions. This model primarily targets clients living in remote areas.[3]Treatment arm 2: Community distribution points The second treatment arm, the community distribution point (CDP) arm, leverages existing community outreach sessions that offer HIV care and now additionally offer services for clients with diabetes and/or hypertension. CDPs are sites that are set up temporarily within a community once a month in central locations, such as community centers or schools. Each CDP is linked to one clinic, which means that CDPs are organized through this clinic. A team consisting of a nurse and additional health personnel offer diabetes and hypertension screening, treatment initiation, adherence support, and counseling according to the standardized treatment guidelines described above. ## Rural Health Motivators—the government-led community health worker cadre Evidence from high-income countries shows that the involvement of community-based health workers to provide support to people living with chronic health conditions can improve linkage to, and retention in, care through health counseling and treatment adherence support at the community or household level [14]. It may also allow other cadres, such as nurses and physicians, to focus on care for people with more complex problems as tasks that do not require extensive medical training, such as providing health information or following up with clients who missed appointments, can be carried out by community health workers (CHWs) [13]. CHWs, therefore, have the potential to play a critical role in scaling up client-centered care for diabetes and hypertension in Eswatini and other LMICs. However, the extent to which CHW programs for chronic health conditions in LMICs can achieve this goal, and which delivery models are likely to be most effective, feasible, and acceptable, need to be systematically evaluated. As of now, comprehensive and rigorous evidence on the effectiveness of CHW involvement in health care provision for diabetes and hypertension in LMICs is scarce. Both treatment arms of the WHO-PEN@Scale study involve the government-led and government-funded community health worker cadre, the Rural Health Motivators (RHMs), in addition to the service delivery models described above. The RHM program is a community-based health care volunteer program, which was established in 1976 by the Eswatini Ministry of Health to facilitate the extension of health promotion services to the communities through interpersonal communication (Eswatini Ministry of Health: Community based health services: annual program report 2018, unpublished). There are over 3000 RHMs across the country. They are non-salaried and non-specialist and offer services focused on basic health promotion messaging for sexual and reproductive health, nutrition, and child health [15]. To ensure participation and ownership of the community, RHMs are chosen at the community level and trained by the Ministry of Health. $53\%$ of RHMs is over the age of 55 and $94\%$ is female. Additional details on the RHMs are available elsewhere [16, 15]. In WHO-PEN@Scale, RHMs are involved with the following tasks: (i) basic screening of clients at risk of diabetes and/or hypertension, (ii) extensive, personalized lifestyle counseling, (iii) referral of clients at risk of diabetes and/or hypertension, (iv) monitoring treatment adherence and health status of clients with diabetes and/or hypertension, and (v) demand creation for services offered in the respective treatment arm. RHMs screen clients based on their age, sex, and measured waist circumference. They are equipped with standardized guidelines on screening as well as health messaging in the form of a flip chart specifically developed for them. In the DSD arm, RHMs will refer clients to clinics, and in the CDP arm to the CDPs. Urgent cases will immediately be referred to primary care clinics or tertiary care facilities. ## Theory of change Figure 2 details the theory of change for the WHO-PEN@Scale project. In this section, we describe this theory in more detail. Fig. 2Theory of change. Abbreviations: BG = blood glucose, BP = blood pressure, CAG = community advisory group, CDP = community distribution point, DSD = differentiated service delivery, FTC = facility-based treatment club, HCW = health care worker, RHM = Rural Health Motivator, SOPs = standard operating procedures ## Problem A large share of people living with diabetes or hypertension have not been diagnosed. Furthermore, blood glucose and blood pressure control are often not achieved even among those who are aware of their condition. ## Assumptions The WHO-PEN@Scale study relies on several assumptions. First of all, it is assumed that the health system in Eswatini has the capacity to absorb new clients who are linked to care because of the interventions. This includes the assumption that the health personnel has the capacity to extend the set of services offered to clients with diabetes and hypertension. Second, it is required that the needs for and benefits of diabetes and hypertension care are recognized across all levels. This includes the health personnel at the facilities, who are assumed to recognize that they can play an active role and contribute considerably to the improvement of diabetes and hypertension prevention and control by extending health services offered at the primary care clinic level. In addition, it needs to be assumed that the RHMs visit a given number of households and recognize that their clients are either at risk of diabetes or hypertension or in need of better care. Finally, clients also need to recognize that diabetes and hypertension have severe health consequences in the long run and that they can benefit from care even if, at a given moment, they feel healthy. Third, for the DSD arm, it is assumed that clients and health care workers see the benefit of the DSD models. Although it might lead to a higher work load at first, once established, these models will ease the burden on healthcare workers by saving time and resources. Clients might need to incorporate the DSD model into their life routine and adhere to scheduled meetings, for example. Despite these upfront costs, it is assumed that clients understand that in the long term they will benefit from the change in the service provision model. The last assumption is that health care workers and RHMs provide health services according to the standard operating procedures developed by the Ministry of Health. ## Input The WHO-PEN@Scale project requires several inputs. If an increasing number of people living with diabetes or hypertension are linked to care, there will be a larger demand for diabetes and hypertension medication. For the implementation of the interventions, additional human resources are required. For example, an NCD coordinator in each region or nurses preparing and implementing the DSD models and the CDPs. Furthermore, existing health personnel needs to be involved in the activities such as the health care workers and the different levels of the RHM program. For the implementation and training on the models themselves, resources for printing, transport, and lunches are required. The CAGs need to be equipped with point-of-care blood glucose and blood pressure measurement devices. The facility-based treatment club needs a meeting venue, ideally at the clinic. The implementation of the CDPs will require additional transport and a location where they can be set up. The RHMs need to be equipped with tape measures to measure waist circumference and flip charts for health education and lifestyle counseling. ## Activities and output for the DSD models and CDPs First, the SOPs need to be developed. Then, the main activities involve the training of the health care personnel on these SOPs, the enrolment of clients in the different DSD models, and the distribution of equipment to the CAGs. The expected outputs are the finalized standard operating procedures, the trainings of healthcare workers, and a number of DSD models as well as CDPs implemented. ## Activities and output at the RHM level For the RHMs, the flip chart, which is used to guide them during the household visit, and a monitoring tool need to be developed. Then, the RHMs need to be trained in the use of these instruments. The expected outputs are the RHM trainings and the finalized documents (flip charts, monitoring tool). ## Outcomes These inputs, activities, and outputs will result in several outcomes. At the health system level, awareness of diabetes and hypertension will have increased among health care workers and RHMs. Nurses will enroll clients in the DSD models or advise them to go to CDPs. RHMs will screen and refer clients at risk of diabetes and hypertension. At the client level, this will result in increased awareness and the uptake of DSD models and services provided at CDPs as well as clients seeking health care at primary care facilities following the RHM’s referral. ## Impact Finally, the implementation of WHO-Pen@Scale will result in better control of blood glucose and blood pressure among the population living with diabetes and/or hypertension. ## Trial duration The funding for WHO-PEN@Scale started on January 1, 2019, which is also the study start. Recruitment of clients for the DSD and CDP arms started in November 2021. The WHO-PEN@Scale study will be completed on March 31, 2023. ## Randomization The unit of randomization is a primary healthcare clinic along with all RHMs (and their corresponding service area) that are, as per the public health system structure, assigned to that clinic. There are 118 public primary healthcare facilities in Eswatini. The 97 clinics connected to the Client Management Information System (CMIS) at trial start (in January 2019) were eligible for inclusion in the study. The CMIS is the electronic health registry used by health personnel, which documents clients’ disease trajectory, health-seeking behavior, medication prescription, and health status. Of these 97 clinics, nine had to be excluded because a major non-governmental organization (NGO) was planning to implement different health service decentralization models for NCDs there and four (one per region) were excluded because they were determined to be pilot clinics by the Ministry of Health. The remaining 84 were evenly randomized across study arms. We conducted a stratified randomization. Clinics were randomly allocated to the three study arms stratified by region (with there being four regions in Eswatini), rural/urban location of the clinic, and education (share of the population living in the clinic’s 5-km catchment area with completed secondary education or higher). The randomization was conducted by the Heidelberg Institute of Medical Biometry, which is not involved in the study and thus an independent entity. During the preparation for implementation, it became clear that seven clinics were unable to implement the interventions and needed to be replaced with clinics without CMIS. Randomization of the replacements was stratified by region only as there were no matching clinics based on rural–urban location or educational profile of the surrounding population. The random sampling of replacement clinics was undertaken by study staff at the Heidelberg Institute of Global Health. The code and seed used were the same as in the first round of randomization. ## Endpoints The primary outcomes are (i) mean glycated hemoglobin (HbA1c) among adults aged 40 years and older with diabetes and (ii) mean systolic blood pressure among adults aged 40 years and older with hypertension. For definitions of diabetes and hypertension, see Table 6.Table 6Definition of population included in outcome analysisPrediabetesHbA1c ≥ $5.7\%$ and ≤ $6.4\%$DiabetesHbA1c > $6.4\%$ orSelf-reported previous diabetes diagnosisHypertensionSystolic blood pressure ≥ 140 mmHg orDiastolic blood pressure ≥ 90 mmHg orSelf-reported previous hypertension diagnosis The secondary outcomes are:Mean HbA1c among adults aged 40 years and older with diabetes or prediabetesProportion of adults aged 40 years and older with diabetes who have an HbA1c less than $7.0\%$ (glycemic control)Proportion of adults aged 40 years and older with diabetes who report to have ever been tested for diabetesProportion of adults aged 40 years and older with diabetes or prediabetes who report to have ever been tested for diabetesProportion of adults aged 40 years and older with diabetes who report to have been diagnosed with diabetes prior to the household surveyProportion of adults aged 40 years and older with diabetes or prediabetes who report to have been diagnosed with diabetes or prediabetes prior to the household surveyProportion of adults aged 40 years and older with diabetes who report to be taking medication for their diabetesMean systolic blood pressure among adults aged 40 years and older with diabetesMean diastolic blood pressure among adults aged 40 years and older with diabetesMean systolic blood pressure among adults aged 40 years and older with diabetes or prediabetesMean diastolic blood pressure among adults aged 40 years and older with diabetes or prediabetesMean diastolic blood pressure among adults aged 40 years and older with hypertensionProportion of adults aged 40 years and older with hypertension who have a systolic blood pressure < 140 mmHg and a diastolic blood pressure < 90 mmHg (hypertension control)Proportion of adults aged 40 years and older with hypertension who report to have ever had their blood pressure measuredProportion of adults aged 40 years and older with hypertension who report to have been diagnosed with hypertension prior to the household surveyProportion of adults aged 40 years and older with hypertension who report to be taking blood-pressure-lowering medicationProportion of adults aged 40 years and older with diabetes, prediabetes, or hypertension who report to be a current smokerProportion of adults aged 40 years and older with diabetes, prediabetes, or hypertension who report to be drinking alcohol daily. Mean number of minutes in a typical week spent doing moderate- or vigorous-intensity exercise among adults aged 40 years and older with diabetes, prediabetes, or hypertensionProportion of adults aged 40 years and older with diabetes, prediabetes, or hypertension who correctly responded to each individual question on diabetes- and hypertension-related knowledge. The primary and secondary outcomes will be assessed via a household survey conducted in the areas served by the RHMs that are assigned to each study clinic. ## Sampling strategy for the household survey In Eswatini’s public sector health system, each RHM is assigned an area within their community. We refer to these areas as RHM service areas. Within each service area, all households are served by only one RHM. RHM service areas do not overlap (that is, it is never the case that two RHMs serve all or part of the same area). For clinics with more than ten assigned RHMs, we will randomly select ten RHMs with their corresponding service area. The households in each RHM service area will be sampled based on a “random walk” procedure. The starting point for the random walk is the RHM’s homestead, which can be located anywhere in their service area. When having arrived at the RHM’s homestead, the survey team will obtain information from the RHM on the number of households in, and the geographical boundaries of, the service area. Then, the team will randomly generate a number (using a random number generator on a tablet) from one to four to determine in which direction they will move (North, East, South, or West). All households in this direction will be included in the survey. If the survey team reaches the RHM service area boundary before completing the required number of interviews, they return to the RHM homestead and generate a new random number that defines the next direction in which the survey team will walk. Whether or not the RHM provided the survey team with accurate information on the service area boundaries will be verified by the survey team by also visiting the first household beyond the border of the service area to enquire from the household head which RHM serves their household. For clinics with less than ten RHMs, all RHM service areas, and all households within these areas, will be visited by the survey team for inclusion in the survey. ## Inclusion and exclusion criteria for the household survey All non-pregnant household members aged 40 years or older will be invited to participate in the survey. The first part of the survey, which has as its main purpose the screening of all adults aged 40 years or older for prediabetes, diabetes, and hypertension, consists of (i) a short interviewer-administered questionnaire (see Additional file 1) covering previous diagnoses and current treatment of diabetes and hypertension, (ii) blood pressure measurements, and (iii) fasting blood glucose and HbA1c measurements. For the impact assessment, those identified as having prediabetes, diabetes, or hypertension based on this screening questionnaire and following the definitions in Table 6 will additionally be included in the survey for a more detailed assessment of self-reported characteristics as well as body measurements (see Additional file 1). For the analysis of syndemic processes, a random subsample of 600 participants aged 40 years and older who do not have prediabetes, diabetes, or hypertension based on the screening questionnaire results and definitions in Table 6 will also be included in these more extensive assessments. Apart from pregnancy, the only exclusion criterion is an inability to provide written informed consent. ## Return visits Upon arrival at the household, the interviewer will compile a list of all household members aged 30 years and older. If one or more eligible household members are not at home and do not return while the survey team is still at the household, the interviewer schedules a second visit either on the same or on another day. If the household member is still not available on the second visit, a third visit is scheduled. If the household member is not available again, the household member is marked as “not at home”. If a homestead is occupied but no member is present, the survey team will visit on two additional days at different times. If no household member is met during these visits, the household is marked as “not at home”. ## Survey instrument modules As described above, the objective of the household screening questionnaire is to identify all non-pregnant household members aged 40 years and older who have prediabetes, diabetes, and/or hypertension. The survey team will visit the household, schedule a visit for the interview, and ask household members aged 40 years and older to not eat or drink anything in the 12 h before the interview. On the day of the interview, blood pressure will be measured. If the systolic blood pressure is 140 mmHg or higher or the diastolic blood pressure is 90 mmHg or higher, a second measurement will be taken for confirmation and the average calculated. Then, fasting blood glucose will be measured. For individuals with a fasting blood glucose level of 5.6 mmol/L or higher, HbA1c will be measured for confirmation of prediabetes/diabetes. Fasting blood glucose and HbA1c measurements will be taken with point-of-care devices. The extensive interviewer-administered questionnaire (administered to all those identified as having prediabetes, diabetes, or hypertension in the screening module and the random subsample of 600 participants for the syndemics analysis) will cover (a) socio-demographic and economic characteristics such as marital status, education, and religion, (b) history of diabetes and hypertension and health-seeking behavior for each of the two conditions, (c) visits and services provided by RHMs, (d) health care utilization and related expenditure, (e) treatment adherence and knowledge on diabetes and hypertension, (f) behavioral cardiovascular disease risk factors such as tobacco and alcohol use, and physical activity, and (g) anxiety and depression. There will be the following additional body measurements: (a) height (using a tape measure against a wall) and weight (using a scale, which is zeroed prior to each day of study procedures) to calculate body mass index (BMI), (b) a waist circumference (using tape measures), and (c) a rapid HIV test. The household-level survey module will be administered to the head of the household, or, if not present, another adult household member with sufficient knowledge of their household characteristics, and cover (i) access to water and sanitation, (ii) household expenditure, (iii) ownership of assets and livestock, (iv) housing characteristics, and (v) non-health-related household expenditures. ## Power calculations The number of participants to be included in the household survey was determined through a power calculation. This power calculation accounts for clustering at the level of the unit of randomization (i.e., at the level of a healthcare facility with its assigned RHM service areas). Our power calculation assumes that the intra-cluster correlation coefficient does not exceed 0.04 for the blood pressure outcome and 0.08 for the diabetes outcome. These estimates are based on the intra-cluster correlation coefficient for primary sampling units from the 2012 South African National Health and Nutrition Examination Survey (SANHANES) [17], which is a nationally representative household survey that measured both blood pressure and HbA1c among adults aged 40 years and older. We expect this estimate to be conservative as we sample several areas for each cluster. Based on our sampling strategy for the household survey, we also assume a coefficient of variation of cluster sizes of 0.4. The minimum effect size that we want to be able to detect is an absolute difference in mean HbA1c of $0.8\%$ (primary diabetes endpoint) and an absolute difference in mean systolic blood pressure of 5.0 mmHg (primary hypertension endpoint). Lastly, we assume a mean HbA1c among adults (aged ≥ 40 years) with diabetes of $8.2\%$ with a standard deviation of $2.3\%$, and a mean systolic blood pressure among adults (aged ≥ 40 years) with hypertension of 153 mmHg with a standard deviation of 22.5 mmHg. These numbers were again taken from the 2012 SANHANES [17]. We aim for a minimum of $80\%$ statistical power. Under these assumptions, we require 224 participants per study arm for the diabetes endpoint, and 560 participants per study arm for the hypertension endpoint. These power calculations have been performed using the clustersampsi package in Stata version 15 [18]. ## Impact of WHO-PEN@Scale on population health To determine whether the WHO-PEN@Scale health service intervention is more effective than the standard of care, we will pool data from the two treatment arms and compare them to the standard of care. In a second step, we will compare data between the two treatment arms to determine which health service decentralization strategy is more effective as well as compare each treatment arm individually to the standard of care. We will estimate ordinary least squares regression models to compare mean HbA1c among adults with diabetes and mean systolic blood pressure among adults with hypertension between the study arms. All regression models will regress the outcome onto a binary indicator for the intervention as well as each stratum, and adjust standard errors for clustering at the level of the unit of randomization (the primary healthcare facility and its assigned RHM areas). In secondary analyses, we will also include participants’ socio-demographic characteristics as covariates, which is not expected to substantially affect the point estimates but may well reduce the variance. We will use a significance level of $p \leq 0.05$ for all analyses. Because there is disagreement in the research literature as to whether a multi-arm trial like WHO-PEN@Scale would require an adjustment for multiple hypothesis testing [19], we will use a p-value < 0.05 to indicate significance in these comparisons (i.e., we will not adjust for multiple hypothesis testing). We will also conduct subgroup analyses for males vs. females, 10-year age groups, education categories, household wealth quintiles, and rural vs. urban household residency. We are not planning to impute any missing data. ## Synergistic interactions between various diseases To assess any synergistic interactions between diabetes, hypertension, HIV, and other conditions [20] and to provide insight for the tailoring of diabetes and hypertension care for people with such comorbidities, we will conduct supplementary mixed-methods research, drawing from epidemiology, medical anthropology, and political science. First, based on data from the household survey, we will identify potential synergistic interactions. Subsequently, using qualitative research methods and qualitative comparative analysis, we will examine structural factors along the life course, such as gender, poverty, forced migration, stigma, and unequal social/economic relationships, to assess how they influence the resultant morbidity related to diabetes, hypertension, HIV, and other chronic conditions, and their co-occurrence.(i)Quantitative assessment of any syndemics The household survey will provide in-depth information on participants with prediabetes, diabetes, and/or hypertension. In addition, we will survey a random sample of household members with neither prediabetes, diabetes, nor hypertension, administering to them the same questionnaire as to the study sample. This strategy will yield representative data on the co-occurrence of diabetes, hypertension, HIV, and other pathologies, including depression, at the population level. The survey will also measure relevant structural factors at the individual and household levels, including socio-economic status. We will then use structural equation modelling (SEM) to obtain variance and co-variance estimates between the variables and latent constructs that they might describe [21]. Assessing a multitude of SEM models will provide insight into the optimal representation of the interplay between the variables, and as such identify the most appropriate framework that concurs with the obtained data.(ii)In-depth life-history interviews After characterizing the syndemic in terms of identifying any exacerbation of morbidity, i.e., emergent morbidity from co-occurrence of diabetes and HIV, we will conduct life histories with a subsample of respondents in this population group to explore the relevant structural factors [22]. A diversity-sampling approach will be taken to ensure that male and female respondents without diabetes and hypertension, living in rural and urban communities, over varied ages will be recruited. The total sample will also be guided by the point of theoretical saturation, whereby no more novel information is elicited [23]. Life-history interviews will examine contextual factors that are likely to interact with and compound the morbidity burden linked to NCDs, HIV, and other conditions. We will also examine factors that potentially contribute to the co-occurrence of these diseases. These will include violence, poverty, stigma, and unequal social/economic relationships. The interviews will encompass specific events and periods during the life course. With the consent of respondents, interviews will be audio-recorded, transcribed, and translated for qualitative content analysis.(iii)Qualitative comparative analysis Typically, qualitative comparative analysis (QCA) is used by scholars engaged in the qualitative and intensive study of macro social phenomena, particularly in sociology and political sciences [24]. It is, furthermore, a useful approach for the analysis of multiple in-depth case studies at any scale. This approach provides tools to untangle complex combinations of explanatory factors related to particular outcomes, when studying a relatively small number of cases (from around 5 to 50) and when traditional statistical methods are not appropriate. The approach also enables qualitative and quantitative data to be analyzed together using a systematic approach [25]. Applying QCA, analysts are able to identify more than one pathway that are potentially causal to an outcome, whether factors are potentially causal only if in conjunction with other conditions, and whether pathways that lead to an outcome differ from those that fail to achieve the outcome. We will use the participants recruited for the in-depth life-history interview as cases for QCA. Data for analysis will be drawn from the thematic qualitative analysis of these interviews and from the household survey. Under the syndemic framework, we hypothesize that a series of (social and health-related) conditions contribute to the co-occurrence of HIV and NCDs and/or exacerbated morbidity caused by co-occurrence and interaction of these conditions. We will therefore be able to combine qualitative and quantitative data to investigate conditions that are potentially causal for an outcome, for example, the presence of emergent (or excess) morbidity from the co-occurrence and interaction of HIV and diabetes, or conditions for co-occurrence. Such conditions might include violence, poverty, stigma, and unequal social/economic relationships. We will pay particular attention to the gendered impact of these factors. Using these conditions, a “truth table” will be constructed. This will help to identify the conditions that are necessary and/or sufficient for specific outcomes. The analysis will also include consideration for the temporal distribution of conditions across the life course [26]. ## Oversight and monitoring The Heidelberg Institute of Global Health study team is the coordinating center of the WHO-PEN@Scale study. It coordinates and oversees all research activities and ensures that they are in line with the study protocol. It presides the Project Management Committee (PMC), which is comprised of one representative of each of the nine consortium partners, and acts as the supervisory body of the study as well as the ultimate decision-making body of the consortium. The PMC meets on a monthly basis and ad hoc meetings are scheduled if required. The Scientific Steering Committee is* the advisory board and includes five members with relevant expertise including in research methods and/or cardiovascular disease care in low- and middle-income settings. Four of these members also comprise the Ethics Advisory Board, which is updated semi-annually. The Eswatini Ministry of Health coordinates the implementation of the study arms, which includes the recruitment of clients for the DSD models and CDPs. They ensure that the study is conducted according to the Eswatini Health and Human Research Review Board (EHHRRB). As explained below, the EHHRRB receives a progress report of the study and reviews whether the study complies with all ethical guidelines. During the design phases of the study arms, the Project Implementation Technical Team (PITT) was leveraged. The PITT comprises representatives of all government departments that are involved in the NCD health service decentralization, NGOs working in health service provision for chronic diseases, and all Eswatini-based consortium partners and met on a weekly basis. The PITT contributed considerably in forging the study design to bring a maximum benefit to the Eswatini population. Furthermore, the on-the-ground experience from all PITT members and their diverse backgrounds ensured that the study design was appropriate in the Eswatini context and the heterogeneity of needs and behaviors across population groups. The Clinton Health Access Initiative (CHAI) coordinates all data collection activities on the ground and reports to the PMC in a meeting on a monthly basis as well as to the Heidelberg Institute of Global Health in weekly meetings. ## Trial status and implementation timeline In 2019, the study arms were initially designed, standard operating procedures developed, and components of the treatment arms piloted and adapted. Training of the 1280 RHMs in both treatment arms started in April 2021, with the majority of trainings conducted between August and November 2021. Training of health care workers at the DSD clinics started in September 2021 and was concluded in January 2022. Once trained, nurses started organizing the clinic-based DSD models. The first models were launched in November 2021. While DSD models are extended on a rolling basis to meet the increased demand for diabetes and hypertension care, implementation was considered to have been completed once each DSD arm clinic offered at least one DSD model, which was achieved in April 2022. Healthcare workers in the CDP arm were trained between January and March 2022. Once the trainings were concluded, healthcare workers started to offer diabetes and hypertension services during CDP sessions. Thus, implementation for the CDP arm was considered to have been completed in March 2022. The household survey is planned to be conducted between October 2022 and January 2023. ## Dissemination The results will be communicated directly to the Eswatini Ministry of Health in the form of policy briefs, presentations, and extensive reports. They will furthermore be reported to the European Commission in an extensive report. The dissemination on the ground will be led by the CHAI, Diabetes Eswatini, and Eswatini Business Health and Wellness, which will hold stakeholder meetings to increase the reach of the results. Furthermore, all results will be published in peer-reviewed journals. Authorship will be defined according to the rules of the International Committee of Medical Journal Editors and, for non-medical journals, the publication agreement set up and signed by all involved institutions. ## Discussion The feasibility pilot conducted in the Lubombo region in 2016 showed that including primary healthcare facilities in the provision of healthcare for diabetes and hypertension improved population health [11]. The encouraging results led the Eswatini Ministry of Health to the conclusion that a health service decentralization to even lower levels, i.e., the community and household levels, can bring additional benefits. Although in the standard of care tasks have already been shifted from physicians in tertiary care facilities to nurses in primary care clinics, the two treatment arms include service provision models that have the ambition of being tailored to the needs of the population living with diabetes or hypertension. We expect that the involvement of RHMs in both treatment arms will create additional demand for diabetes and hypertension health services and reach individuals that do not regularly visit healthcare facilities. This study will contribute further evidence on the effectiveness of task-shifting and the inclusion of community health workers in healthcare provision for diabetes and hypertension. It will be crucial in informing health care provision for diabetes and hypertension in Eswatini and form the basis for a health system reform on a national scale. Furthermore, the findings may also prove valuable to other governments in sub-Saharan Africa when reforming their health care systems in order to meet the growing need for diabetes, hypertension, and other chronical disease care. The impact evaluation described in this protocol will be accompanied by several additional analyses, such as the acceptability, implementation, and cost-effectiveness studies. We believe that the complementarity and comprehensiveness of all these studies will yield highly relevant insights for the Eswatini government as well as the governments of other sub-Saharan African countries. We will not only evaluate if the implemented interventions are effective but also generate further insights as to why they do or do not work. 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--- title: Analysis of facial emotion expression in eating occasions using deep learning authors: - Elif Yildirim - Fatma Patlar Akbulut - Cagatay Catal journal: Multimedia Tools and Applications year: 2023 pmcid: PMC10031178 doi: 10.1007/s11042-023-15008-6 license: CC BY 4.0 --- # Analysis of facial emotion expression in eating occasions using deep learning ## Abstract Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of $77.68\%$ for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems. ## Introduction Emotions are complex psycho-physiological change arising from the interaction of an individual with biochemical [8] and environmental [32] influences. It is the main factor that determines the individual sense of health and plays a central role in the daily life of the person. There are numerous types of emotions that have an impact on how people live and are associated with other people [14]. Sometimes, people are dominated by these emotions. The choices they make, the activities they take, and the recognition they have are all impacted by the emotions they encounter at any given time [25]. Besides, emotions can be also affected by many things [20], such as daily activities, the places visited, and the people spent time together. The foods have a significant effect on the emotions as well [30]. Recent research findings suggest that the food that is consumed acts as a mood regulator [11] in both positive and negative ways. People have a complex relationship with food. When people are happy, sad, or feel other emotions, they choose food based on the emotion they feel. Food is present in almost every aspect of people’s lives and can affect their mood significantly. A clear understanding of customers’ emotions can provide various benefits to companies and organizations such as improving customer satisfaction, establishing the connection between the customer and brand, and consequently, commercial success. Moreover, understanding the affective behavior of humans can help product managers to better understand to identify the products with the highest impact. According to TripAdvisor’s survey [37], $94\%$ of diners choose their restaurants based on online reviews. The reason why review sites are taken as a reference is that there are many restaurants to choose from and the customer does not have the opportunity and time to experience all of them individually. In addition, it is important to get benefits from the best practices and experiences. Today, sites such as Yelp, TripAdvisor, Zomato, and OpenTable are global priority channels where restaurants and dining experiences are shared and evaluated. Not all comments are reliable on these platforms. Therefore, these platforms also include reviews from more trusted people, such as food bloggers. They seek different ways to reduce the bias in the evaluation of the eating experience. Today’s world benefits from automatic emotion recognition systems in different domains [6] such as patient care, medical diagnosis, education, video gaming, automotive assistance, recruiting personnel for companies, fraud detection for finance, etc.. For instance, emotion recognition systems can play a significant role in healthcare because the patient’s diagnosis and the recovery period can be understood and managed with higher accuracy [1]. In addition, it is utilized in tough follow-up processes of depression and anxiety patients [2]. Another effective utilization area is the evaluation of the student/lecturer interaction [31] during online education. Especially, during the Covid-19 pandemic, online education has increased dramatically all around the world, therefore, it needs to be supported with innovative approaches more than ever. The literature contains plenty of studies that aim to detect only emotions [3, 29] with similar approaches to this study. As valuable examples, Vatcharaphrueksadee et al. [ 39] and Jaiswal et al. [ 21] work with facial data and use CNN models to classify emotions. However, the aim of these studies is to develop a generic emotion recognition system that can be used in the above-mentioned domains. To the best of our knowledge, this is the first attempt to detect emotion during eating occasions. In literature, even if there are a lot of studies investigating the relationship between eating and emotions, most of them contain questionnaire-based survey data. The aim of this study is to detect emotions in real time. Despite the studies’ work on the same subject with continuous or real-time data being rare, few examples can be found in the state of the art. An example of this is the work of Carroll et al. [ 10], which performs emotion recognition by using ECG and EDA signals. In the study, the effects of emotional eating behavior in humans through physiological signals were investigated. To elicit emotional eating, successful sentimental stimulation must be experienced, which may include a person’s reaction to the meals. For this reason, we avoided triggering the emotion with an external method. We asked the individual to convey what they felt at the time of eating so that the precise causal effect of emotions on food intake could be determined. We examined certain emotions from the consumer perspective and a variety of models were put forward. In literature, studies are mostly clustered around the six basic emotions, namely happiness, fear, sadness, surprise, anger, and disgust, although they were expanded later on. Hence, we planned to analyze six basic emotions, however, three of these emotions, namely happy, sad, and disgusted and, neutral status were experienced during the experiments. Particularly, we aim to analyze how food affects emotions by examining facial expressions. To achieve this objective, an emotion recognition model was developed using state-of-the-art deep learning approaches. In our proposed approach, we developed a mobile application that users can capture videos of themselves while eating. We built a fine-tuned VGG16 model to recognize the emotional status of participants on eating occasions. We also use the DeepFace algorithm [36] that a nine-layer neural network with over 120 million connections and is able to capture demographic information such as the age, race, gender, and emotion of that person from the facial features to validate our prediction model in real-time. Our contributions are three-fold: We developed a system that recognizes emotions during eating occasions. We evaluated the performance of different deep learning-based algorithms for this problem. We demonstrated the effectiveness of the fine-tuned deep learning-based emotional status prediction system. This paper is structured as follows: Section 2 explains the related work. Section 3 describes the method used in this study. Section 4 presents the experimental results. Section 5 discusses the conclusion. ## Related work Motivation is one of the fundamental forces driving behavior. An person’s desire to eat is a biological source of motivation. According to the two-factor theory of emotion, [33], the experience of emotion is regulated by the strength of the experienced arousal, while the intensity of the desire to eat determined what the emotion and intensity will be. Various studies aimed to model this bond. Carroll et al. [ 10] aimed to discourage people from emotional eating habit. According to their study, people are more prone to emotional eating when they are distressed or upset. By detecting the negative emotions just before the eating with a wearable system, it is possible to help people to change their unhealthy eating patterns. In a similar study by King and Meiselman [23], it was mentioned how positive emotions are particularly effective on people with obesity problems. In the study, it was concluded that stress increases the level of eating, and positive emotions balance this level a little bit, even more, effective than a diet. Based on previous research, we can say that emotions and food have a direct relationship. Kenney and Adhikari’s study [22] revealed that emotion is the essential factor when it comes to conceptualizing the customer experience and predicting the demand of different customer profiles in the food industry. In another research, the results indicate that food flavors are powerful clues that activate memories and stimulate instinctive responses. This way, a considerable amount of the food servicing sector takes advantage of natural product flavors to tempt consumers. For instance, most donut chains [28], places its bakeries at the store entrance and are spread around with the seductive scent of product to entice clients. In addition, some cafeterias encourage clients to buy more by adding delightful flavors to their dining rooms. An experiment [34] in the deli revealed that after the introduction of coffee flavors into the setting, beverage sales increased $300\%$. Also, the research has revealed that aroma is an important factor affecting client fulfillment and attitude, in conformity with the restaurant atmosphere model. Frayn et al. [ 15] reported that female students having sentimental feeding accrued foods consumption that contain added sugar following a negative state of mind induction, though no increase was reported when salty foods are consumed. In another article by van Strien et al. [ 38], the difference between two female groups, which are highly emotional eaters and low emotional eaters, had been investigated. It was found that the group that consisted of low emotional eaters ate a similar amount of food after joy and sadness, while the high emotional eaters’ group ate more food when they were unhappy than when they were happy. The foods that affect emotions, mood, and the social environment have also an effect on choosing the type and the amount of food consumed. There are situations where roles change during the relationship between eating and emotions. Just like the foods that affect emotions, mood and the social environment have also an effect on choosing the type and the amount of food consumed. For instance, a person can eat a dessert when he feels unhappy, or he can enjoy the meal more when he is happy. Studies have shown that the social environment increases eating. It can be said that the rate of eating at a wedding or a school cafeteria is high. The reason is that a joyful environment such as a wedding or a meal eaten by chatting with friends is more relaxing and creates a desire to eat more because it increases the happiness hormone. Eating tempting meals while in a positive mood can stimulate appetite and people can eat more [13]. Moreover, in a study of people having eating disorders (i.e., bulimia nervosa) [9], people with this ailment watched positive and neutral video clips, respectively, to determine whether people can have a positive mood. Thanks to positive video clips, it has been observed that people with these two different ailments have more regular levels of eating. We observed that most of the studies conducted to reveal the relationship between emotions and food use observation and survey techniques. As shown on Table 1, Ouyang et al. [ 28], Frayn et al. [ 15], van Strien et al. [ 38], Evers et al. [ 13], Cardi et al. [ 9], Altheimer et al. [ 4], and Herren et al. [ 19] used survey data. The highlight of our study is that the resultant data is supported by a piece of physical evidence that is taken instantly by the mobile application. Table 1Comparative table between the related worksStudyThe goalTechniques usedDataset employedSample size(N)Evaluation measuresCarroll et al. [ 10]To prevent emotional eatingNormalization, feature extraction, Gaussian Process RegressionEKG and EDA signals12Valence= $72.62\%$, Arousal= $75.00\%$Ouyang et al. [ 28]food aromas and consumer emotions relationshipLogistic RegressionSurvey196B(Wald)= 0.386, Exp(B)= 1.472, p-value= 0.137Frayn et al. [ 15]To find the food addiction after sad mood inductionEye Tracking with EyeLink sensorSurvey and fixation data66Emotional Eatingvan Strien et al. [ 38]emotional eating and food intake relationship in sadness and joyVR–MIP, ANOVASurvey60Dutch Eating Behavior QuestionnaireEvers et al. [ 13]observing positive emotions as a trigger for food intakeANOVASurveyS1: 68, S2: 84F[1, 37]= 60.40, $p \leq 0.001$, pn(eta)ˆ2 = 0.62Cardi et al. [ 9]observing positive emotions to reduce the overeating behaviourANOVASurvey30p = 0.002Vatc. et al. [ 39]Facial emotion recognitionCNNCK+, FER2013123, $12365\%$Jaiswal et al. [ 21]Facial emotion recognitionCNNFERC2013, JAFFE123, 10FERC2013 = $70.14\%$, JAFFE= $98.65\%$Altheimer et al. [ 4]relation of sadness and anxiety to emotional eatingBivariate correlation, LMERSurveyS1: 118, S2: 111DEQ, PSS, DSIHerren et al. [ 19]To find if ES is associated with BMI through EE, and the role of physical activities, ethnicity and genderLinear RegressionSurvey1674B = 0.0017, CI $95\%$ [0.0001, 0.0042]Our studyTo detect emotions while eatingCNN, Fine tuned VGG-16FER-2013, CK+, Tailored Dataset123, 123, $1377.68\%$ Even if people are largely aware of the emotions they are experiencing, they may mislabel the source of the arousal or wish to deceive the system. One of the ways to overcome this is to understand emotions from non-verbal behaviors and express them to others in this way. This is where the role of understanding emotions by analyzing facial expressions or physiological signals comes into play like Carroll et al. ’s [10] study. They use physiological signals (ECG and EDA) in their study, and the accuracies they get from their model are $72\%$ and $75\%$ which is close to the accuracy we get from our models, but it is hard to compare the two works because the type of data used is different from each other. Vatcharaphrueksadee et al. [ 39] developed a VGG-16-based model with CK+ 48 and FER2013 datasets and reached $65\%$ accuracy. We also train models with the same approach and get an accuracy of $77\%$ from fine-tuned VGG-16 model. Jaiswal et al. [ 21] get $70\%$ accuracy from their CNN model, using the FERC2013 dataset, which is a blurry version of FER2013 data. The conclusion from most studies is that the idea that certain foods can have habit-forming effects has acquired a lot of awareness over the last years. Many studies showed that foods rich in sugar and fat affect the reward systems in the brain. Studies [16] established that both fat and sugar consumption releases the dopamine and provides delightful effects. Many people turn to food to reduce negative feeling, and a few analysis has supported this linkage. ## Method We conducted a user study to capture the emotional changes while eating through facial expressions and used videos taken during meals to analyze people’s emotions. We aimed for individuals to record their own emotions in order not to experience emotional changes caused by third parties around them. In this context, a mobile application was developed and a system was introduced for participants to record themselves while eating. This way users can instantly forget that they are in an experiment and perform their eating activities. The following subsections provide more information about the method applied in this research. ## Development of a mobile application to capture emotions In order to collect emotion while eating, we developed a mobile application (Fig. 1) with React Native [27] to be compatible with both Android and iOS mobile operating systems. React Native is a framework developed by Facebook and allows the development of applications in a cross-platform manner based on React JavaScript. It has a proprietary language format called JSX. Fig. 1Sample screenshots of the mobile application to collect emotion during eating a) Landing screen that explains the instructions during the usage, b) Video is taken with the whole face visible during eating, and c) After having an idea about the food eaten, emotion is selected from the list The emotion collection method to be used in this study was selected as a result of an analysis of widely used methods in the literature. The most common methods are the Self-Assessment Manikin (SAM) [7], Expressing Emotions and Experiences (3E) [35], and Emocards [12], which are used to collect emotions before, during, or after the use of the tested system. However, as emotions are usually very short instants [5], trying to remember them even just after the action is not a practical application for individuals. As such, we have developed an application to be able to capture expressions of emotions instantly as they are experienced, it can be considered as an experience sampling method [24]. Common methods such as the SAM represent what has been deliberated for appraising the emotions awakened by static features, mostly physical characteristics or look-and-feel. Thus, it is assumed that the emotion of the individual does not change expeditiously. There can be shifts progressively, however, those are not remarkably fast. Nevertheless, when the priority is on assessing the emotional occasions during eating triggered by active interaction such as food with a mobile application, it is unavoidable that the sentiments can change rapidly even between extremes. Hence, collecting instant information about emotions during app use becomes critical not only for reducing the time lapse between emotional experiences but also for capturing very short instant emotions, which are followed by new emotional experiences during an interaction. This approach merges the mobile application and the emotion gathering tool in the alike system. The device is utilized for the self-assessment of emotion during eating. There are six basic emotion options that are offered to participants, namely neutral, happy, sad, disgusted, scared, and angry. There are no time and usage restrictions. During the eating activity, it is preferred to use small buttons in a mobile context by selecting an expression status on the display. Parallel to the method in the literature like SAM and Emocards, this approach also pushes the person to choose the best probable option from a set of choices. The proposed approach also concerns the problem of selecting the most proper emotion, but now the preference is accomplished rather often and usually just after an interaction has occurred, which should effectively diminish the necessity for establishing multiple expressions at once. ## Proposed deep emotion recognition model Since we intend to use computerized emotion recognition approaches, we are concerned with the precision of measurements of facial features to get emotions. Because emotions can be specified by facial expressions, it is reasonable to match a collection of action units (AUs) to emotions. Most individuals cannot arbitrarily restrain their emotional expressions. Extreme situations make detecting emotions more difficult and can be managed by examining muscle activity over time rather than just pictures. Within the scope of the study, we developed a framework to transform instant facial expressions into emotions. Figure 2 presents the outline of the developed prototypical implementation. The model follows a funnel logic to lower video frames to emotions and is divided into five main phases. Users collect facial data in video format using the mobile application’s camera. Later with phase 1, the process of detecting faces and removing facial features from video frames comes into play. Each detected face is determined and stored in the relevant frame, and then, the second stage processes begin. In the second phase, the data points are passed through an elimination process. In the beginning, each data points contain 20 frames that represent the same emotion, which is later reduced to 5 frames. The aim here is to select the most appropriate sequential frames to create sequential images. After detecting related frames, facial regions are detected by MTCNN [40] for all of the training and test samples. Then, five critical facial points such as the eyes, nose, and the corners of the mouth are used to perform the resemblance transformation. We obtain the cropped faces and resize them to be 48x48 pixels. Each pixel at [0, 255] in images is normalized. This input was used as phase 3 with a conventional CNN architecture. After the conventional CNN implementation was not successful, VGG16 was added and tuned (phase 4) and the results were calculated (phase 5). To perform classification we calculate the probability of each emotion with deep learning algorithms. The proposed emotion identification architecture extracts facial attributes that are related to various emotions and applies them as an additional modality to detect emotions. The architecture is built using two consecutive cascaded networks, which are depicted in Fig. 3. Fig. 2Implementation process of emotion recognition systemFig. 3Architectural view of CNN model to predict four emotions The first network uses the VGG16 architecture with matching filter size, pooling, and convolutional layers. In the first layer, we applied 3×3 filters with a convolution stride of 1. Following the convolution operation, the main motivation was to keep spatial resolution, therefore the padding was fixed to 1 pixel. Max pooling was done on a 2 × 2 pixel window (i.e., stride is 2) and performed after each convolutional layer. One global average pooling that takes the average of each feature map obtained from the last convolutional layer was placed after the eleventh convolutional layer. As an activation function, we used Rectified Linear Units (ReLU) in each layer. We used Rectified Linear Units (ReLU) not only as an activation function in each hidden layer of the network but also as the prediction function at the last layer. In the study, we trained our model with public datasets such as FER-2013 [18] and CK+ [26] datasets and verified results using our collected samples via mobile application. ## Experimental results In this section, we discuss the results of our CNN-based model, which is developed for emotion recognition from facial expressions. We analyzed and compared the effectiveness of a couple of deep learning methods. We chose our training set from public databases such as FER-2013 and CK+ and our test set was assembled with face images constructed via our mobile application during eating activity. The developed system is designed to work in real-time, therefore, the most accurate output is aimed. Participants were seated in a standard home environment and initiated a video session via mobile application. What they need to do in this process is just to taste or see the food. The main task is to rate each participant’s feelings either tasting meals or seeing them and reporting via the mobile application. The camera of the mobile phone was used to capture video in real-time that contains various facial expressions corresponding to four emotional categories. The emotion captured was instantly reflected on the display. The association between listening to music and watching video content while eating with food consumption in the natural environment was assessed using 13 participants between the ages of 18 and 25. It was reported that four different emotions were felt during the experiment, they are neutral, happy, sad, and disgusted. We recorded while they ate their meal with three case studies such as alone eating, eating while listening to music, and watching audiovisual content. Table 2 demonstrates the results of our CNN-based model on the gathered validation set. It can be noticed that fine-tuned architecture performs quite well in neutral and happy cases. However, the performance on sad and disgusted is average, mostly due to relatively fewer training samples. Table 2Average accuracy for each emotionMethodNeutralHappySadDisgustedOverall accuracyVGG$1679.36\%$$76.91\%$$64.34\%$$68.17\%$$72.20\%$Fine tuned VGG$1684.14\%$$88.38\%$$68.67\%$$69.51\%$$77.68\%$Deepface$81.53\%$$90.63\%$$62.13\%$$59.89\%$$73.54\%$ The gathered data show that the presence of music is associated with higher food intake. Within-participant comparisons exposed higher food intake and more extended meal periods while listening to music but no significant differences in music speed or volume. The existence of music seems to be not an effective method of changing the positive or negative emotional states that influences food intake. The sample view is shown in Fig. 4. Fig. 4Sample facial expression during eating: a) the dominant emotion of the first image is ‘neutral’ with accuracy of $92.64\%$ b) the second image displays ‘neutral’ emotion with accuracy of $63.69\%$, and c) third image exhibits the ‘happy’ emotion with accuracy of $92.71\%$ In Fig. 4a, the participant’s mood was neutral while eating alone without any environmental effect. In Fig. 4b, the participant started to listen to music, but his dominant emotion was still neutral. In Fig. 4c, his dominant emotion changed to ‘happy’ after he started to watch a video. ## The hypothesis Our proposed system hypothesized the following statements; Food reviews are better conducted with emotion detection using face recognition via mobile app. Emotion recognition accuracy is higher in food review made in a musical environment than in a non-musical environment. The employment of pretrained models with conventional methods brings higher performance in emotion recognition from facial expression. In line with the hypothesis (i), we observed the data provided by automatic emotion recognition systems eliminates the user’s deception factor. Detecting the tendency of users to choose inaccurate answers in paper-based surveys is often ignored as a difficult problem. The absence of this situation in the proposed system enables it to be used as a more effective approach. We also found that the classification accuracy of reviews made in music playing environments was on average $3.54\%$ higher as hypothesis (ii) association. Especially happy as the most affected emotion with $14.6\%$ shows the effect of musical environments on the selection of positive emotions. These results showed that emotions were experienced more intensely in musical environments. Our experiments support the hypothesis (iii) as, the results in Table 2 show the effectiveness of pretrained models, as successful results cannot be obtained with the conventional CNN approach. ## Threats to validity Since the proposed system is affected by various factors, we identified the possible threats in the perspective of internal and external validity. As the first threat of internal validity, participant selection bias affects the results. It is known that obese people experience higher positive emotions when eating than average people [17]. For this reason, body mass index (BMI) should be considered as an important indicator in the selection of participants. Maturation should be considered as the second factor. Eating assessments are not legitimate for older people, mostly due to progressive age-related health problems. As the external validity perspective we identified situational factors such as facial expressions cannot be extracted from the video taken due to the use of the mobile application in a very dark or very bright environment. Similarly, the face must be seen from the front during video shooting. If the participant uploads a video shot from the side that only shows part of his face, the system will not be successful. If there is more than one face during the review, an undesirable situation occurs that negatively affects the emotion analysis. Finally, there may be users who cannot get the use of the mobile app. There are instructions on the opening interface for this, and the development was conducted by considering the human–computer interaction (HCI) and user experience (UX) design principles. ## Conclusion The relationship between emotion and eating is a topic that has been being studied for a long time and its origin is based on the obesity literature. Therefore, previous studies have mostly focused on explaining eating in obese individuals, however, new theories aim to explain the eating behavior of people of normal weight. Thus, our research focus is superintended on the emotional dimension of eating occasions. Emotions vary in duration and frequency of sensation, trigger patterns and locations, and physiological correlates. Relationships between a certain emotion and eating behavior should be stronger if it occurs more frequently than other emotions during eating. While we were aiming for a method to recognize basic emotions such as happiness, disgust, sadness, and others, studies show that there is no system that has achieved a perfect success rate yet. The reason is that the face can express many emotions at the same time. The systems used also make some assumptions based on facial expressions and gestures, therefore, the possibility of making mistakes increases in this emotional intensity. During our empirical experiments, we have employed various generic deep learning algorithms as well as pre-trained models such as VGG16. We observed that fine-tuned VGG16 performed quite well in neutral and happy cases. This model achieved an overall accuracy of $77.68\%$, while Deepface resulted with $73.54\%$ and traditional VGG16 was $72.20\%$. We concluded that supportive studies are needed to recognize disgust and sad emotions for all deep models. We plan to add new functionalities and evaluate other deep learning-based models to improve the overall accuracy. We manually asked the users to feedback on the emotions they felt at the moment, which was used to validate the predictions of the system. This study can be used in all research on the relationship between eating and emotion. For example, in understanding the feelings of eating disorders and depression. Similarly, it can pave the way for applications that disable the human factor in food evaluations. 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--- title: Homocysteine exchange across skeletal muscle in patients with chronic kidney disease authors: - Giacomo Garibotto - Daniela Picciotto - Daniela Verzola - Alessando Valli - Antonella Sofia - Francesca Costigliolo - Michela Saio - Francesca Viazzi - Pasquale Esposito journal: Physiological Reports year: 2023 pmcid: PMC10031238 doi: 10.14814/phy2.15573 license: CC BY 4.0 --- # Homocysteine exchange across skeletal muscle in patients with chronic kidney disease ## Abstract Sites and mechanisms regulating the supply of homocysteine (Hcy) to the circulation are unexplored in humans. We studied the exchange of Hcy across the forearm in CKD patients ($$n = 17$$, eGFR 20 ± 2 ml/min), in hemodialysis (HD)‐treated patients ($$n = 14$$) and controls ($$n = 9$$). Arterial Hcy was ~ 2.5 folds increased in CKD and HD patients ($p \leq 0.05$–0.03 vs. controls). Both in controls and in patients Hcy levels in the deep forearm vein were consistently greater (+~$7\%$, $p \leq 0.05$–0.01) than the corresponding arterial levels, indicating the occurrence of Hcy release from muscle. The release of Hcy from the forearm was similar among groups. In all groups arterial Hcy varied with its release from muscle ($p \leq 0.03$–0.02), suggesting that muscle plays an important role on plasma Hcy levels. Forearm Hcy release was inversely related to folate plasma level in all study groups but neither to vitamin B12 and IL‐6 levels nor to muscle protein net balance. These data indicate that the release of Hcy from peripheral tissue metabolism plays a major role in influencing its Hcy plasma levels in humans and patients with CKD, and that folate is a major determinant of Hcy release. Sites and mechanisms underlying the supply of Homocysteine (Hcy) to the circulation are unexplored in humans. Methionine synthase is expressed, although at intermediate level, in skeletal muscle, which, however, represents a large part (about $40\%$) of body weight. At variance with liver, pancreas and kidney, that express the entire methionine cycle, the transsulfuration pathway is virtually absent in muscle; in addition, even if MTHFR is present in muscle, the methionine remethylation pathway is limited by lack of BHMT. We studied the exchange of Hcy across the forearm (which is mainly composed of muscle) in CKD patients, in hemodialysis (HD)‐treated patients and controls. Arterial Hcy was 2.5 folds increased in CKD and HD patients. Both in controls and in patients Hcy levels in the deep forearm vein were consistently greater (+$7\%$, $p \leq 0.05$–0.01) than the corresponding arterial level, indicating the occurrence of Hcy release from muscle. The release of Hcy from the forearm was similar among groups (−1.8 ± 0.5, −2.1 ± 0.8 and −1.7 ± 0.5 nmol/min). In all groups arterial Hcy varied with its release from muscle, suggesting that muscle plays an important role on plasma Hcy levels. Forearm Hcy release was inversely related to folate level in all study groups. These data indicate that the release of Hcy from peripheral tissue metabolism plays a major role in influencing its plasma levels in humans and patients with CKD, and that folate is a major determinant of Hcy release. ## INTRODUCTION An increase in plasma homocysteine (Hcy), a putatively atherothrombotic sulfur amino acid, is very common in patients with CKD and occurs almost in $85\%$ of patients with End‐Stage Renal Disease (ESRD) undergoing maintenance hemodialysis (HD; Bostom & Culleton, 1999). Hcy, which is found mainly intracellularly, supports a number of physiological processes, including purine and thymidine biosynthesis, amino acid homeostasis, epigenetic maintenance, and redox defense (Ducker & Rabinowitz, 2017). Tissue Hcy levels reflect a balance between its synthesis from methionine via S‐adenosyl‐l‐methionine (SAM)‐dependent methylation reactions, its remethylation to methionine and its catabolism through the transmethylation and transsulfuration pathways (Figure 1). Methionine synthase (MS) and betaine‐homocysteine methyltransferase (BHMT), a zinc metalloenzyme that catalyzes the transfer of methyl groups from betaine to Hcy to produce dimethylglycine and methionine, are the major enzymes involved in the remethylation pathway. FIGURE 1: Pathways of methionine metabolism in skeletal muscle. Methionine synthase (MS) is expressed, although at intermediate level, in skeletal muscle. MS and betaine‐homocysteine methyltransferase (BHMT), a zinc metalloenzyme that catalyzes the transfer of methyl groups from betaine to homocysteine (Hcy) to produce dimethylglycine and methionine, are the major enzymes involved in the remethylation pathway. At variance with liver, pancreas and kidney, the transsulfuration pathway is virtually absent in muscle; in addition, the remethylation pathway is limited by lack of BHMT expression. Early studies have shown that patients with CKD have markedly reduced clearance of Hcy from plasma (Guttmorsen et al., 1997). Steady‐state isotopic studies have shown that the flows through the transsulfuration pathway and the remethylation pathway are impaired in patients with CKD (Stam et al., 2003; van Guldener et al., 1999). However, these dynamic whole‐body studies do not reveal the tissue specificity or function of the underlying reactions, and sites and mechanisms which regulate circulating Hcy both in the normal condition and disease are still not completely understood. On a theoretical ground, an increase in plasma Hcy may occur following an increase in its production rate (i.e., transmethylation), a decreased rate of its removal (by transsulfuration or remethylation), or decreased elimination from body fluids. 1‐C pathway enzymes are not uniformly distributed in all tissues. Tissues where transsulfuration or remethylation are restricted may export Hcy for further metabolism by other tissues expressing the entire 1‐C cellular machinery. Early studies have focused 1‐C metabolism in liver, pancreas and kidney (Finkelstein et al., 1971; Mato et al., 2002). In vivo studies in rodents have shown that even if the fraction of intracellular methionine derived from the methylation of Hcy is highest in liver, most Hcy is retained in liver cells (Fiona et al., 2009). In contrast, the pancreas exports to plasma both methionine and Hcy, and this matches matching the contribution from liver (Fiona et al., 2009). In recent years the importance of methionine cycle in skeletal muscle, in particular on inducing differential DNA methylation, has been reappraised both in physiology (Terruzzi et al., 2011) and disease (Van Dyck et al., 2022). Methionine synthase is expressed, although at intermediate level, in skeletal muscle, which, however, represents a large part (about $40\%$) of body weight. At variance with liver, pancreas and kidney, that express the entire methionine cycle, the transsulfuration pathway is virtually absent in muscle (Figure 1) (Chen et al., 1997; Veeranki & Tyagi, 2015). In addition, the remethylation pathway is mainly dependent on methylenetetrahydrofolate reductase (MTHFR) because of limited expression of both BHMT (Sunden et al., 1997) and the more recently discovered BHMT2 (Chadwick et al., 2000). In the postabsorptive state and during fasting, skeletal muscle cells meet with a sizable amount of methionine deriving from net protein degradation (Lundholm et al., 1987). Previously, we observed that human leg tissues, which are mainly composed of skeletal muscle, release Hcy in the circulation, while splanchnic organs (which mainly represent liver and gut exchanges) show, as a trend, a positive Hcy balance (Garibotto et al., 2003), a finding which suggests a compartmentalization of 1‐C metabolic reactions to maintain plasma Hcy levels (Garibotto et al., 2003). However, the tissue sources of plasma Hcy in humans have not been identified; in addition, if hyperhomocysteinemia in CKD may in part be due to increased entry into the blood compartment has not been studied yet. The renewed interest in Hcy as a player in the methylation processes in aging and cachexia (Bauchart‐Thevret et al., 2009; Ducker & Rabinowitz, 2017; Shahal et al., 2022; Terruzzi et al., 2011; Verbruggen et al., 2009) prompted us to review the results of studies on muscle amino acid kinetics performed from our laboratory (Garibotto et al., 2006, 2015). While the results on protein metabolism have been previously published (Garibotto et al., 2006, 2015), data on Hcy exchange are still unpublished. The aim of this study was to evaluate the role of skeletal muscle on the Hcy export to the circulation in CKD patients. In this work we calculated the exchange of Hcy across the forearm, which is mainly composed of muscle, in renal patients and control subjects in from forearm perfusion studies in which Hcy measurements together with forearm blood flow, net protein balance, folate and vitamin B12 data were available (Garibotto et al., 2006, 2015). As a second step, we studied the individual role of muscle net protein balance (as a source of intracellular methionine), interleukin‐6 (IL‐6) (as a marker of inflammation), folate and vitamin B 12 status, on Hcy muscle handling. Taken together our results indicate that skeletal muscle plays a role greater than previously supposed in Hcy production and export to other tissues in fasting conditions, and that Hcy release from muscle is strictly dependent on folate availability. ## MATERIAL AND METHODS Arterial and forearm deep vein plasma Hcy measurements were available in 17 patients with CKD 4–5 (age 53 ± 6 years, eGFR 20 ± 2 ml/min, 14 M/3F, BMI 25 ± 2), 14 patients with CKD5d on maintenance thrice‐weekly hemodialysis (HD) schedule (age 64 ± 4 years, 11 M/3F, BMI 24 ± 1, Kt/V 1.3 ± 0.10) and 9 control subjects (age 45 ± 4 years, BMI 23 ± 4, 6 M/3F; Garibotto et al., 2006, 2015). Six CKD patients and 7 HD patients displayed clinical signs of cardiovascular disease and increased CRP levels (>5 mg/L). Average protein intake was 0.9 ± 0.1 and 1.1 ± 0.1 g/kg, respectively, in CKD and HD patients; calorie intake was 30–32 and 28–31 kcal/kg, respectively, in each group, as estimated by nutritional interviews. BMI, fat free and fat mass (anthropometric measures) were not different in patients and controls (Garibotto et al., 2006, 2015). Both CKD and HD patients received phosphate binders, sodium bicarbonate, vitamin D supplements as clinically needed. In addition they received vitamin B6 (pyridoxine hydrochloride) supplements (50 mg every other day). Control subjects were on a diet providing 30–32 kcal and 0.9–1.2 grams of protein/Kg/day, as assessed by dietary histories and urea excretion. Routine laboratory tests, acid–base, and electrolyte measurements were all normal. The study was part of a larger protocol on amino acid and protein metabolism in CKD approved by the Ethical Committee of the Department of Internal Medicine of the University of Genoa (Ref. N.62006; Garibotto et al., 2006, 2015). All subjects were informed about the nature, purposes, procedures, and possible risks of the study before their informed consent was obtained. The procedures were in accordance with the Helsinki declaration. ## Protocol All studies were performed in the overnight, basal postabsorptive state. HD patients were studied after approximately 72 to 74 h from the last dialytic treatment. Briefly, a brachial artery and an ipsilateral retrograde cubital vein catheter were placed percutaneously. Triplicate sets of arterial and venous samples were taken at 20‐min intervals during a 60‐min period. Forearm blood flow was measured after each sample set. ## Analytic procedures Since phenylalanine is not metabolized in muscle, the measure of the net phenylalanine balance across the forearm represents the difference between protein synthesis and degradation. In the postabsorptive condition, as evaluated in the present study, the net balance of phenylalanine across the forearm (as an expression of net protein balance) is negative because protein degradation is greater than protein synthesis. During the study, blood samples were collected in heparinized syringes and immediately kept in ice until delivery to the laboratory. At the lab, blood samples were transferred into cooled ethylendiaminetetraacetate (EDTA) tubes, which were immediately centrifuged at 6000 rpm for 10 min at +4°C. Plasma was quickly separated from blood cells and stored at −80°C until assayed. Total Hcy concentrations were determined in triplicate in plasma samples by HPLC (Chadwick et al., 2000) within 1 year from sampling. Blood phenylalanine was determined by an amino acid analyzer. All other serum chemical measurements were determined by routine clinical chemistry laboratory procedures: folate and vitamin B12 were determined by competitive protein binding techniques. Total folate concentrations determined by protein binding are approximately $9\%$ lower than results obtained with LC/MS/MS. ## Calculations All the results are corrected × 100 ml forearm volume. Forearm volume was measured in a large plastic cylinder by water displacement from the tip of the arterial catheter to the upper edge of the wrist cuff. Blood flow was expressed as ml/min.100 ml forearm volume (Garibotto et al., 2006, 2015). The exchange of Hcy and phenylalanine across the forearm was calculated by Fick's principle:((Xa)‐(Xv))x plasma flow, where Xa and Xv are the concentrations of Hcy in arterial and venous plasma, respectively. The results are expressed as nmol/min.100 ml forearm. ## Statistical analyses All data are presented as the mean ± SEM. Statistical analysis was performed using the two‐tailed t test to compare arterial with venous data. Linear regression and correlation were used to evaluate the relationships between muscle Hcy exchange and plasma folate, vitamin B12, IL‐6 and net muscle protein balance. Among cytokines, we focused on IL‐6 because it is secreted by skeletal muscle and may act locally (Garibotto et al., 2006). Non‐Gaussian distributed variables were Log transformed before analysis. Statistical analysis was performed with the GraphPad Prism Statistical Package (San Diego). ## RESULTS No significant differences among study groups were observed for mean plasma folate (13 ± 3, 11 ± 2 and 9 ± 2 ng/mL in CKD, HD and control subjects, respectively, p = NS). Also mean vitamin B12 levels (908 ± 37, 963 ± 58 and 800 ± 67 pg/mL in CKD, HD and control subjects, respectively, p = NS) were similar among groups. Average Hcy levels, as well as their exchange rates across the forearm, are reported in Figure 2. Arterial Hcy levels were within the normal range (Bostom & Culleton, 1999) in controls (average 9 ± 2.5 nmoL/mL) and were ~ 2.5 folds increased in CKD and HD patients (23 ± 1.9 and 26 ± 3.3 nmoL/mL, respectively, $p \leq 0.05$–0.03). Both in controls and in patients Hcy levels in the deep forearm vein were consistently greater (+~$7\%$, $p \leq 0.05$–0.01) than the corresponding arterial level, indicating the occurrence of Hcy release from muscle. The release of Hcy from the forearm was similar among groups (−1.8 ± 0.5, −2.1 ± 0.8 and −1.7 ± 0.5 nmol/min.100 ml forearm in CKD, HD patients and controls, respectively; Figure 2). FIGURE 2: Homocysteine (Hcy) arterial levels and Hcy exchange across the forearm in CKD (n = 17), HD (n = 14) and control (n = 9) subjects. Statistically significant from controls: p < 0.05–0.03.* To study the determinants of muscle Hcy metabolism we examined the relationships between muscle Hcy exchange and plasma folate, vitamin B12, IL‐6 and net muscle protein balance (Table 1). Muscle release of Hcy was inversely related to folate level in controls and both in CKD and HD patients (Table 1, Figure 3). The slopes were nonstatistically different ($$p \leq 0.98$$). Visually, the intercepts with the x‐axis (folate levels) were higher for CKD and HD patients vs. controls, suggesting resistance to folate in renal patients. However, the values were nonstatistically different ($$p \leq 0.19$$). No associations between vitamin B12 levels and forearm Hcy release was observed for both patients and controls (Table 1, Figure 4). No relationship was also observed between Il‐6 levels and the forearm release of Hcy neither when CKD and HD patients were analyzed individually ($r = 0.03$ and $r = 0.02$ for CKD and HD patients, respectively) nor when data were pooled ($r = 0.06$, p = NS). Similarly, there was no relationship between forearm muscle protein net balance and Hcy release ($r = 0.11$ and $r = 0.10$, for CKD and HD patients, respectively, p = NS). FIGURE 4: Relationship between the forearm exchange of homocysteine (Hcy) and plasma vitamin B12 levels in CKD (n = 15), HD (n = 10), and control (n = 9) subjects. In control subjects arterial Hcy was related to its release from muscle (r = −0.81, $p \leq 0.02$; Figure 5). Also, Arterial Hcy was also related to the release of the same amino acid both in CKD and HD patients (r = −0.52; $p \leq 0.03$ and r = −0.71; $p \leq 0.02$, respectively), suggesting that release from peripheral tissues plays a major role on plasma Hcy levels. The slopes were not significantly different from controls. The intercept of the x‐axis of pooled data was borderline ($p \leq 0.075$) statistically significant from controls. FIGURE 5: Relationships between arterial homocysteine (Hcy) levels and Hcy forearm exchange in CKD (n = 17), HD (n = 14), and control (n = 9) subjects. ## DISCUSSION There are several observations indicating that sulfur amino acid levels are altered in the uremic muscle, suggesting abnormal methionine metabolism. As shown by early studies on intracellular amino acid concentration in uremic patients, methionine is normal, while cysteine is increased and taurine is present in low concentration (Bergstrom et al., 1990; Lindholm et al., 1989). The current study used the forearm kinetics of Hcy to better understand the role of skeletal muscle in Hcy metabolism and to unravel some of the interactions between muscle Hcy exchange and its potential modulators, such as net protein balance (as a source of methionine), IL‐6 and folate/B12 circulating levels. Three major observations are made from this study. First, in accordance with our previous observation in healthy subjects (Garibotto et al., 2003), in patients with CKD the skeletal muscle releases Hcy in the circulation, a finding, which is in keeping with limited remethylation in human muscle. Second, as a new finding, our data suggest that both in patients with CKD, as well as in the normal condition, the Hcy export from peripheral tissues is an important contributor to Hcy circulating levels; of note, in CKD, the Hcy release from muscle is quantitatively similar to the normal condition. Third, the Hcy export output from muscle is highly variable according to folate plasma levels. In patients with CKD, blood Hcy levels are inversely related to GFR over wide range of kidney function, which suggests that kidney uptake and/or metabolism plays a major role in Hcy removal (Bostom & Culleton, 1999). As a matter of fact, a decreased whole body transmethylation rate is observed in patients even in patients with moderate degree CKD (Tessari et al., 2005). In addition, decreased whole body remethylation rates have been also observed in CKD5, HD‐treated patients (van Guldener et al., 1999). Furthermore, the human kidney plays a major role on the removal of S‐adenosylhomocysteine (SAH), a potent inhibitor of methylation reactions, which likely accounts for increased SAH levels in CKD (Garibotto et al., 2009). However, Hcy increase in blood can also be promoted by factors that are often observed in CKD, such as deficiencies of folate and vitamin B12 and/or genetic variants of several enzymes of folate and one‐carbon pool metabolism (Lee et al., 1999; Perna & Ingrosso, 2019). Our study shows that the release of Hcy from skeletal muscle to the blood compartment is not increased in CKD; however, muscle is extremely sensitive to low folate levels, with large increases in Hcy export observed in patients with subnormal plasma folate. These findings are in accordance with the strong influence of folate on MS in muscle cells (Chen et al., 1997). Although nonstatistically significant, in CKD, lack of Hcy release from muscle is observed at higher‐than‐normal plasma folate levels, suggesting the occurrence of altered Hcy inflow/outflow from muscle or CKD‐related resistance of the folate‐dependent muscle Hcy remethylation pathway. However, since Hcy muscle inflow is high (Fiona et al., 2009), a defective Hcy transport in muscle in uremia cannot be ruled out from our study. In most normal subjects, a folate dose of 400 to 600 μg produces a prompt fall of $20\%$ to $30\%$ in total plasma Hcy concentrations; in contrast, patients with ESRD are more resistant to this Hcy‐lowering action of folic acid and even the use of pharmacological doses of folic acid or reduced folates does not normalize completely circulating Hcy levels (Ducloux et al., 2002; Massy, 2003; Sunder‐Plassmann et al., 2000). More commonly occurring vitamin deficiencies in maintenance dialysis patients include those for vitamin C, folate and pyridoxine (Kalantar‐Zadeh & Kopple, 2003). Patients studied here received pyridoxine supplementation; therefore, the role of pyridoxine on Hcy muscle metabolism could not be addressed. In addition, the normal vitamin B12 levels observed in patients studied here (no subject had a B12 serum level <150 pg/mL; Hunt et al., 2014) may have accounted for a lack of association between vitamin B12 levels and Hcy muscle release from muscle. Methionine provides a source of methyl groups that can reduce the need for C‐1 transfers via folate to methylate homocysteine (Ducker & Rabinowitz, 2017). In the postabsorptive state skeletal muscle protein degradation is a major source of intracellular methionine. In patients studied here, net protein balance was to a similar extent negative in patients and control subjects, suggesting that in CKD the intracellular methionine supply was similar to controls. In addition, no association was observed between net protein balance and forearm Hcy release. Hcy release from tissues can be promoted by oxidative stress and inflammation (Giustarini et al., 2009). However, in our study, no association between Hcy release from muscle and IL‐6 levels was observed. This suggests that methionine metabolism in muscle is less sensitive to inflammation than in other tissues. However, a role of the inflammasome on Hcy muscle metabolism cannot be ruled out, since the inflammasome components were not evaluated in our study. Clearance studies in healthy adult humans estimate that 1.2 mmol of Hcy, or approximately $5\%$ to $10\%$ of the total daily cellular production, is delivered daily to the plasma compartment; this figure increases by a factor of 20 × in vitamin‐depleted subjects (Refsum et al., 1998). Considering that the mean fraction of forearm made up of muscle tissue is approximately 0.6 (Yki‐Jarvinen et al., 1987), that forearm blood flow in forearm muscle is approximately $60\%$ to $70\%$ of total flow, that muscle is on the average $40\%$ of body weight, the estimate of the amount of Hcy released into the circulation gives a figure of about 790 umol/day, that is, $66\%$ of the calculated Hcy daily delivery of the plasma compartment (Refsum et al., 1998). This makes the skeletal muscle the major site for export of Hcy to extracellular fluids. Whole body transmethylation, remethylation and transsulfuration rates have been already studied in healthy subjects (Fukagawa et al., 1998, 2000; MacCoss et al., 2001) and in renal patients (Guttmorsen et al., 1997; Stam et al., 2003; van Guldener et al., 1999), but no data regarding methionine kinetics in muscle are available. According to our data Hcy release from muscle account as for $5\%$–$10\%$ of whole body remethylation (Davis et al., 2004), suggesting that the amount of Hcy escaping from the intracellular remethylation pathway is small. However, studies using systemic isotope infusion associated to limb amino acid kinetics might better quantify this estimate. Our data also suggest that loss of muscle mass may cause a decrease in Hcy levels. This is in accordance with data showing that plasma Hcy is positively associated with markers of protein‐energy nutritional status, including somatic indexes, in maintenance dialysis patients (Kalantar‐Zadeh et al., 2003; Suliman et al., 2001). In addition data from our study, that show evidence of a role of muscle in Hcy metabolism, may offer a new paradigm for the study of sarcopenia and CKD‐related cachexia. Elevated levels of Hcy are associated with frailty, skeletal muscle malfunctioning, metabolic injury, and mortality (Kado et al., 2002; Veeranki & Tyagi, 2013). High Hcy might cause wasting by lowering ischemic skeletal muscle responses and angiogenesis through decline in PGC‐1α function (Veeranki et al., 2014). More recently, it has been shown that high Hcy upregulates mitophagy in skeletal muscle remodeling via epigenetic regulation (Singh et al., 2022) an effect that can be corrected by hydrogen sulfide. ## Study limitations In this study, the control subjects did not receive vitamin B6 or calcitriol supplements; therefore, the vitamin supplementation in patients might have influenced both Hcy levels and metabolism. In addition, folate assessment was limited to one single serum folate determination, which cannot distinguish between a transitory decrease in dietary folate intake and a chronic deficiency state. Similarly the use of a single serum cobalamin may be neither sensitive nor specific for cobalamin deficiency. Finally, this study may result not enough sensitive to detect small changes in forearm Hcy exchange occurring in CKD, since Hcy presents a $30\%$ lower enrichment in the forearm vein with respect to phenylalanine. However, increasing sample size was not feasible in this study, which was based on retrospective data collection. ## CONCLUSION In conclusion, our study shows that skeletal muscle is a major provider of Hcy to the systemic circulation both in the normal condition and in patients with CKD. Our study also suggests that extensive Hcy muscle shuttling to the organs, which possess the entire methionine cycle enzymes is a necessary homeostatic response to maintain Hcy levels. Folate plasma levels appear to be a major determinant of muscle Hcy release, suggesting that lack of transsulfuration and hindered remethylation pathway are critical for Hcy escape from muscle. Data provided by this work could be useful to the understanding of the sites regulating 1‐C pool metabolism and the mechanisms leading to muscle protein wasting, observed in many systemic as well as organ diseases. ## AUTHOR CONTRIBUTIONS Giacomo Garibotto and Antonella Sofia designed the study and performed the experiments, Alessando Valli performed the Hcy analysis, Daniela Picciotto and Daniela Verzola analyzed the data and made the figures, Francesca Costigliolo, Michela Saio and Pasquale Esposito drafted the manuscript, Alessando Valli and Francesca Viazzi edited and revised the manuscripts. All authors have read and approved the final version of the manuscript. ## FUNDING INFORMATION This study was supported by grants from the Ministero dell’ Università e della Ricerca Scientifica e Tecnologica PRIN, Iyali. ## CONFLICT OF INTEREST No conflicts of interest, financial or otherwise, are declared by the authors. ## ETHICS STATEMENT All experimental procedures were reviewed and approved by the Ethical Committee of the Department of Internal Medicine of the University of Genoa. All subjects were informed about the nature, purposes, procedures, and possible risks of the study before their informed consent was obtained. The procedures were in accordance with the Helsinki declaration. ## References 1. Bauchart‐Thevret C., Stoll B., Burrin D. G.. Intestinal metabolism of sulfur amino acids. Nutrition Research Reviews (2009) 22 175-187. PMID: 19835653 2. Bergstrom J., Alvestrand A., Furst P.. 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--- title: PTEN‐AKT pathway attenuates apoptosis and adverse remodeling in ponatinib‐induced skeletal muscle toxicity following BMP‐7 treatment authors: - Ayushi Srivastava - Dinender K. Singla journal: Physiological Reports year: 2023 pmcid: PMC10031244 doi: 10.14814/phy2.15629 license: CC BY 4.0 --- # PTEN‐AKT pathway attenuates apoptosis and adverse remodeling in ponatinib‐induced skeletal muscle toxicity following BMP‐7 treatment ## Abstract Tyrosine kinase inhibitors (TKIs) including ponatinib are commonly used to treat cancer patients. Unfortunately, TKIs induce cardiac as well as skeletal muscle dysfunction as a side effect. Therefore, detailed mechanistic studies are required to understand its pathogenesis and to develop a therapeutic treatment. The current study was undertaken to examine whether ponatinib induces apoptosis and apoptotic mechanisms both in vitro and in vivo models and furthermore to test the potential of bone morphogenetic protein 7 (BMP‐7) as a possible treatment option for its attenuation. Sol8 cells, a mouse myogenic cell line was exposed to ponatinib to generate an apoptotic cell culture model and were subsequently treated with BMP‐7 to understand its protective effects. For the in vivo model, C57BL/6J mice were administered with ponatinib to understand apoptosis, cell signaling apoptotic mechanisms, and adverse muscle remodeling and its attenuation with BMP‐7. TUNEL staining, immunohistochemistry (IHC), and real‐time polymerase chain reaction (RT‐PCR) methods were used. Our data show significantly ($p \leq 0.05$) increased TUNEL staining, caspase‐3, BAX/Bcl2 ratio in the in vitro model. Furthermore, our in vivo muscle data show ponatinib‐induced muscle myopathy, and loss in muscle function. The observed muscle myopathy was associated with increased apoptosis, caspase‐3 staining, and BAX/Bcl‐2 ratio as confirmed with IHC and RT‐PCR. Furthermore, our data show a significant ($p \leq 0.05$) increase in the involvement of cell signaling apoptotic regulator protein PTEN and a decrease in cell survival protein AKT. These results suggest that increased apoptosis following ponatinib treatment showed an increase in skeletal muscle remodeling, sarcopenia, and fibrosis. Furthermore, BMP‐7 treatment significantly ($p \leq 0.05$) attenuated ponatinib‐induced apoptosis, BAX/Bcl2 ratio, decreased PTEN, and increased cell survival protein AKT, decreased adverse muscle remodeling, and improved muscle function. Overall, we provide evidence that ponatinib‐induces apoptosis leading to sarcopenia and muscle myopathy with decreased function which was attenuated by BMP‐7. BMP‐7 attenuates ponatinib‐induced muscle toxicity via PTEN‐AKT pathway. The figure describes the mechanisms of ponatinib‐induced muscle toxicity. ## INTRODUCTION Ponatinib is a third generation tyrosine kinase inhibitor (TKI), used for various types of cancer therapies such as leukemia, lung cancer, and glioblastoma (Gozgit et al., 2012; Luciano et al., 2020; Tan et al., 2019; Zhang et al., 2014). However, clinical utilization of TKIs is associated with several adverse toxicities in various organs including cardiovascular, hepatic, and gastrointestinal (Bauer et al., 2016; Shah et al., 2013). Ponatinib‐induced cardiotoxicity has recently been shown to activate apoptosis in cardiomyocytes and causes cardiac dysfunction (Casavecchia et al., 2020; Chan et al., 2020; Ma et al., 2020; Singh et al., 2019; Talbert et al., 2015). Additionally, loss of skeletal muscle mass and muscle myopathy due to TKIs is a commonly recognized adverse drug reaction (Rinninella et al., 2020, 2021; Uchikawa et al., 2020), seen in around $80\%$ of chronic myeloid leukemia patients (Janssen et al., 2019). The musculoskeletal symptoms reported in pre‐clinical trials of ponatinib and other TKI studies include muscular cramps, myalgia, pain, and fatigue (Bouitbir et al., 2020; Cortes et al., 2012, 2018; Janssen et al., 2019, 2020; Kekäle et al., 2015). These symptoms have debilitating effects on disease control and impair the quality of life in patients (Janssen et al., 2019, 2020). However, much is unknown about the effect of ponatinib on skeletal muscle cells and the resulting muscle myotoxicity. Apoptosis is a type of programmed cell death that maintains tissue homeostasis during development and aging (Elmore, 2007). The early stage of apoptosis includes inducing signals for death receptors, increased reactive oxygen species, and change in protein levels of BCL2‐Associated X Protein (BAX), B‐cell lymphoma 2 (Bcl2), and BCL2‐associated agonist of cell death (BAD), this is followed by activation of mitochondrial pathway and commitment to cell death (Elmore, 2007; Shamas‐Din et al., 2013). Hallmarks for apoptosis include caspase‐mediated protein cleavage, chromatin condensation, and DNA fragmentation (Liu & Ahearn, 2001). Dysregulation of apoptosis is known to play important role in multiple diseased conditions including cardiac infarction, neurological disorders, diabetes, cancer, and muscular dystrophy (Anversa et al., 1998; Reed, 1999; Savitz & Rosenbaum, 1998; Serdaroglu et al., 2002). Apoptosis has also been associated with skeletal muscle atrophy (Dirks & Leeuwenburgh, 2005; Dupont‐Versteegden, 2006; Schwartz, 2008). However, the lack of understanding of the exact cause of apoptosis and its mechanisms in ponatinib‐induced muscle myopathy is still unknown. Available therapeutic strategies for skeletal muscle toxicity following TKI treatment have been recommended to patients, however, evidence‐based research, treatment efficacy, and pharmacological interactions have not yet been established. Thus, alternate therapeutic strategies are needed. Bone morphogenetic protein 7 (BMP‐7) is an anti‐inflammatory growth factor belonging to the transforming growth factor beta superfamily which plays an important role in various biological processes (Aluganti Narasimhulu & Singla, 2020). Previous studies have established that BMP‐7 has anti‐fibrotic, anti‐apoptotic, and anti‐inflammatory effects in cardiomyocytes of infarcted hearts and diabetic cardiomyopathy (Aluganti Narasimhulu & Singla, 2020, 2021; Elmadbouh & Singla, 2021; Urbina & Singla, 2014). It remains unknown whether BMP‐7 could be a potential candidate to attenuate ponatinib‐induced skeletal muscle toxicity. Therefore, in the current study, to the best of our knowledge, we establish for the first time: [1] ponatinib induces apoptosis in skeletal muscle cells both in vitro and in vivo models, [2] ponatinib‐induced apoptosis is mediated through BAX‐Bcl2 and PTEN‐Akt pathway, [3] Increased apoptosis enhances skeletal muscle fibrosis and remodeling, [4] BMP‐7 treatment inhibits apoptosis, BAX‐Bcl2 ratio, PTEN‐Akt pathway, and skeletal muscle adverse remodeling, [5] Most importantly, BMP‐7 treatment improves skeletal muscle function. ## Cell culture model Sol8 cells (mouse myogenic cell line) were obtained from the American Type Culture Collection (ATCC) and maintained in Dulbecco's Modified Eagle's Medium (DMEM; ThermoFisher Scientific; cat# 11965092), as we reported previously (Tavakoli Dargani et al., 2018). The DMEM medium was supplemented with $10\%$ fetal bovine serum (R&D Systems; cat# S11550), glutamine (ThermoFisher Scientific; cat# 25030081), sodium pyruvate (ThermoFisher Scientific; cat#11360070), and penicillin–streptomycin (P/S; ThermoFisher Scientific; cat# 15070063), and cells were cultured at 37°C, $5\%$ CO2. ## MTT assay The ponatinib dose curve was established via the MTT assay. A 96‐well plate was used to culture the Sol8 cells that were treated with an increasing concentration of ponatinib (Selleck Chemicals; cat# S1490) (4, 8, 10, 12, 14, and 16 μm) to determine the optimal concentration and cell viability. MTT Kit (Roche; cat# 11465007001) was used as per the instructions provided by the manufacturer. Briefly, mitochondrial activity was assessed by the formation of formazan crystals as an indicator of cell viability. The optical density of the solubilized purple formazan crystals was measured at 550 and 600 nm via Bio‐Rad plate reader (Bio‐Rad) (Singla, Garner, et al., 2019). Cell viability data were presented as a percentage of the control values. ## BrdU assay The effect of ponatinib and BMP‐7 on cell proliferation was determined by BrdU Cell Proliferation enzyme‐linked immunosorbent assay (ELISA) Kit (Abcam; cat# ab126556). Sol8 cells were plated in a 96‐well plate (104 cells/well) and treated with 8 μM ponatinib and 500 ng/mL of BMP‐7 (Bioclone; cat# PA‐0401) for 24 h, which was then replaced with fresh DMEM for 24 h. Manufacturer's protocol provided with the kit was used to perform the cell proliferation assay. In brief, BrdU reagent (20 μL/well) was added to the cells for 24 h. Cells were fixed with fixing solution (200 μL/well, 30 min, room temperature [RT]) and washed as per the instructions followed by incubation with anti‐BrdU detector antibody (100 μL/well, 1 h, RT). Peroxidase Goat Anti‐Mouse IgG conjugate (100 μL/well, 30 min, RT) was added, followed by final wash with both wash buffer and distilled water. Finally, the 3,3′,5,5′‐tetramethylbenzidine peroxidase substrate (100 μL/well, 30 min, RT) was added followed by the addition of Stop solution (100 μL/well). Absorbance was measured at 450 and 550 nm via Bio‐Rad plate reader and net absorbance was calculated by subtracting the blank absorbance (Bhansali et al., 2021). ## Preparation of apoptotic cell culture model Sol8 cells were cultured for 24 h in a 96‐well plate or in 8‐well plates (104 cells/well). Three different study groups were assigned: control, ponatinib, and ponatinib + BMP‐7. Cells were treated with 8 μM of ponatinib (24 h) and 500 ng/mL of BMP‐7 (24 h), followed by replacement with DMEM for 24 h. ## Animal preparation and experimental design The Institutional Animal Care and Use Committee (IACUC) and The University of Central Florida (UCF) approved all the animal procedures and protocols. A total of 43 C57BL/6J male and female mice (10 ± 2‐week‐age) (JAX) were split into three groups ($$n = 14$$–15 animals/group): control ($0.9\%$ saline), ponatinib (5 mg/kg/day; cumulative dose of 25 mg/kg body weight) and ponatinib (5 mg/kg/day) + BMP‐7 (200 μg/kg/day; cumulative dose of 600 μg/kg body weight). Saline and ponatinib were administered for five consecutive days (Monday to Friday) via intraperitoneal injection and BMP‐7 via intravenous injection on three alternative days (Monday, Wednesday, and Friday). The dose for ponatinib and BMP‐7 used in the present study was followed as previously reported (Aluganti Narasimhulu & Singla, 2021; Zhang et al., 2014). Body weights were recorded prior to the first injection (initial weight) and on the day of the sacrifice (final weight). On day 14 (D‐14) mice were subjected to muscle function tests followed by euthanization via cervical dislocation under $4\%$ isoflurane. Blood samples and bilateral soleus muscle (SM) tissues were collected after mice sacrifice. SM tissues were washed with 1× phosphate buffer saline (PBS), weighed, and stored at −80°C for RNA or saved in $4\%$ paraformaldehyde (PFA) for immunohistochemical (IHC) staining, ELISA, and histological staining. ## Body and SM weight Change in body weight was evaluated by subtracting the initial body weight recorded prior to any treatment administration from the final weight recorded on the day of the sacrifice (D‐14). Ratio of SM weight to body weight was calculated to determine the change in SM mass after ponatinib administration. ## Grip strength test A grip strength meter (Columbus Instruments) was used to measure the grip strength of both the forelimbs and four limbs (combined) as mentioned previously (Aluganti Narasimhulu & Singla, 2021). In brief, the mouse was held and allowed to grab the grid by forelimbs and then by combined four‐limbs and gently pulled away in a horizontal manner. The grip strength was recorded in real‐time as peak force in grams. Six to nine trials were averaged and normalized with body weight (g). SigmaPlot software (Systat Software, Inc.) was used to plot the bar graphs. ## Weights test Muscle strength was further assessed using weights test as reported previously (Aluganti Narasimhulu & Singla, 2021; Dessouki et al., 2020). Briefly, mice were subjected to carry weights in increasing order from 15 to 65 g (15, 25, 35, 45, 50, 55, 60, and 65 g) for 3 s. Analysis was done by calculating two scores: weigh × time (WT) and trial × time (TT). TT was scored by multiplying the trial number by the time of hold and WT was scored by multiplying the highest weight held by the time the weight was held. Data was presented by bar graph plotted using SigmaPlot software. ## Tissue processing and deparaffinization After 48 h in $4\%$ PFA, SM tissues were washed with PBS thrice and transferred to $70\%$ ethanol. The tissues were processed using Leica TP1020 tissue processing system (Leica Biosystems). Tissue Tek TEC (Sakura Finetek) was used to embed SM tissues and sectioned at 5 μm thickness using a Microm HM 325 (Fisher Scientific) and placed on microscopic slides (Aluganti Narasimhulu & Singla, 2022). ## TUNEL staining Apoptosis was determined using TUNEL staining for both Sol8 cells and SM tissue sections. In Situ Cell Death Detection Kit (TMR red, Roche; cat# 12156792910) was used according to the provided protocol and as previously described (Singla & McDonald, 2007). Cells were washed, dried, and directly mounted with Antifade Vectashield mounting medium containing 4′,6‐diamidino‐2‐phenylindole (DAPI; Vector laboratories; cat# H‐120010) to stain the nuclei. SM sections were deparaffinized using xylene, following rehydration by successive incubation in decreasing concentration of ethanol ($100\%$, $90\%$, $70\%$, $50\%$, $30\%$) and a final wash with distilled water. Tissue sections were pre‐treated with Proteinase K (25 μg/mL in 100 mM Tris–HCL) and Tunel assay was performed as mentioned in Merino and Singla [2018] followed by blocking with $10\%$ normal goat serum (NGS, Vector Laboratories; cat# S‐1000) and further co‐stained with Myosin (1:100; Sigma Aldrich; cat# M7523‐1ML) followed by secondary antibody, (Alexa 488 goat anti‐rabbit, 1:200; ThermoFisher Scientific; cat# A11008). Sections were then washed with PBS and covered with DAPI‐containing mounting medium. The percentage of apoptotic nuclei was calculated by dividing the total TUNEL positive nuclei by total DAPI positive nuclei [(total TUNEL+ve/total DAPI) × 100]. Four areas were imaged using a Keyence BZ‐X810 fluorescent microscope (Keyence), and quantified using ImageJ. Fluorescence and Brightfield representative images were taken in ×20 and ×40 magnification and histograms were made using Sigma Plot. ## Immunofluorescence staining For immunocytochemistry (ICC) staining, Sol8 cells (104 cells/well) were treated, fixed permeabilized, and blocked ($10\%$ NGS) as described previously (Tavakoli Dargani et al., 2018). Double Immunohistochemistry (IHC) staining was performed on SM tissue sections as previously published (Aluganti Narasimhulu & Singla, 2021). Briefly, SM sections underwent deparaffinization, rehydration, and blocking by $10\%$ NGS. Prior to staining with apoptotic markers SM tissue sections were co‐stained with Myosin (overnight at 4°C) and Alexa 488 goat anti‐rabbit (1:200; 1 h 30 m at RT). Both cells and tissue sections were incubated with primary antibodies of pro‐apoptotic markers: caspase 3 (1:300; Santa Cruz Biotechnology; Cat# sc‐7148), BAX (1:300; Santa Cruz Biotechnology; Cat# sc‐493) and anti‐apoptotic marker Bcl2 (1:300; Santa Cruz Biotechnology; Cat# sc‐492) overnight at 4°C followed by washing with PBS and incubation with secondary antibody, (Alexa 568 goat anti‐rabbit; 1:1000 for in vitro and 1:500 for in vivo; Invitrogen; cat# A11011) for 1 h 30 min at RT. Finally, after washing nuclei were stained by Vectashield mounting medium with DAPI (Aluganti Narasimhulu & Singla, 2022; Dessouki et al., 2020). Images were recorded and quantified as previously described (Aluganti Narasimhulu & Singla, 2021; Singla, Johnson, et al., 2019). BAX to Bcl2 ratio was also calculated by dividing the percent of BAX‐positive cells by the percent of Bcl2‐positive cells (Salakou et al., 2007). ## Real‐time polymerase chain reaction analysis For real‐time polymerase chain reaction (RT‐PCR), SM tissue was used to isolate RNA by TRIzol™ Reagent (ThermoFisher; cat# 15596018) following cDNA synthesis using SuperScript™ III First‐Strand Synthesis SuperMix for quantitative real‐time polymerase chain reaction (qRT‐PCR, ThermoFisher; cat# 11752050). qRT‐PCR was performed by CFX96 C1000 Touch™ Thermal Cycler Multicolor Real‐Time PCR Detection System (Bio‐Rad) with SYBR Green (Invitrogen; cat# 11761500). PCR was performed using mouse primers (Table 1) for: caspase 3, BAX, Bcl2, PTEN, and AKT for apoptotic marker gene expressions and glyceraldehyde 3‐phosphate dehydrogenase as a loading control. Melt curves were established for the reactions and normalized fold expressions were calculated using the 2−ΔΔCT method (Aluganti Narasimhulu & Singla, 2022). TABLE 1 \| Target \| Forward primer \| Reverse primer \| \| --- \| --- \| --- \| \| AKT \| 5′‐ATCCCCTCAACAACTTCTCAGT‐3′ \| 5′‐CTTCCGTCCACTCTTCTCTTTC‐3′ \| \| BAX \| 5′‐CTGGATCCAAGACCAGGGTG‐3′ \| 5′‐CTTCCAGATGGTGAGCGAGG‐3′ \| \| BCL2 \| 5′‐GAACTGGGGGAGGATTGTGG‐3′ \| 5′‐GCATGCTGGGGCCATATAGT‐3′ \| \| Caspase‐3 \| 5′‐GAGCTTGGAACGGTACGCTA‐3′ \| 5′‐GAGTCCACTGACTTGCTCCC‐3′ \| \| GAPDH \| 5′‐ACCCAGAAGACTGTGGATGG‐3′ \| 5′‐CACATTGGGGGTAGGAACAC‐3′ \| \| PTEN \| 5′‐CATTGCCTGTGTGTGGTGATA‐3′ \| 5′‐AGGTTTCCTCTGGTCCTGGTA‐3′ \| ## Enzyme‐linked immunosorbent assay For ELISA Assay 25 μg of protein extracted from SM tissue homogenate (Merino & Singla, 2018) was used as per the provided instructions for Mouse Phosphatase and Tensin Homologs (PTEN; MYBIOSOURCE; cat# MBS3806814) and Mouse RAC‐alpha serine/threonine (AKT; MYBIOSOURCE; cat# MBS288382) ELISA kits. Absorbance was measured at 450 nm using Bio‐Rad plate reader and histograms for each were plotted as arbitrary units. ## Histological staining Hematoxylin and eosin (H&E) staining was performed on SM sections as described previously (Aluganti Narasimhulu & Singla, 2021). The muscle sections are stained pink whereas nuclei are stained blue/purple. Myofibrillar size and atrophy were quantified by assessing the myocyte size (mm2). For Masson's staining, interstitial fibrosis (IF) was determined in SM sections as previously published (Singla, Johnson, et al., 2019). SM sections were stained red, nuclei in black, and fibrosis in blue. IF was measured by quantifying the total fibrotic area (in blue) in mm2. After staining sections were mounted with permount and three to four areas/section were recorded using light microscopy under Keyence BZ‐X810 for quantification at 20× magnification via ImageJ. Representative images were taken at 40× magnification. ## Statistical analysis Statistical significance was analyzed between groups using Student's t‐test and one‐way analysis of variance followed by Tukey test using Sigma Plot. All values are presented as ±standard error of mean, with $p \leq 0.05$ considered statistically significant. ## Effect of ponatinib on skeletal muscle cell viability To evaluate dose‐dependent toxicity of ponatinib on SM (Sol8) cells, MTT assay was performed. As shown in Figure 1A ponatinib doses from 4 to 16 μM significantly ($p \leq 0.05$) decreased cell viability, with increased concentration of ponatinib as compared to control. Based on these results, further in vitro experiments, were performed using 8 μM ponatinib concentration. FIGURE 1: Effect of ponatinib on soleus muscle (Sol8) cell viability and BMP‐7 on cell proliferation. Representative graph shows (A) dose‐dependent toxicity of ponatinib on Sol8 cells, units presented in % viability (n = 6; in quadruplicates); (B) quantitative analysis of the effect of ponatinib and BMP‐7 on Sol8 cell proliferation, units presented in optical density (n = 6–12; in duplicates). Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and One‐way ANOVA followed by Tukey test. p < 0.05 versus Control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7.* ## Effect of BMP‐7 treatment on skeletal muscle cells proliferation To further determine the effects of ponatinib on cell proliferation, BrdU cell proliferation ELISA assay was performed. Figure 1B shows a significant reduction in cell proliferation ($p \leq 0.05$) by ponatinib when compared to control. We observed a significant ($p \leq 0.05$) increase in Sol8 cell proliferation in ponatinib + BMP‐7 group compared to the ponatinib group‐suggesting BMP‐7 plays a protective role in cell proliferation following ponatinib treatment. ## Effect of BMP‐7 treatment on ponatinib‐induced apoptosis and pro‐apoptotic marker caspase 3 in Sol8 cells TUNEL staining was performed in Sol8 cells to establish ponatinib‐induced apoptosis in Sol8 cells. Figure 2A show brightfield images (Figure 2A[a,g,m], ×20) of Sol8 cells with and without treatment groups of ponatinib and BMP‐7, TUNEL‐positive cells in red (Figure 2A[b,h,n]), total nuclei stained with DAPI in blue (Figure 2A[c,i,o]), merged (Figure 2A[d,j,p], 20×), merged brightfield (Figure 2A[e,k,q], 40×), and enlarged images (Figure 2A[f,l,r]) shows TUNEL‐positive nuclei in pink, suggesting an increase in apoptotic TUNEL nuclei in ponatinib‐treated group as compared to control (Figure 2A[a–f]) and its reduction in the BMP‐7‐treated group. Furthermore, the quantitative analysis in Figure 2B shows a significant increase ($p \leq 0.05$) in TUNEL‐positive nuclei in ponatinib treated group as compared to control. Whereas significant decrease ($p \leq 0.05$) in TUNEL‐positive nuclei were observed following BMP‐7 treatment suggesting that BMP‐7 attenuates ponatinib‐induced apoptosis. FIGURE 2: Effect of BMP‐7 treatment on ponatinib‐induced apoptosis and pro‐apoptotic marker caspase 3 in Sol8 cells. Representative images show positive cells for (A) TUNEL staining and (C) caspase 3. (A, C) Show (×20) brightfield images (a, g, m), TUNEL and caspase 3‐positive cells in red (b, h, n), DAPI in blue (c, i, o), and merged images (d, j, p). Scale bar = 100 μm. ×40 Brightfield merged images (e, k, q) and black boxes and arrows indicate enlarged sections of brightfield merged images (f, l, r). Histograms (B, D) show quantitative analysis for TUNEL and caspase 3‐positive cells over DAPI (n = 4; in triplicates). Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and One‐way ANOVA followed by Tukey test. p < 0.05 versus Control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7; DAPI, 4′,6‐diamidino‐2‐phenylindole.* Next, to strengthen the finding of TUNEL staining, we performed caspase 3 ICC. Our data show higher number of positive cells for pro‐apoptotic marker caspase 3 in ponatinib (Figure 2C[g–l]) group as compared to control (Figure 2C) whereas ponatinib + BMP‐7‐treated (Figure 2C) group shows decrease in caspase 3 staining. The quantitative data Figure 2D show a significant increase ($p \leq 0.05$) of caspase 3 in ponatinib‐treated cells as compared to control. Whereas, following BMP‐7 treatment a significant decrease ($p \leq 0.05$) in caspase 3 staining was observed, suggesting BMP‐7 inhibits caspase 3 expression following ponatinib treatment. ## Effect of BMP‐7 treatment on pro‐apoptotic marker BAX, anti‐apoptotic marker Bcl2, and BAX/Bcl2 ratio in Sol8 cells To determine whether ponatinib‐induced apoptosis triggers pro‐apoptotic marker BAX and anti‐apoptotic marker Bcl2 double IHC staining was performed. Our ICC data demonstrate an increase in expression of BAX protein positive cells in ponatinib group compared to control (Figure 3A). We observed a decreased expression of BAX protein following BMP‐7 treatment (Figure 3A). Additionally, our quantitative data analysis shows a significant ($p \leq 0.05$) increase in BAX staining in ponatinib group as compared to control. Upon BMP‐7 treatment a significant ($p \leq 0.05$) reduction of BAX positive cells was seen (Figure 3B). FIGURE 3: Effect of BMP‐7 treatment on pro‐apoptotic marker BAX, anti‐apoptotic marker Bcl2 and BAX/Bcl2 ratio in Sol8 cells. Representative images show positive cells for (A) pro‐apoptotic marker BAX and (C) anti‐apoptotic marker Bcl2. (A, C) Show (×20) brightfield images (a, g, m), BAX and Bcl2‐positive cells in red (b, h, n), DAPI in blue (c, i, o), merged images (d, j, p). Scale bar = 100 μm. ×40 brightfield merged images (e, k, q) and black boxes and arrows indicate enlarged sections of brightfield merged images (f, l, r). Bar graphs (B, D) show quantitative analysis for pro‐apoptotic marker BAX and anti‐apoptotic marker Bcl2 ICC (n = 4; in triplicates). Histogram (E) show the quantitative analysis for BAX/Bcl2 ratio for ICC. Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus Control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BAX, BCL2‐associated X protein; Bcl2, B‐cell lymphoma 2; BMP‐7, bone morphogenetic protein 7; ICC, immunocytochemistry.* Figure 3C demonstrates the decrease in Bcl2‐positive cells in ponatinib group as compared to controls. Following BMP‐7 treatment Bcl2‐positive cells were increased (Figure 3C). The quantitative data showed a significant ($p \leq 0.05$) reduction in number of positive cells of Bcl2 in ponatinib group as compared to control. Whereas a significant ($p \leq 0.05$) increase was observed in ponatinib + BMP‐7 group (Figure 3D). We further investigated the BAX/Bcl2 ratio after ponatinib and BMP‐7 treatment, Figure 3E histogram shows a significant ($p \leq 0.05$) increase in BAX/Bcl2 ratio in ponatinib group as compared to control and this increase was reversed in BMP‐7 group. This data is further indicative that ponatinib‐induced apoptosis involves BAX and Bcl2 cell signaling. ## Effect of BMP‐7 treatment on ponatinib‐induced weight loss and sarcopenia Change in body weight after ponatinib and BMP‐7 administration was assessed by measuring weight gain and weight loss across the three groups. Figure 4B shows a significant ($p \leq 0.05$) loss in weight after ponatinib administration as compared to control and a significant ($p \leq 0.05$) weight gain was noted upon BMP‐7 treatment. We further investigated the effect of ponatinib and BMP‐7 on SM mass loss. Change in SM weight to body weight was calculated. Figure 4C shows ponatinib‐treated muscles had significantly ($p \leq 0.05$) reduced weight and developed sarcopenia as compared to control. Whereas a significant ($p \leq 0.05$) improvement in SM mass was seen after the BMP‐7 treatment. This is indicative of BMP‐7 being a potential therapeutic intervention that can be administered to attenuate weight loss and sarcopenia exhibited after ponatinib treatment. FIGURE 4: Effect of BMP‐7 treatment on ponatinib‐induced weight loss, sarcopenia and muscle dysfunction. (A) Schematic representation of the injection schedule and the study design. Bar graphs showing increase in (B) body weight (BW) (C) soleus muscle weight on BMP‐7 administration. Muscle function was evaluated on day 14 before mice sacrifice. Bar graphs representing the quantification and analysis for multiple muscle function tests (D) forelimb grip strength, (E) four‐limbs grip strength, (F) weight's test‐TT Test and (G) weight's test‐WT test (n = 14–15). Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7; TT, trial × time; WT, weigh × time.* ## Effect of BMP‐7 treatment on ponatinib‐induced muscle dysfunction To study the effect of ponatinib on the loss of muscle function and assess its improvement by BMP‐7, mice were subjected to the following tests: [1] grip strength for forelimbs and four limbs and [2] weights test. Data for grip strength for both forelimb (Figure 4D) and four limbs (Figure 4E) show a significant ($p \leq 0.05$) loss of grip strength in the muscles of ponatinib‐treated mice as compared to control. However, following BMP‐7 treatment a significant ($p \leq 0.05$) improvement was noticed in the grip strength tests. Weights test data was achieved by analyzing TT method and the WT method. For both methods, as depicted in Figure 4F,G, a significant ($p \leq 0.05$) decrease in the forelimb muscle strength was observed in ponatinib mice as compared to control, which was significantly ($p \leq 0.05$) improved upon BMP‐7 treatment group. This is suggestive that BMP‐7 treatment improves ponatinib‐induced muscle dysfunction. ## Effect of BMP‐7 treatment on ponatinib‐induced apoptosis and pro‐apoptotic marker caspase 3 in SM To further confirm our in vitro findings of ponatinib‐induced apoptosis, we performed TUNEL staining to assess ponatinib‐induced apoptosis and its attenuation by BMP‐7 on SM tissue sections. We demonstrated TUNEL‐positive nuclei (Figure 5A) and caspase 3‐positive cells (Figure 5C). The images show a significantly higher number of TUNEL‐positive nuclei in ponatinib‐treated SM (Figure 5A[f–j]) in comparison with control (Figure 5A[a–e]) whereas a decrease in TUNEL‐positive cells was noticed in ponatinib + BMP‐7 group (Figure 5A[k–o]). Furthermore, the quantitative analysis shows a significant ($p \leq 0.05$) increase in TUNEL‐positive nuclei in ponatinib‐treated group as compared to control. However, the ponatinib + BMP‐7 group had a significant decrease in TUNEL‐positive nuclei following BMP‐7 treatment as compared to the ponatinib group (Figure 5B). FIGURE 5: Effect of BMP‐7 treatment on ponatinib‐induced apoptosis and pro‐apoptotic marker caspase 3 in SM tissue. As shown in ×40 representative photomicrographs of SM tissue sections (A) TUNEL staining and (C) caspase 3 demonstrating positive cells stained red (a, f, k), myosin stained muscle cells in green (b, g, l), total nuclei stained in blue with DAPI (c, h, m) and merged images (d, i, n). Scale bar = 100 μm. Dotted white boxes and arrows indicate enlarged sections of merged images (e, j, o). Bar graphs (B, D) represent quantitative analysis of IHC (n = 7–8) and graph (E) show the gene expression for caspase 3 (n = 4–6). Error bar = ± standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7; DAPI, 4′,6‐diamidino‐2‐phenylindole; IHC, immunohistochemistry; SM, soleus muscle.* Pro‐apoptotic marker caspase 3 was further examined by double IHC. Our data show increased expression of pro‐apoptotic marker caspase 3 in ponatinib group as compared to control. Whereas a decrease in caspase 3‐positive cells is noticeably seen following BMP‐7 treatment (Figure 5C). Next, the quantitative data show a significant increase ($p \leq 0.05$) of caspase 3 in ponatinib‐treated group as compared to control and its significant decrease ($p \leq 0.05$) after BMP‐7 treatment (Figure 5D). To strengthen our IHC findings, RT‐PCR analysis was performed, which shows a significant increase ($p \leq 0.05$) in caspase 3 gene expression in ponatinib‐treated mice as compared to control (Figure 5E). After BMP‐7 administration a significant ($p \leq 0.05$) reduction in caspase 3 gene expression was observed. Both the in vitro and in vivo studies coincide in confirming the occurrence of apoptosis induced by ponatinib which is attenuated following BMP‐7 treatment. ## Effect of BMP‐7 treatment on pro‐apoptotic marker BAX, anti‐apoptotic marker Bcl2, and BAX/Bcl2 ratio in SM The effect of ponatinib and BMP‐7 on pro‐apoptotic marker BAX and anti‐apoptotic marker Bcl2 was further determined by IHC and RT‐PCR. Our IHC data show BAX‐positive cells are increased in ponatinib (f‐j, Figure 6A) group as compared to control (Figure 6A[a–e]). Following BMP‐7 administration BAX‐positive cells decreased in number (Figure 6A[k–o]). Our quantitative data further demonstrates a significant ($p \leq 0.05$) increase in BAX‐positive cells in ponatinib group as compared to control and its significant ($p \leq 0.05$) attenuation in ponatinib + BMP‐7 group (Figure 6B). RT‐PCR data analysis for BAX gene showed a significant ($p \leq 0.05$) higher expression following ponatinib treatment as compared to control; however, a significant ($p \leq 0.05$) reduction in BAX gene expression was observed after BMP‐7 administration (Figure 6C). FIGURE 6: Effect of BMP‐7 treatment on pro‐apoptotic marker BAX, anti‐apoptotic marker Bcl2 and BAX/Bcl2 ratio in SM tissue. As shown in ×40 representative photomicrographs of SM tissue sections (A) BAX and (D) Bcl2‐positive cells are stained red (a, f, k), muscle cells stained green by myosin (b, g, l), DAPI stained nuclei in blue (c, h, m) and merged images (d, i, n). Scale bar = 100 μm. Dotted white boxes and arrows indicate enlarged sections of merged images (e, j, o). Bar graphs (B, E) represent quantitative analysis of IHC (n = 7–8) and graphs (C, F) show the gene expression for BAX (n = 5–6) and Bcl2 (n = 5–7). Histograms (G, H) show the quantitative analysis for BAX/Bcl2 ratio for IHC and real‐time polymerase chain reaction. Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BAX, BCL2‐associated X protein; Bcl2, B‐cell lymphoma 2; BMP‐7, bone morphogenetic protein 7; DAPI, 4′,6‐diamidino‐2‐phenylindole; IHC, immunohistochemistry; SM, soleus muscle.* Our anti‐apoptotic data show decreased number of Bcl2‐positive cells in ponatinib (Figure 6E[f–j]) group as compared to control (Figure 6E[a–e]). Noticeably, an increase was seen in Bcl2 following BMP‐7 treatment (Figure 6E[k–o]). The quantitative data also supports a significant ($p \leq 0.05$) reduction in number of positive cells of Bcl2 in ponatinib group as compared to control whereas a significant ($p \leq 0.05$) increase was observed in ponatinib + BMP‐7 group (Figure 6F). RT‐PCR data analysis in Figure 6G shows significantly ($p \leq 0.05$) lowered Bcl2 gene expression in ponatinib group as compared to control, whereas the Bcl2 gene expression significantly ($p \leq 0.05$) increased in BMP‐7 treated mice. Next, we evaluated the BAX/Bcl2 ratio using both IHC and RT‐PCR data. The quantitative analysis in Figure 6D,H shows a significant ($p \leq 0.05$) increase in BAX/Bcl2 ratio in ponatinib group as compared to control and ponatinib + BMP‐7 groups. This set of data strengthens the initial data of the in vitro study in our in vivo model, suggesting that significant changes in BAX, Bcl2 expression, and BAX/Bcl2 ratio occur due to ponatinib‐induced apoptosis. Furthermore, indicating BMP‐7 administration ameliorates pro‐apoptotic marker BAX and BAX/Bcl2 ratio and promotes anti‐apoptotic Bcl2 expression. ## Effect of BMP‐7 treatment on cell signaling markers PTEN and AKT To determine the regulatory effect of ponatinib and BMP‐7 on pro‐apoptotic regulator of apoptosis protein PTEN and pro‐survival protein AKT, we examined expressions of both PTEN and AKT with ELISA and RT‐PCR. Our ELISA data show a significant ($p \leq 0.05$) increase in PTEN expression of ponatinib‐treated SM, compared to control (Figure 7A). However, a significant ($p \leq 0.05$) reduction in expression was observed following BMP‐7 treatment (Figure 7A). This data was further confirmed using PTEN gene expression where we observed a significant increase of PTEN in ponatinib which was decreased following BMP‐7 treatment. However, our ELISA data on pro‐survival protein AKT demonstrate significant ($p \leq 0.05$) decrease in expression levels in the ponatinib group when compared to control and its significant ($p \leq 0.05$) increase in ponatinib + BMP‐7‐treated group (Figure 7C). RT‐PCR data following the pattern of AKT ELISA data and support its role in the regulation of ponatinib‐induced apoptosis. This set of data suggests that ponatinib regulates ponatinib‐induced apoptosis through PTEN‐AKT pathway which is attenuated by BMP‐7. FIGURE 7: Effect of BMP‐7 treatment on cell signaling markers PTEN and AKT. Enzyme‐linked immunosorbent assay and real‐time polymerase chain reaction was performed using SM tissue to determine the levels of PTEN and AKT. Histograms (A, B) represent the quantitative analysis of PTEN and AKT levels in 25 μg of protein (n = 6–8). Bar graphs (C, D) show the gene expression for PTEN (n = 5–6) and AKT (n = 5–6). Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus control; # p < 0.05 versus ponatinib. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7; SM, soleus muscle.* ## Effect of BMP‐7 treatment on ponatinib‐induced muscle atrophy and adverse muscle remodeling To evaluate if ponatinib‐induced apoptosis causes muscle atrophy and adverse muscle remodeling and to determine their attenuation with BMP‐7. H&E and Masson's trichrome staining were performed on SM tissue sections. In Figure 8A, representative photomicrographs of H&E staining show a visible decrease in the myofibrillar size of SM tissue in the ponatinib‐treated group (Figure 8A,B) as compared to control (Figure 8A[a]) suggesting atrophy. Whereas in ponatinib + BMP‐7‐treated group following BMP‐7 administration (Figure 8A[a–c]) a significant increase in the muscle cell size was observed as compared to ponatinib‐treated SM. Quantitative analysis in Figure 8B confirmed that ponatinib‐treated SM tissues had significantly ($p \leq 0.05$) reduced muscle cell size as compared to control. Whereas BMP‐7 administered mice showed a significant ($p \leq 0.05$) increase in their muscle cell size. FIGURE 8: Effect of BMP‐7 treatment on ponatinib‐induced muscle atrophy and adverse muscle remodeling. Representative images (×40) of SM sections (A) stained with hematoxylin and eosin to detect atrophy and (C) of Masson's trichrome staining to show interstitial fibrosis in soleus muscle on day 14 after ponatinib administration in control and experimental groups. Stained sections were quantified at ×20 magnification and magnified for visualization of atrophy and interstitial fibrosis. Bar graphs (B, D) represent quantitative analysis for muscle atrophy and interstitial fibrosis (n = 6–8). Error bar = ±standard error of the mean. Statistical significance was analyzed using Student's t‐test and one‐way ANOVA followed by Tukey test. p < 0.05 versus control; # p < 0.05 versus ponatinib. Scale bar = 100 μm. ANOVA, analysis of variance; BMP‐7, bone morphogenetic protein 7; SM, soleus muscle.* Skeletal muscle fibrosis in ponatinib mice and its reduction by BMP‐7 was assessed by Masson's Trichrome staining. Representative photomicrographs in Figure 8C show increased collagen (blue area) in ponatinib‐treated SM tissues (Figure 8C[b]) as compared to control (Figure 8C[a]) indicating IF. Following BMP‐7 treatment a significant decrease in IF was observed (Figure 8C[c]) as compared to the ponatinib group. The quantitative data in Figure 8D also shows a significant ($p \leq 0.05$) increase in IF in ponatinib administered mice as compared to control and its significant ($p \leq 0.05$) decrease in BMP‐7‐treated mice. ## DISCUSSION Ponatinib is a potent orally bioavailable TKI known to induce cardiotoxicity and myotoxicity as major adverse side effects (ClinicalTrials.gov, 2020a, 2020b; Casavecchia et al., 2020; Chan et al., 2020; Ma et al., 2020; Singh et al., 2019; Talbert et al., 2015). Very limited studies have been done on ponatinib till now, Phase 2 pre‐clinical trials of ponatinib have reported muscle spasms, muscular weakness, musculoskeletal pain, and myalgia (ClinicalTrials.gov, 2020a, 2020b). The forenamed adverse side effects justified a study to investigate the mechanism of action for ponatinib‐induced skeletal muscle toxicity. As of now, as per the best of our knowledge there are no published studies on ponatinib muscle toxicity in animals. The current work is a novel study being performed regarding ponatinib‐induced toxicity in SM cells and the protective effect of BMP‐7 treatment in both in vitro and in vivo models. This study presents the following major and important information on ponatinib that demonstrates muscle toxicity as follows: [1] decrease in Sol8 cell viability and proliferation, [2] increase in apoptotic‐positive nuclei as confirmed with TUNEL staining and upregulation of pro‐apoptotic markers caspase 3 and BAX, [3] decrease in anti‐ apoptotic marker Bcl2 expression, [4] increase in PTEN, a negative regulator of apoptosis and decrease in pro‐survival protein AKT, [5] decrease in body weight and sarcopenia development, [6] increase in muscle dysfunction, and finally [7] development of muscle atrophy and adverse muscle remodeling in SM. BMP‐7 treatment attenuates muscle toxicity both in in vitro and in vivo via decreasing apoptosis, decreased pro‐apoptotic markers, increasing levels of anti‐apoptotic proteins, reversing adverse muscle remodeling, and improving muscle function. First, we identified the correct dose of ponatinib‐induced muscle toxicity (PMIT) in Sol8 cells in vitro using MTT assay. After ponatinib dose confirmation we aimed to understand whether this anti‐cancer drug has any effects on Sol8 cell proliferation. The result of our data demonstrated a significant decrease in cell proliferation following ponatinib treatment. Our data show significant skeletal muscle cell toxicity in Sol8 cells by ponatinib in a dose‐dependent manner which correlates with previously published studies on ponatinib‐induced dose‐dependent cytotoxicity in endothelial cells, cardiomyocytes, and cancer cells (Casavecchia et al., 2020; Gozgit et al., 2011; Liu et al., 2019; Saussele et al., 2020; Singh et al., 2019; Talbert et al., 2015). Apoptosis is a programmed cell death mechanism identified by DNA fragmentation and mitochondrial‐mediated protein such as caspase 3. Ponatinib‐induced apoptosis in Sol8 cells was initially determined by significant increase in DNA fragmentation using TUNEL staining and increased levels of caspase 3. Furthermore, presence of apoptosis was confirmed by upregulation of pro‐apoptotic protein BAX and downregulation of anti‐apoptotic protein Bcl2 in in vitro studies. To further understand the significance of ponatinib‐induced apoptosis in cell culture model, we developed ponatinib‐induced muscle toxicity in an in vivo model. Our model shows significant decrease in body weight, loss of muscle mass with decreased muscle function. Loss of skeletal muscle mass and body weight during TKI therapy has been associated with dose limiting clinical toxicities which leads to negative clinical outcomes such as temporary or permanent treatment discontinuation and decrease in overall survival of patients (Rinninella et al., 2020, 2021). Therefore, our animal studies are in agreement with clinical studies using ponatinib and other TKIs (ClinicalTrials.gov, 2020a, 2020b; Janssen et al., 2019; Kekäle et al., 2015). Next, we investigated the impact of ponatinib‐induced sarcopenia by assessing the muscle function of mice. A significant increase in muscle dysfunction was observed in both grip strength test and weight's test for ponatinib‐treated mice. This anti‐cancer drug‐induced muscle toxicity model corroborates with other published studies on muscle toxicity (Dessouki et al., 2020; Merino & Singla, 2018). Additionally, we performed TUNEL staining and caspase 3 staining on SM to confirm our in vitro findings. Our data show significantly increased apoptosis and caspase 3 using immunostaining and RT‐PCR methods. Next, to strengthen our apoptosis findings, we performed Bcl2 and BAX IHC and gene expression. Bcl2 family of interacting proteins play a pro‐survival role whereas family members BAX and Bcl‐2 homologus antagonist/killer (BAK) play an opposing role in the regulation of apoptosis in cancer, cardiomyocyte, and muscle cells (Aluganti Narasimhulu & Singla, 2022; Merino & Singla, 2018; Ramadan et al., 2019). However, the role of BAX and Bcl2 proteins in ponatinib‐induced apoptosis is not well‐established; therefore, our performed studies suggest a significant increase in BAX protein following ponatinib treatment whereas a decrease in Bcl2 protein was observed using IHC and RT‐PCR. Moreover, significant increase in BAX/Bcl2 ratio for both ICC and IHC were noticed. Our data on BAX and Bcl2 agrees with other reported studies published on hypoxia‐induced muscle and cardiac apoptosis (Cho et al., 2004; Webster et al., 1999). Next, we confirmed whether ponatinib‐induced apoptosis in SM is mediated by PTEN‐AKT pathway. Loss of PTEN due to mutations in cancer cells upregulates cell proliferation mediated by Akt pathway whereas upregulation of PTEN in cells induces apoptosis (Johnson & Singla, 2018; Singla, 2015), therefore, balance on PTEN/Akt confirms cell survival vs cell death. Moreover, PTEN is a negative regulator of pro‐survival protein AKT. Previous studies have shown inhibition of pro‐survival pathways during ponatinib‐induced apoptosis in cardiomyocytes (Merino & Singla, 2018; Singh et al., 2020; Talbert et al., 2015). However, the role of PTEN‐AKT pathway in ponatinib‐induced apoptosis in SM is not known. A significant increase in PTEN expression was observed in ponatinib‐treated SMs. Moreover, a significant decrease in AKT expression was noticed following ponatinib treatment. Our data in the present study suggest that ponatinib‐induced apoptosis in SM is regulated by PTEN‐AKT pathway. Following apoptosis confirmation in ponatinib‐induced SM, it becomes reasonable to ask whether apoptosis has further effects on muscle remodeling. Therefore, we examine myofibrillar size for muscle atrophy and IF using H&E and Mason's trichrome histological staining. Our data showed significant loss of myofibrillar size and increased collagen deposition in the SM after ponatinib administration, suggesting presence of atrophy and fibrosis. This muscle remodeling data agrees with previously published studies showing apoptosis‐induces atrophy and muscle fibrosis in variety of conditions such as aging, inflammation, and liver cirrhosis (Dirks & Leeuwenburgh, 2005; Kurosawa et al., 2021; Lala‐Tabbert et al., 2019; Saito et al., 2020). Based on current literature, there are no readily available therapeutic options to treat ponatinib‐induced skeletal muscle toxicity. In this study, we identified BMP‐7 a growth factor belonging to the transforming growth factor‐β superfamily, commonly given to osteoporosis patients as a potential therapeutic agent. BMP‐7 has previously been reported to have anti‐fibrotic properties in heart, muscle, and kidney (Aluganti Narasimhulu & Singla, 2020, 2021; Elmadbouh & Singla, 2021; Urbina & Singla, 2014; Zeisberg et al., 2003). The findings of the current study suggests that BMP‐7 promotes Sol8 cell proliferation, inhibits ponatinib‐induced apoptosis in both in vitro and in vivo models as observed by decrease in TUNEL stained nuclei, pro‐apoptotic markers caspase 3 and BAX, and increase in anti‐apoptotic marker Bcl2 expressions. Furthermore, decrease in BAX/Bcl2 ratio, PTEN expression and increased AKT expression also reinforces that BMP‐7 reduces ponatinib‐induced apoptosis. Moreover, significant decrease in loss of body weight and sarcopenia, improved muscle function and decrease in muscle atrophy and fibrosis after BMP‐7 treatment was observed. This set of data suggests potential therapeutic efficacy of BMP‐7 in attenuation of ponatinib‐induced apoptosis and muscle remodeling. This agrees with previously reported studies showing BMP‐7 improves skeletal muscle fibrosis, atrophy, sarcopenia, and muscle dysfunction in diabetes and atherosclerosis (Aluganti Narasimhulu & Singla, 2020, 2021; Elmadbouh & Singla, 2021; Urbina & Singla, 2014). In conclusion, to the best of our knowledge we report for the first time that ponatinib induces skeletal muscle toxicity via apoptosis in both in vitro (Sol8 cells) and in vivo (SM) models. Apoptosis is confirmed by TUNEL staining, expression of pro‐apoptotic and anti‐apoptotic markers, caspase 3, BAX and Bcl2, and BAX/Bcl2 ratio. Further expanding on how ponatinib‐induced apoptosis leads to the development and progression of muscle myopathy as seen by increase in sarcopenia, muscle dysfunction, atrophy, and adverse muscle remodeling. Evident attenuation of apoptosis, sarcopenia, muscle dysfunction, atrophy, and adverse muscle remodeling after BMP‐7 treatment is promising. 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--- title: TRPV4 functional status in cystic cells regulates cystogenesis in autosomal recessive polycystic kidney disease during variations in dietary potassium authors: - Kyrylo Pyrshev - Anna Stavniichuk - Viktor N. Tomilin - Naghmeh Hassanzadeh Khayyat - Guohui Ren - Mariya Kordysh - Oleg Zaika - Mykola Mamenko - Oleh Pochynyuk journal: Physiological Reports year: 2023 pmcid: PMC10031299 doi: 10.14814/phy2.15641 license: CC BY 4.0 --- # TRPV4 functional status in cystic cells regulates cystogenesis in autosomal recessive polycystic kidney disease during variations in dietary potassium ## Abstract Mechanosensitive TRPV4 channel plays a dominant role in maintaining [Ca2+] i homeostasis and flow‐sensitive [Ca2+] i signaling in the renal tubule. Polycystic kidney disease (PKD) manifests as progressive cyst growth due to cAMP‐dependent fluid secretion along with deficient mechanosensitivity and impaired TRPV4 activity. Here, we tested how regulation of renal TRPV4 function by dietary K+ intake modulates the rate of cystogenesis and mechanosensitive [Ca2+] i signaling in cystic cells of PCK453 rats, a homologous model of human autosomal recessive PKD (ARPKD). One month treatment with both high KCl ($5\%$ K+) and KB/C ($5\%$ K+ with bicarbonate/citrate) diets significantly increased TRPV4 levels when compared to control ($0.9\%$ K+). High KCl diet caused an increased TRPV4‐dependent Ca2+ influx, and partial restoration of mechanosensitivity in freshly isolated monolayers of cystic cells. Unexpectedly, high KB/C diet induced an opposite effect by reducing TRPV4 activity and worsening [Ca2+] i homeostasis. Importantly, high KCl diet decreased cAMP, whereas high KB/C diet further increased cAMP levels in cystic cells (assessed as AQP2 distribution). At the systemic level, high KCl diet fed PCK453 rats had significantly lower kidney‐to‐bodyweight ratio and reduced cystic area. These beneficial effects were negated by a concomitant administration of an orally active TRPV4 antagonist, GSK2193874, resulting in greater kidney weight, accelerated cystogenesis, and augmented renal injury. High KB/C diet also exacerbated renal manifestations of ARPKD, consistent with deficient TRPV4 activity in cystic cells. Overall, we demonstrate that TRPV4 channel activity negatively regulates cAMP levels in cystic cells thus attenuating (high activity) or accelerating (low activity) ARPKD progression. Polycystic kidney disease (PKD) is a devastating pathology, which is characterized by the development of numerous fluid‐filled cysts leading to declined renal function. Previous evidence found augmented cAMP levels and impaired mechanosensitivity in cystic cells. Here, we investigate how functional activity of the mechanosensitive TRPV4 channel affects cystogenesis and cAMP levels in PCK453 rats, a homologous model of human autosomal recessive PKD. We show that high KCl diet increases renal TRPV4 levels, augments TRPV4‐dependent Ca2+ influx in freshly isolated cystic cell monolayers and markedly slow the development and growth of renal cysts in PCK453 rats. In contrast, impaired TRPV4 activity in cystic cells leads to the membrane accumulation of AQP2 water channel indicative of augmented cAMP levels in cystic cells leading to greatly accelerated cystogenesis and renal injury. We propose that pharmacological or dietary means leading to TRPV4 stimulation might be useful to counteract PKD progression in the clinical setting. ## INTRODUCTION Whole body water and solute homeostasis depends critically on the ability of the kidneys to perpetually filter plasma and to excrete any unneeded extras and waste products with urine. This is achieved by reabsorption of approximately $99\%$ of the filtrate by the renal tubules accompanied by the secretion of certain constituents, most commonly potassium and protons. It is now generally accepted that dynamic alterations in fluid flow serve as physiologically relevant mechanical cues leading to elevations in intracellular Ca2+ concentration ([Ca2+] i) in renal tubule cells (Weinbaum et al., 2010). This, in turn, triggers numerous intracellular cascades to adjust the rates of reabsorption and secretion (Satlin et al., 2006; Weinbaum et al., 2010; Woda et al., 2001). The mechanosensitive Ca2+‐permeable transient receptor potential vanilloid type 4 (TRPV4) channel is abundantly expressed in the distal segments of the renal tubule, including the connecting tubule and the collecting duct, and also to a lesser extent in the proximal tubule (Berrout et al., 2012; Pyrshev et al., 2022). TRPV4 activation by increases in tubular flow/increased perfusion is indispensable for the direct Ca2+ influx and mechanosensitive elevations in [Ca2+] i (Pyrshev et al., 2022). Genetic deletion of TRPV4 does not only abolish flow‐dependent [Ca2+] i responses (Berrout et al., 2012), but also precludes flow‐induced alterations in electrolyte transport in perfused cortical collecting ducts (Taniguchi et al., 2007). The term “polycystic kidney disease” entails a large diverse group of hereditary disorders, which commonly manifest as the formation and further enlargement of multiple fluid‐filled cysts in renal parenchyma (Bergmann et al., 2018, Ghata & Cowley, 2017). PKD progression causes a gradual decline of renal function and the development of interstitial fibrosis eventually progressing to the end‐stage renal disease (ESRD; Bergmann et al., 2018; Ghata & Cowley, 2017). Despite the distinct underlying genetic defects: polycystin 1 and 2 (PC1 and PC2) for the most prevalent autosomal dominant PKD (ADPKD) and fibrocystin for a rapidly evolving “juvenile” autosomal recessive PKD (ARPKD), the malicious epithelial‐to‐cyst cell transformation is associated with a drastic switch in overall transport direction from predominantly reabsorptive to secretory (Belibi et al., 2004; Sullivan et al., 1998). Abundant published evidence identified the central role of inappropriately increased intracellular cAMP levels leading to over‐activation of B‐Raf/ERK signaling in driving apical Cl−‐secretion and accelerated proliferation/apoptosis of cystic cells (Belibi et al., 2004; Wallace, 2011; Yamaguchi et al., 2003, 2006). Indeed, the only FDA‐approved drug for ADPKD treatment, vasopressin receptor 2 blocker, tolvaptan, interferes with cystogenesis by reducing intracellular cAMP levels although at the expense of compromised urinary concentrating ability (Edwards et al., 2018; Torres et al., 2004; van Gastel & Torres, 2017). This encourages further search of new strategies to decrease cAMP levels with no disturbance in systemic water balance during PKD treatment. Multiple reports demonstrate that dysregulated cAMP signaling is associated with decreased basal [Ca2+] i levels and inability to respond to flow‐induced shear stress by elevating [Ca2+] i in cystic cells (Mekahli et al., 2013; Yamaguchi et al., 2004, 2006). Of note, the Ca2+ permeable PC2 channel lacks intrinsic mechanosensitive properties, but it can heteromerize with TRPV4 to produce a functional mechanoactivated channel in renal epithelium (Du et al., 2012; Kottgen et al., 2008; Zhang et al., 2013). While this strongly indicates that deficient TRPV4 activity can lead to PKD pathology, its deletion, somewhat surprisingly, does not cause renal cystic phenotype in mice and zebra fish (Kottgen et al., 2008). On the contrary, we showed an impaired TRPV4‐dependent Ca2+ influx and deficient channel glycosylation in freshly isolated monolayers of cystic cells of PCK453 rats, a homologous model of human ARPKD (Zaika et al., 2013), and in primary cultured cystic cells of human ADPKD kidneys (Tomilin et al., 2018) pointing to a common underlying mechanism associated with channel dysfunction in driving cystogenesis. Moreover, TRPV4 agonist, GSK1016790A, was capable of reducing cystogenesis in PCK453 rats (Zaika et al., 2013). Overall, this argues that the systemic factors leading to TRPV4 stimulation might be of clinical relevance in counteracting PKD progression. It has been recently shown that TRPV4‐mediated Ca2+ influx is the critical adaptive mechanism in promoting kaliuresis upon dietary K+ load (Pyrshev et al., 2022). Increased dietary K+ intake leads to a marked upregulation of renal TRPV4 expression and activity to facilitate flow‐induced K+ secretion via large conductance Ca2+ activated BK channel in the collecting duct (Mamenko et al., 2017). To this end, the current investigation aimed to determine how physiologically relevant manipulation of TRPV4 activity and expression by dietary K+ regimen affects cAMP signaling at the cellular level and cystogenesis in PCK453 rats. ## Reagents All chemicals and materials were from Sigma, VWR (Radnor), Fisher, and Tocris unless noted otherwise and were at least of reagent grade. ## Research animals A homologous human ARPKD animal model, PCK453 rats were originally purchased from Charles Rivers Laboratories and bred in local facility. One‐month‐old littermates (males and females in equal quantities) were randomly assigned to consume regular diet ($0.9\%$ K+, TD7012), high KCl diet ($5\%$ K+, TD150699), and high KBicarbonate/Citrate (KB/C) diet ($5\%$ K+ with Bicarbonate to citrate in 4:1 ratio, TD150759) for 1 month. All diets were purchased from Envigo. Animals were given either tap water ad libitum, or water containing 28 μg/kgBW of a TRPV4 inhibitor, GSK2193874 (estimated final concentration 40 nM); or 26 μg/kgBW of a TRPV4 activator, GSK1016790A (estimated final concentration 40 nM), as necessary for experimental design. Spot urine samples were collected at the last day of the respective treatments before sacrifice between 10 am and 11 am to minimize contribution of the circadian cycle. Cardiac blood punctures were done post mortem. Previous characterization of PCK453 rats showed comparable kidney‐to‐total bodyweight ratio and cystic volume in males and females at 8 weeks, as used in this study, with significantly accelerated ARPKD progression being observed in males starting from 18 weeks (Mason et al., 2010). ## Systemic measurements Urinary and plasma K+ concentrations were measured using Jenway PFP7 Flame photometer (Bibby Scientific). Plasma was separated by centrifugation at 1300 g in Vacutainer Plus SST plastic tubes with clot activator and gel for plasma separation (BD; Cat. # 367988). Urinary pH was measured using MI‐410 pH microelectrode (Microelectrodes Inc.). Urinary creatinine concentration was assessed with QuantiChrom Creatinine Assay Kit (BioAssay Systems; Cat. # DICT‐500) utilizing improved Jaffe method (Mamenko et al., 2012). Aldosterone was measured using an enzymatic immunoassay kit (Cayman Chemical; Cat. # 501090) in accordance with the vendor's protocol. Kidney injury marker 1 (KIM1) was measured using a commercially available kit (R&D System; Cat. # RKM100). ## Histological analysis Kidneys from PCK453 rats were paraffin embedded using the standard protocols and cut into 5 μm thick sections around central area. Trichrome staining kit (Abcam, Cat. # ab150686) was applied for visualization of overall morphology, as well as collagenous connective tissue fibers in kidney sections. Two maximally distal sections within the same kidney from at least 4 animals were used for analysis. Images were quantified using ImageJ 1.50 software (NIH) to calculate cystic and fibrotic areas. For quantification of cystogenesis, the RGB images of the kidney sections were converted to the monochrome binary bit depth, using the Threshold tool. Particle Analyzer tool was applied to the processed images for calculating the number and the area of large cysts (>2 mm2 area) and small dilations (0.2–2 mm2 area) separately. For renal fibrosis area calculations, blue color was extracted from the RGB images of the kidney sections with calculations being performed as similarly described above. Renal fibrosis was presented as a percentage of the total area of the renal section. ## Immunofluorescent microscopy Paraffin embedded 5 μm thick kidney sections from PCK453 rats were used for analysis. For deparaffinization and fixation, the samples were subsequently washed with xylene (3 times for 5 min), $100\%$ (2 times for 1 min), $95\%$ (1 min), $70\%$ (1 min) ethanol, and finally distilled water (5 min). The samples were further treated with antigen retrieval solution (BD Pharmingen Retrievagen A, pH 6.0; Fisher Scientific; Cat. # BDB550524) two times for 3 min. After extensive washout, kidney sections were permeabilized with $0.1\%$ Triton ×100 (Sigma‐Aldrich; Cat. # 56H0850) for 10 min and treated with $10\%$ normal goat serum for an hour at room temperature. Sections were incubated overnight at +4°C with anti‐AQP2‐ATTO Fluor‐550 (1:200, Alomone Labs; Cat. # AQP2‐002‐AO) and anti‐TRPV4 (1:500, Alomone labs; Cat.#. ACC‐034) antibodies. Followed by washing with PBS for 20 min at room temperature, the samples were incubated with Alexa 488 F(ab') secondary antibodies (1:1000 Invitrogen, Eugene, OR, USA; Cat. # A1182668). After washing with PBS for 20 min at room temperature, nuclei were stained with DAPI (0.5 μg/mL) for 10 min at room temperature. The samples were mounted with Fluoromount mounting media (Thermo Scientific). The labeled kidney sections were imaged with a Nikon A1R confocal microscope, as we did similarly before (Berrout et al., 2012; Mamenko et al., 2016). In brief, samples were excited with 405, 488, and/or 561 nm laser diodes and emission captured with a 16‐bit Cool SNAP HQ2 camera (Photometrics) interfaced to a PC running NIS elements software. ## Western blotting Freshly isolated kidneys were placed on ice, decapsulated, and homogenized in 3 volumes of ice‐cold lysis buffer containing 50 mM TrisCl, 5 mM EDTA and $1\%$ Triton X‐100 (pH 7.5) supplemented with Complete Mini protease and PhosSTOP phosphatase inhibitor cocktails (Roche Diagnostics). The homogenates were centrifuged at 1000g for 15 min at +4°C, and sediment was discarded. Protein concentration was determined with NanoPhotometer N60 by a standard absorbance protocol. The samples (40 μg/lane) were separated on $9\%$ polyacrylamide gels at 150 V for 90 min and transferred to a nitrocellulose membrane for 1.5 h at 100 V. Nitrocellulose membranes were incubated with primary anti‐TRPV4 antibodies (1:1000, Alomone labs; Cat.#. ACC‐034) overnight at +4 °C. Upon washout (3 times for 10 min in TBS‐Tween), the membrane was incubated with peroxidase‐conjugated goat anti‐rabbit (1:10000, Jackson ImmunoResearch Laboratories, Cat. # NC9448271) secondary antibodies for 1 h at room temperature. Ponceau red staining was used to verify equal protein load in different samples. Blots were quantified using ImageJ 1.50 software (NIH). The intensities of the non‐glycosylated (lower) and glycosylated (upper) bands were normalized to the total signal of the respective line in Ponceau red staining. ## Mechanical isolation of collecting duct derived cystic cell monolayers Kidneys were decapsulated and sliced into 2 mm thick sections. Monolayers of cells were mechanically isolated from open cyst cavities under stereomicroscope using watchmaker forceps, as we described previously (Pavlov et al., 2015; Zaika et al., 2013). The isolated monolayers were attached apical side upward to square 5 × 5 mm cover‐glasses coated with poly‐L‐lysine and placed into ice‐cold physiologic saline solution buffered with HEPES (pH 7.35). The monolayers were used within 2 h of isolation. ## Intracellular Ca2+ measurements Monolayers of cystic cells were loaded with Fura‐2 by incubation with 2 μM Fura‐2/AM acetoxymethyl ester in the bath solution for 40 min at room temperature followed by a washout with bath solution for additional 10 min. Cover glasses with cystic monolayers were placed in an open‐top imaging study chamber (RC‐26GLP; Warner Instruments) with a bottom coverslip viewing window and the chamber attached to the microscope stage of a Nikon Ti‐S Wide‐Field Fluorescence Imaging System (Nikon Instruments) integrated with Lambda XL light source (Sutter Instrument) and QIClick 1.4 megapixel monochrome CCD camera (QImaging) via NIS Elements 4.3 Imaging Software (Nikon Instruments). Cells were imaged with a 40× Nikon Super Fluor objective, and regions of interest (ROIs) were drawn for individual cells. For [Ca2+] i measurements, the Fura‐2 fluorescence intensity ratio was determined by excitation at 340 and 380 nm and calculating the ratio of the emission intensities at 511 nm in the usual manner every 5 s. The changes in $\frac{340}{380}$ ratio were converted into changes in intracellular calcium concentration, as we described in great details previously (Mamenko, Zaika, O'Neil, et al., 2013). Experiments were performed under permanent perfusion of a bath solution containing (in mM): 150 NaCl, 5 KCl, 1 CaCl2, 2 MgCl2, 5 glucose, and 10 HEPES at 1.5 mL/min rate. For mechanical stimulation, an abrupt increase of perfusion flow to 15 mL/min (10×) was used. This produces a shear stress of 3 dyne/cm2, which is within the physiological range existing in the collecting duct in conditions with increased flow delivery, such as high K+ diet (Berrout et al., 2012). In average, 4 cystic cell monolayers (30–50 cells in each) from at least 3 different rats were used for each tested condition. ## Statistical analysis All summarized data are reported as mean ± SD. Statistical comparisons were made using one‐way ANOVA with post hoc Tukey test or one‐way repeated ANOVA with post hoc Bonferroni test (for paired experiments within the same group). p value <0.05 was considered significant. ## Elevated K+ intake increases TRPV4 expression in the kidney of ARPKD PCK453 rats Abundant experimental evidence demonstrates a central role of Ca2+‐permeable TRPV4 channel in flow‐sensitive [Ca2+] i signaling of the renal tubule (Berrout et al., 2012; Mamenko et al., 2017; Pyrshev et al., 2022). TRPV4 activity is drastically impaired in cyst cells of ARPKD (Zaika et al., 2013) and ADPKD (Tomilin et al., 2018). Here, we aimed to examine whether physiologically relevant stimulation of TRPV4 expression and activity is capable of reducing cystogenesis. To this end, we fed PCK453 rats with either high KCl or high K organic anion (bicarbonate: citrate, B/C as 4:1) diets for 1 month to also investigate a role of accompanying anions. Of note, theses diets contained the same amount of K+ (approximately $5\%$). Neither of the treatments induced notable adverse alterations in systemic K+ balance. Thus, both high K+ diets did not affect plasma K+ levels, when compared to the control group (Figure S1a). Aldosterone levels were similarly increased in high KCl and high KB/C groups (Figure S1b), although relative urinary K+ excretion, defined as K+/creatinine ratio, was somewhat greater in high KCl group (Figure S1c). Urinary pH was more acidic in high KCl group and more alkalic in high KB/C group, as expected (Figure S1d). As shown on the representative Western blot from whole kidney homogenates (Figure 1a) and the summary graph (Figure 1b), both diets significantly increased renal TRPV4 expression with the stimulatory effect being more pronounced in high KB/C group. It is important to note that TRPV4 appears as a duplet of bands with higher representing a heavy mannitol sugar glycosylation form (Wu et al., 2007; Xu et al., 2006). Drastically decreased TRPV4 glycosylation was observed during both ARPKD and ADPKD (Tomilin et al., 2018; Zaika et al., 2013). Interestingly, both high KCl and high KB/C diets led to a much stronger upregulation of the glycosylated (Figure 1c) than non‐glycosylated (Figure 1d) forms. Again, the effect was considerably stronger in high KB/C‐treated group. FIGURE 1: High KCl and high KB/C (bicarbonate/citrate) diet increases renal TRPV4 expression in ARPKD PCK453 rats. (a) Representative Western blot probed with anti‐TRPV4 antibodies from whole kidney lysates of PCK453 rats kept on regular (0.9%K+), high KCl (5%K+), and high KB/C (5%K+, bicarbonate: citrate as 4:1) diets for 1 month. Each line represents individual animal. The Ponceau red staining of the same nitrocellulose membrane demonstrating equal protein loading is shown below. “g” denotes upper glycosylated form of TRPV4. Summary graphs comparing total (b) glycosylated (c) and non‐glycosylated (d) forms of TRPV4 levels in PCK453 rats from the conditions in (a). The intensity values were normalized to the total signal of the respective lines in Ponceau red staining. Data are presented as mean ± SD. Numbers of each experimental groups (individual animals) are shown below. Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) between experimental groups shown with brackets on the top.* We next explored the underlying cause of a higher stimulatory effect of high KB/C diet on renal TRPV4 expression in PCK453 rats. For this, we monitored the specific sites of TRPV4 expression with immunofluorescent microscopy in the renal sections from PCK453 rats fed control, high KCl, and high KB/C diets. Since over $98\%$ of the renal tubules do not undergo transformation into cysts/dilations (Bergmann et al., 2018), their contribution to the overall renal TRPV4 expression (Figure 1a) will be dominant. TRPV4 was primarily localized to the apical and subapical regions of AQP2‐positive collecting ducts in control (Figure S2). We observed a very similar pattern in high KCl treated rats with TRPV4‐reporting signal being stronger using the same laser settings (Figure S2). Interestingly, we detected accumulation of the TRPV4‐reporting signal on both apical and basolateral sides of AQP2‐positive collecting ducts, as well as notable appearance of TRPV4 in AQP2‐negative cortical tubules (most likely proximal segments) on the basolateral side in rats fed high KB/C diet (Figure S2). We concluded that the increased basolateral TRPV4 presence in both collecting ducts and the proximal tubule explains the overall higher renal TRPV4 expression in high KB/C versus high KCl groups. ## High KCl and KB/C diets elicit opposite effects on TRPV4 activity in cyst cells of PCK453 rats While the observed upregulation of TRPV4 channel by dietary K+ supplementation is promising, it is imperative to augment channel activity to restore normal [Ca2+] i homeostasis in cystic cells. We have previously developed a method of mechanical isolation of cystic cell monolayers suitable for [Ca2+] i imaging from open cyst cavities in kidneys of PCK453 rats (Zaika et al., 2013). Thus, we next directly monitored TRPV4‐dependent Ca2+ influx upon application of a highly potent and selective channel agonist, GSK1016790A in freshly isolated cystic cells from animals fed control, high KCl, and high KB/C diets for 1 month. We showed previously that GSK1016790A has no effect on [Ca2+] i when TRPV4 was deleted (Berrout et al., 2012; Mamenko et al., 2017), implying that the agonist‐induced elevations in [Ca2+] i are indeed TRPV4‐dependent. Representative micrographs of [Ca2+] i before and following application of GSK1016790A (40 nM for 5 min) are shown in Figure 2a, and the respective time courses of [Ca2+] i changes for each condition are shown in Figure 2b. Figure S3 shows no apparent heterogeneity in [Ca2+] i responses in individual monolayers of cystic cells for all tested conditions. As summarized in Figure 2c, GSK1016790A induced moderate increases of [Ca2+] i by 117 ± 96 nM ($$n = 366$$ cells, $$n = 5$$ kidneys) in cystic cells from the control diet group. These values were significantly increased to 196 ± 144 nM ($$n = 214$$ cells, $$n = 4$$ kidneys; $p \leq 0.05$) in PCK453 rats fed high KCl. Unexpectedly, we observed much reduced GSK1016790A‐induced [Ca2+] i responses in cystic cells from high KB/C group: 42 ± 43 nM ($$n = 177$$ cells, $$n = 4$$ kidneys; $p \leq 0.05$). We have also stimulated TRPV4 by increasing fluid flow over the apical plasma membrane from 1.5 mL/min to 15 mL/min, as we similarly did before (Mamenko et al., 2017; Zaika et al., 2013). As shown on the averaged time course (Figure 2d) and the summary graph of the amplitude of flow‐induced [Ca2+] i elevations (Figure 2e), cystic cells only marginally responded to high flow in the control group and the effect was notably stronger in high KCl group, whereas no significant response to flow was detected in cystic cells from high KB/C group. Moreover, basal [Ca2+] i levels, another index of TRPV4 activity (Tomilin et al., 2018), were increased in high KCl diet versus control and high B/C diets: 73 ± 23 nM ($$n = 214$$ cells, $$n = 4$$ kidneys), 59 ± 20 nM ($$n = 366$$ cells, $$n = 5$$ kidneys; $p \leq 0.05$), and 55 ± 20 nM ($$n = 177$$ cells, $$n = 4$$ kidneys; $p \leq 0.05$), respectively. FIGURE 2: TRPV4‐dependent Ca2+ influx and flow‐induced [Ca2+] i signaling in cystic cells are stimulated by KCl but inhibited by high KB/C diet. (a) Shown on left is schematic representation of isolation of cystic cell monolayers using watchmaker forceps from kidneys of PCK453 rats suitable for [Ca2+] i imaging. Representative pseudocolor images of [Ca2+] i changes (blue—low and red—high) in freshly isolated cystic cell monolayers loaded with Ca2+‐sensitive dye Fura2 at the baseline and following 5 min application of 40 nM TRPV4 agonist, GSK1016790A from PCK453 rats kept on regular (0.9%K+), high KCl (5%K+), and high KB/C (5%K+, bicarbonate: citrate as 4:1) diets for 1 month. (b) The averaged time courses of [Ca2+] i changes upon application of 40 nM GSK1016790A (shown with the bar on top) in individual cystic cells within monolayer for the conditions in (a). The numbers of individual cells are shown. The data were obtained from at least 4 different monolayers isolated from at least 3 different rats for each group. Individual analysis for each tested monolayer is shown in Figure S3. (c) Summary graph comparing the magnitudes of GSK1016790A‐mediated [Ca2+] i elevations calculated as the difference in [Ca2+] i values before and after application of the TRPV4 agonist in individual cystic cells from the conditions in (a). The numbers of individual cells are shown. Bars and whiskers represent SE and SD, respectively. Mean and median values are denoted with lines (long and short). Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) between experimental groups shown with brackets on the top. (d) The averaged time courses of [Ca2+] i changes upon abrupt increase in perfusion flow from 1.5 mL/min to 15 mL/min (shown with the bar on top) in individual cystic cells within monolayer for the conditions in (a). (e) Summary graph comparing the magnitudes of flow‐induced [Ca2+] i elevations calculated as the difference in [Ca2+] i values before and at the end of high flow application in individual cystic cells from the conditions in (a). The numbers of individual cells are shown. Bars and whiskers represent SE and SD, respectively. Mean and median values are denoted with lines (long and short). Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) between experimental groups shown with brackets on the top. Overall, our current results show that while both high KCl and high KB/C diets augment renal TRPV4 expression, high KCl increases TRPV4 activity, whereas paradoxically high KB/C decreases channel activity in cystic cells of PCK453 rats. ## TRPV4 activity in cystic cells exhibits inverse correlation with cystogenesis and renal injury marker in PCK453 rats We next investigated how augmented TRPV4 activity in cystic cells during high KCl diet and impaired TRPV4 activity during high KB/C diet contributes to ARPKD progression at the whole kidney level. As summarized in Figure 3a, 1 month treatment with high KCl diet significantly decreased kidney to total bodyweight ratio indicative of reduced cystogenesis, when compared to the control group fed regular diet. To elucidate a potential role of TRPV4, PCK453 rats were similarly fed with either regular or high KCl diet on the background of an orally active TRPV4 antagonist, GSK2193874 (28 μg/kgBW). Systemic TRPV4 blockade had a tendency to increase kidney‐to‐total bodyweight ratio during regular diet versus control (Figure 3a). Importantly, the beneficial effect of high KCl diet was reversed upon concomitant treatment with GSK2193874 leading to increased kidney‐to‐total bodyweight ratio. Similarly, 1‐month treatment with high KB/C diet (low TRPV4 activity in cystic cells) led to a significant increase in kidney‐to‐total bodyweight ratio, suggesting an accelerated cystogenesis (Figure 3a). Of note, systemic administration of TRPV4 agonist, GSK1016790A (26 mg/kgBW) did not significantly change the ratio when compared to the condition of high KB/C diet. This is consistent with only a mild stimulatory effect of GSK1016790A on TRPV4‐dependent Ca2+ influx in cystic cells shown in Figure 2. Overall, these results support the notion that the effects of dietary K+ supplementation on reduction in the kidney‐to‐bodyweight ratio depend on the magnitude of TRPV4 activity in cystic cells but not on total TRPV4 expression in PCK453 rats. It is also important to mention that the prolonged consumption of either high KCl or high KB/C led to similar decreases in total bodyweight of the tested animals compared to that kept on regular diet (Figure S4). In turn, this would lead to underestimation of the high KCl condition due to the considerably lower denominator in the ratio. At the same time, neither GSK2193874 (TRPV4 blocker) nor GSK1016790A (TRPV4 activator) had a significant effect on total bodyweight (not shown). FIGURE 3: Regulation of TRPV4 activity by K+ intake affects renal morphology in PCK453 rats. (a) Summary graph of the kidney‐to‐total bodyweight ratio in PCK453 rats fed regular (0.9%K+), high KCl (5%K+) diets in the absence and presence of TRPV4 antagonist, GSK2193874 (highlighted in red) in drinking water (28 μg/kgBW, estimated on‐cell concentration of 40 nM), high KB/C (5%K+, bicarbonate: citrate as 4:1) diet, and high KB/C with TRPV4 activator, GSK1016790A (highlighted in green) in drinking water (26 μg/kgBW, estimated on‐cell concentration of 40 nM) for 1 month. Data are presented as mean ± SD. Numbers of each experimental groups (individual animals) are shown below. Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) versus regular diet as showed with brackets on the top. (b) Representative kidney sections of PCK453 rats from the conditions in A processed with trichrome staining kit to quantify morphology/cystogenesis and collagen fiber accumulation (fibrosis, blue color). “L” represents a large cyst, whereas “S” shows small cystic dilations.* We further assessed how interventions shown in Figure 3a affect renal cystogenesis, using renal sections from PCK453 rats. As shown in Figure 3b, large cysts were visible primarily in the medullary area, whereas much smaller dilations were rarely present in the medulla and cortex of control group, as expected for 2 months old PCK453 rats. All cysts were positive for AQP2 (see further), which is consistent with their collecting duct origin, as is expected for ARPKD (Bergmann et al., 2018). Figure 4 contains quantitative assessment of the total kidney section area (Panel A), cystic area (Panel B), the number of large (area > 2 mm2) cysts per section (Panel C), and small (0.2 mm2 < area < 2 mm2) dilations (Panel D). Renal sections from animals on high KCl diet had notably fewer cysts (Figure 3b). Specifically, the cystic area was reduced from $10.2\%$ ± $3.1\%$ in control group to $4.7\%$ ± $3.3\%$ upon KCl ($$p \leq 0.002$$; Figure 4b). This was attributed to the significantly decreased number of large cysts (Figure 4c) and small dilations (Figure 4d). In contrast, inhibition of TRPV4 with GSK2193874 markedly augmented cystogenesis similarly on both regular and high KCl diet (Figure 3b). Thus, the cystic areas were increased to $14.1\%$ ± $3.1\%$ ($$p \leq 0.009$$) and $17.2\%$ ± $8.7\%$ ($$p \leq 0.016$$) in GSK2193874 and high KCl + GSK2193874 groups, respectively. The total kidney section areas were also significantly increased in both groups (Figure 4a). The average number of large cysts was increased in GSK2193874 and high KCl + GSK2193874 (Figure 4c), whereas the number of small dilations was augmented only in renal sections from KCl + GSK2193874 group (Figure 4d). Similarly, high KB/C diet alone and in combination with GSK1016790A led to a significantly greater cystogenesis with cystic area being $14.4\%$ ± $3.1\%$ ($$p \leq 0.009$$) and $14.5\%$ ± $1.3\%$ ($$p \leq 0.0006$$), respectively (Figure 4b). Most notably, this was accompanied with the appearance of a large number of small dilations in the cortical area (Figure 3b and 4d). As shown in Figure S5, these dilations were AQP2‐positive suggesting that they were developed from the cortical collecting ducts and/or connecting tubules and not from other tubular segments. Despite apparent cystogenesis, the extent of renal fibrosis (assessed using Trichrome staining kit, as exemplified in Figure 3b) was minimal (<$0.3\%$ of the total area) in all tested groups (Figure 5a). This is consistent with fibrosis being significant in 18 week old PCK453 rats and much less in 8 weeks, as used in the current study (Mason et al., 2010). However, urinary KIM1 levels were significantly elevated in high KCl + GSK2193874, high KB/C, and high KB/C + GSK1016790A groups (Figure 5b) pointing to an augmented renal injury in the conditions associated with low TRPV4 activity. FIGURE 4: TRPV4 activity inversely correlates with renal cystogenesis in PCK453 rats. Summary graphs of total kidney section area (a), cystic area (b), number of large cysts per section (c), and number of small dilations per section (d) in PCK453 rats fed regular (0.9%K+), high KCl (5%K+) diets in the absence and presence of TRPV4 antagonist, GSK2193874 (highlighted in red) in drinking water (28 μg/kgBW, estimated on‐cell concentration of 40 nM), high KB/C (5%K+, bicarbonate: citrate as 4:1) diet, and high KB/C with TRPV4 activator, GSK1016790A (highlighted in green) in drinking water (26 μg/kgBW, estimated on‐cell concentration of 40 nM) for 1 month. Data are presented as mean ± SD. Numbers of each experimental groups are shown below. Kidney sections from at least 4 different animals were used for each tested group. Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) versus regular diet as shown with brackets on the top.* FIGURE 5: Impaired TRPV4 activity correlates with increased renal injury marker in PCK453 rats. Summary graphs of percent of fibrotic area per kidney section similar to shown in Figure 3b (a) and KIM1 to creatinine ratio in urine (b) in PCK453 rats fed regular (0.9%K+), high KCl (5%K+) diets in the absence and presence of TRPV4 antagonist, GSK2193874 (highlighted in red) in drinking water (28 μg/kgBW, estimated on‐cell concentration of 40 nM), high KB/C (5%K+, bicarbonate: citrate as 4:1) diet, and high KB/C with TRPV4 activator, GSK1016790A (highlighted in green) in drinking water (26 μg/kgBW, estimated on‐cell concentration of 40 nM) for 1 month. Data are presented as mean ± SD. Numbers of each experimental groups (kidneys or urinary samples from different animals) are shown below. Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) versus regular diet as shown with brackets on the top.* ## Functional TRPV4 activity controls cAMP levels in cystic cells We next explored the mechanism of inversed relation between TRPV4 activity and renal cystogenesis in PCK453 rats. The compelling accumulated evidence demonstrates the central role of augmented cAMP levels in driving epithelial cell transformation to malicious cystic phenotype and enhanced proliferation in a B‐Raf/ERK‐dependent manner (Belibi et al., 2004; Wallace, 2011; Yamaguchi et al., 2003, 2006). Thus, we monitored subcellular AQP2 distribution, as an index of cAMP levels, in cystic cells with immunofluorescent microscopy in renal sections from control, high KCl (increased TRPV4 activity), and high KB/C (impaired TRPV4 activity) fed PCK453 rats (Figure 6a). As shown in the representative high magnification images and the respective line‐scan analysis of the intensity of AQP2‐reporting fluorescent signal, the maximum is observed in the apical and subapical regions of cystic cells from control animals pointing to elevated cAMP levels, known to promote apical AQP2 translocation (Figure 6b). Treatment with KCl diet resulted in apparent intracellular retention of AQP2 suggesting lower cAMP levels with the dispersion (decrease by $50\%$ from maximum) being significantly increased from 1.51 ± 0.46 to 2.13 ± 0.61 μm ($p \leq 0.05$) in control and high KCl conditions, respectively (Figure 7). In contrast, even stronger accumulation of AQP2‐reporting signal at the apical membrane was seen in rats fed high KB/C diet (Figure 6b). The dispersion was significantly reduced to 1.19 ± 0.29 μm ($p \leq 0.05$) versus control condition pointing to further elevations in cAMP levels in this case (Figure 7). In contrast, line‐scan analysis of non‐dilated collecting ducts in the same sections showed comparable more diffuse intracellular distribution of the AQP2‐reporting signal in all tested conditions (Figure 6c). The dispersion values were 1.86 ± 0.82, 2.02 ± 0.61, and 2.28 ± 0.82 μm for control, high KCl, and high KB/C, respectively. As shown in the summary graph in Figure 7, there were significant differences in AQP2 distribution between non‐dilated collecting duct and cystic cells in control, which was further exacerbated in KB/C condition. In contrast, the distribution of AQP2‐reporting signal was very similar between non‐transformed collecting duct and cystic cells in high KCl treated PCK453 rats, which is consistent with reduced cAMP levels when TRPV4 activity is higher. Overall, our results in Figures 6 and 7 demonstrate an inversed correlation between TRPV4 activity and cAMP‐dependent AQP2 translocation in cystic cells which would explain accelerated/decelerated cystogenesis in PCK453 rats fed high KB/C and high KCl diets, respectively. FIGURE 6: TRPV4 activity regulates subcellular AQP2 distribution in cystic cells of PCK453 rats. (a) Representative confocal images showing AQP2 (pseudocolor red) distribution in kidney sections of PCK453 rats fed regular (0.9%K+), high KCl (5%K+), and high KB/C (5%K+, bicarbonate: citrate as 4:1) diets for 1 month. Nuclear Dapi staining is shown with pseudocolor blue. Areas with cystic (1) and non‐dilated collecting duct (2) are shown below at higher magnification. The averaged intensities of AQP2‐reporting fluorescent signals around the apical area in cystic (b) and non‐dilated collecting duct (c) cells from the conditions in (a). For each individual cell the fluorescent signals were normalized to their corresponding maximal value. FIGURE 7: TRPV4 activity is inversely related to cAMP levels in cystic cells of PCK453 rats. Summary graph comparing dispersion (decrease by 50% from maximum) of AQP2‐reporting signal in non‐dilated collecting duct versus cystic cells in kidney sections of PCK453 rats fed regular (0.9%K+), high KCl (5%K+), and high KB/C (5%K+, bicarbonate: citrate as 4:1) diets for 1 month. Bars and whiskers represent SE and SD, respectively. Mean and median values are denoted with lines. Numbers of each experimental groups are shown below. Kidney sections from at least 4 different animals were used for each tested group. Significant changes (p < 0.05, one‐way ANOVA with post hoc Tukey test) between groups shown with brackets on the top.* ## DISCUSSION This study provides strong causal evidence of how functional activity of the mechanosensitive TRPV4 channel affects ARPKD progression at both cellular and whole kidney levels. Specifically, we demonstrate that physiologically relevant stimulation of TRPV4 expression and activity in cystic cells by high KCl diet increases basal [Ca2+] i levels, partially restores mechanosensitivity, and causes intracellular AQP2 retention (reduced cAMP levels) to markedly slow the development and growth of renal cysts in PCK453 rats (Figure 8, right panel). In contrast, impaired TRPV4 activity in cystic cells either due to pharmacological antagonism with GSK2193874 or as a result of treatment with high KB/C diet favors the cAMP‐dependent phenotype thus greatly accelerating ARPKD progression and renal injury (Figure 8, left panel). FIGURE 8: Principal scheme showing the significance of TRPV4 activity in cystic cells on ARPKD progression in PCK453 rats. It is commonly accepted that PKD is associated with abnormally low basal [Ca2+] i levels and inability to sense mechanical stimuli by cystic cells irrespective whether this occurs due to bending of the antenna‐like primary cilium (Nauli et al., 2003) or by an acute stretch of the plasma membrane (Delling et al., 2016). The disruption of the activity of multi‐protein complex, which includes atypical G‐protein coupled receptor PC1, Ca2+‐permeable channel PC2 (both mutated in ADPKD), and fibrocystin (mutated in ARPKD; Rossetti et al., 2007; Ward et al., 2002) reportedly accounts for the distorted [Ca2+] i homeostasis in cystic cells (Bergmann et al., 2018; Ghata & Cowley, 2017). However, PC2 channel does not respond to mechanical stimulation (Du et al., 2012; Kottgen et al., 2008), which argues for a potential involvement of additional mechanosensitive components in the complex. Indeed, PC2‐TRPV4 functional hetero‐tetramers (presumably in 2:2 stoichiometry (Stewart et al., 2010)) have been reported in renal epithelial cells with their activity being compromised in cystic cells of both ADPKD and ARPKD (Tomilin et al., 2018; Zaika et al., 2013; Zhang et al., 2013). On the one hand, it is plausible to propose that the deficient mechanosensitive Ca2+ influx underlies a switch in the cellular responses to cAMP from anti‐proliferative in normal tubule cells to mitogenic signaling involving over‐activation of the via B‐Raf/ERK cascade in cystic cells (Gattone et al., 2003; Yamaguchi et al., 1997, 2004, 2006). On the contrary, genetic deletion of TRPV4 abolishes flow‐induced [Ca2+] i responses and decreases basal [Ca2+] i levels but does not lead to cystogenesis (Berrout et al., 2012; Kottgen et al., 2008) suggesting that this is not a requirement for transformation to a cystic phenotype. Having said that, we show that stimulation of TRPV4 activity in cystic cells with high KCl diet (Figure 2) leads to a markedly diffused intracellular distribution of AQP2 indicating lower cAMP levels, when compared to that in control (Figure 6a,b). Moreover, reductions in TRPV4‐dependent Ca2+ influx induced by the high KB/C diet promote apical AQP2 accumulation (higher cAMP) in cystic cells. At the whole kidney level, this correlates with significantly decelerated and accelerated renal cystogenesis, respectively (Figures 3 and 4). Of note, we previously demonstrated that non‐dilated collecting ducts of PCK453 rats have nearly normal mechanosensitivity and intact TRPV4 activity (Zaika et al., 2013). Consistently, our line‐scan analysis shows a much broader (compared to the observed in cystic epithelium) intracellular AQP2 distribution in collecting ducts irrespective of the dietary regimen (Figure 6a,c). This strongly implies that TRPV4 activity specifically in cyst cells is a critical factor which determines the rate of cystogenesis. Indeed, we observed appearance of multiple small cortical dilations in PCK453 rats of 8 weeks old upon treatment with high KB/C diet or TRPV4 antagonist GSK2193874 (Figure 3b), which corresponds to the disease stage of considerably older (18–25 weeks) rats kept in standard conditions (Mason et al., 2010). It is unlikely that the impaired TRPV4 function in cystic cells is associated with decreased TRPV4 expression. We have previously showed comparable levels of the channel in primary cultured human ADPKD and non‐ADPKD cells (Tomilin et al., 2018) and only a mild reduction of $20\%$–$30\%$ in the kidneys of ARPKD PKC453 versus control S/D rats (Zaika et al., 2013). This cannot account for the observed $50\%$–$80\%$ decreases in TRPV4 single channel activity and TRPV4‐mediated Ca2+ influx in cystic cells in both cases. Strikingly, a dramatic decrease in TRPV4 glycosylation has been detected during both ADPKD and ARPKD (Tomilin et al., 2018; Zaika et al., 2013). The post‐translational modification of TRPV4 with high mannose sugars occurs very close to the pore region (Wu et al., 2007; Xu et al., 2006), which can have an immediate (acute) effect on channel gating. Indeed, glycosylation deficient TRPV4 mutants exhibit lower basal activity and disrupted stimulation by mechanical stimuli (Lamande et al., 2011). Furthermore, pharmacological blockade of glycosylation with tunicamycin decreases TRPV4 activity to the levels observed in cystic ADPKD cells (Tomilin et al., 2018). Notably, both high KCl and high KB/C diets have much stronger stimulatory effect on the abundance of the glycosylated form of TRPV4 in the kidney of PCK453 rats (Figure 1). Since we detected an upregulation of TRPV4‐reporting signal with immunofluorescent microscopy not only in cysts but also in non‐dilated collecting ducts as well as in proximal tubules for high KB/C condition (Figure S2), it is quite possible that the increase in TRPV4 glycosylation is predominantly attributable to the normal (i.e., non‐cystic) epithelium during both diets (Figure 1). This would explain the puzzling reduction of TRPV4 activity in freshly isolated cystic monolayers from high KB/C treated PCK453 rats (Figure 2). Moreover, channel glycosylation could also be quite low in cystic cells from high KCl treated mice because the detected TRPV4‐dependent Ca2+ influx (Figure 2b) and mechanosensitive [Ca2+] i responses (Figure 2d) are only at approximately $50\%$ of the values existing in non‐dilated collecting ducts of PCK453 rats (Zaika et al., 2013). Currently, we do not know how high KB/C diet decreases TRPV4 activity in cystic cells. TRPV4 is known to have a relatively weak pH dependence with channel activity being stimulated by low extracellular pH (Mizuno et al., 2003). Thus, it is possible that high KB/C diet‐induced alkalization would exhibit an inhibitory effect on TRPV4. We observed a much reduced TRPV4‐dependent Ca2+ influx in cystic cells from rats on high KB/C diet compared to controls (Figure 2) despite higher overall TRPV4 expression (Figure 1) and seeming membrane localization (Figure S2). This suggests that the channel is locked in non‐active (or low active) state with increased abundance representing a compensatory mechanism. In fact, we previously demonstrated that TRPV4 trafficking is upregulated by cAMP‐PKA mechanism in collecting duct cells (Mamenko, Zaika, Boukelmoune, et al., 2013), which is consistent with predominantly membrane localization of TRPV4 in cystic cells possessing high cAMP levels. However, this cAMP‐dependent TRPV4 translocation does not convert into augmented TRPV4‐dependent Ca2+ influx with channel requiring stimulation of protein kinase C to become active (Mamenko, Zaika, Boukelmoune, et al., 2013). Of interest, recent study demonstrates that expression of PKCζ isoform is downregulated in patients with autosomal dominant PKD with its restoration correlating with reduced cystogenesis in multiple PKD mouse models (Akbari et al., 2022). Thus, it is plausible to propose that this improvement could be at least partially TRPV4‐dependent. Further studies are necessary to test this hypothesis. Increased dietary NaCl intake has been commonly associated with acceleration in PKD progression (Bergmann et al., 2018). Furthermore, treatment with both high and low NaCl diets led to an acceleration in renal cyst development in PCK453 rats (Ilatovskaya et al., 2019). Thus, the contrasting effects of NaCl and KCl diets on cystogenesis in the absence of any beneficial effect of changes in dietary Cl− between low and high salt diets imply that the observed improvement in renal cystogenesis in PCK453 rats fed high KCl diet are driven by elevated K+. At the same time, the observation that high KB/C diet accelerates cystogenesis while high KCl diet reduces the development of cysts in PCK453 rats is somewhat surprising (Figure 3). The previously published evidence suggests that acidification and azotemia accelerates renal cyst growth in an ADPKD model, Han:SPRD rats (Cowley et al., 1996). In contrast, alkalization upon supplementation with either bicarbonate or citrate reported to protect against decline in GFR and to some extent reduced renal cystic burden in this model (Tanner & Tanner, 2000; Torres et al., 1994). In fact, we paired high K+ with both citrate and bicarbonate in this study in anticipation to obtain an additional improvement of renal function beyond that seen with high KCl diet. It turns out that our initial hypothesis led to opposite results with high KB/C treatment causing markedly larger kidney weight (Figure 3a) and number of cysts in renal sections (Figure 4) despite prominent alkalization of urinary pH (Figure S1). It is likely that the underlying genetic defect could account for the discrepancies in the experimental outcomes between the current study and the aforementioned published evidence. Han:SPRD rats are not a homologous model of human ADPKD having a missense mutation in Anks6 gene (Nagao et al., 2010), which causes Nephronophthisis (NPHP)‐like renal pathology in humans (Taskiran et al., 2014). The cystic disease in Han:SPRD rats has dominant inheritance and is characterized by a rapid development of cortical cysts with $75\%$ of them originating from the proximal tubule (Schafer et al., 1994). In contrast, a homologous ARPKD model, PCK453 rat, develops cysts almost exclusively in the collecting duct system (Mason et al., 2010). Interestingly, no beneficial effects of high dietary citrate were found in pcy/pcy mice also having recessive inheritance of the disease (Tanner & Tanner, 2005). In fact, an increased kidney‐to‐bodyweight ratio was observed when potassium/citrate was given in drinking water (Tanner et al., 2000). This is consistent with the effects of high KB/C diet on kidney size shown in Figure 3a. Importantly, TRPV4 antagonist, GSK2193874, recapitulates the detrimental effect of high KB/C diet on kidney structure (i.e., augmented cystogenesis) suggesting an important role of the channel dysfunction in this case (Figures 3, 4, 5). Thus, the opposite effects of dietary citrate in Han:SPRD and PCK453 rats likely reflect the different function and localization of TRPV4 in proximal tubule and collecting duct. In the proximal tubule, TRPV4 is predominantly expressed on the basolateral side where it is not positioned to directly sense changes in tubular flow and regulate basal [Ca2+] i levels, but rather serve as a pressure sensor of the filtration rate (Gualdani et al., 2020; Janas et al., 2016). In summary, we demonstrate that TRPV4 function in cystic cells is an important determinant of growth of the collecting duct derived cysts in ARPKD rat model. Currently, we do not have direct experimental evidence showing that pharmacological or dietary (high KCl diet) stimulation of TRPV4 also attenuates the progression of clinically prevalent ADPKD, but it would likely be the case considering the collecting duct origin of the majority of cysts (Bergmann et al., 2018) and similar mechanisms of TRPV4 dysfunction in both PKD types (Tomilin et al., 2018). TRPV4 is expressed in different tissues and organs with the highest abundance in the kidney (Liedtke et al., 2000). This would provide at least a certain level of specificity upon systemic targeting TRPV4 activity with pharmacology. Previously, we provided a proof‐of‐principle evidence that long‐term administration of a low dose of TRPV4 agonist, GSK1016790A slowed renal cystogenesis in PCK453 rats with no apparent adverse effects (Zaika et al., 2013). Here, we boost this argument by demonstrating that physiologically relevant stimulation of TRPV4 activity with high KCl diet is an effective tool to counteract cyst formation. It should be noted that this maneuver stimulates channel expression/activity in the kidney, but not in other tissues endogenously expressing TRPV4. Thus, we posit that such strategy could be used alone or in combination with other drugs, such as tolvaptan, as an effective treatment of ARPKD and likely ADPKD in clinic. ## AUTHORS CONTRIBUTIONS Conceptualization: Oleh Pochynyuk; investigation: Kyrylo Pyrshev, Anna Stavniichuk, Viktor N. Tomilin, Naghmeh Hassanzadeh Khayyat, Guohui Ren, Oleg Zaika, Mykola Mamenko, Oleh Pochynyuk; formal analysis: Kyrylo Pyrshev, Anna Stavniichuk, Viktor N. Tomilin, Naghmeh Hassanzadeh Khayyat, Guohui Ren, Mariya Kordysh, Oleg Zaika, Oleh Pochynyuk; funding acquisition: Kyrylo Pyrshev, Oleh Pochynyuk; writing original draft: Oleh Pochynyuk; editing: Kyrylo Pyrshev, Anna Stavniichuk, Viktor N. Tomilin, Naghmeh Hassanzadeh Khayyat, Guohui Ren, Mariya Kordysh, Oleg Zaika, Mykola Mamenko, Oleh Pochynyuk. ## CONFLICT OF INTEREST STATEMENT The authors have no conflicts of interest to report. ## ETHICS STATEMENT Animal use and welfare adhered to the NIH Guide for the Care and Use of Laboratory Animals following protocols reviewed and approved by the Animal Welfare Committee of the University of Texas Health Science Center at Houston. ## References 1. Akbari M., West J. D., Doerr N., Kipp K. R., Marhamati N., Vuong S., Wang Y., Rinschen M. M., Talbot J. J., Wessely O., Weimbs T.. Restoration of atypical protein kinase C zeta function in autosomal dominant polycystic kidney disease ameliorates disease progression. 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--- title: Exercise training modifies xenometabolites in gut and circulation of lean and obese adults authors: - Mikaela C. Kasperek - Lucy Mailing - Brian D. Piccolo - Becky Moody - Renny Lan - Xiaotian Gao - Diego Hernandez‐Saavedra - Jeffrey A. Woods - Sean H. Adams - Jacob M. Allen journal: Physiological Reports year: 2023 pmcid: PMC10031301 doi: 10.14814/phy2.15638 license: CC BY 4.0 --- # Exercise training modifies xenometabolites in gut and circulation of lean and obese adults ## Abstract Regular, moderate exercise modifies the gut microbiome and contributes to human metabolic and immune health. The microbiome may exert influence on host physiology through the microbial production and modification of metabolites (xenometabolites); however, this has not been extensively explored. We hypothesized that 6 weeks of supervised, aerobic exercise 3×/week ($60\%$–$75\%$ heart rate reserve [HRR], 30–60 min) in previously sedentary, lean ($$n = 14$$) and obese ($$n = 10$$) adults would modify both the fecal and serum xenometabolome. Serum and fecal samples were collected pre‐ and post‐6 week intervention and analyzed by liquid chromatography/tandem mass spectrometry (LC–MS/MS). Linear mixed models (LMMs) identified multiple fecal and serum xenometabolites responsive to exercise training. Further cluster and pathway analysis revealed that the most prominent xenometabolic shifts occurred within aromatic amino acid (ArAA) metabolic pathways. Fecal and serum ArAA derivatives correlated with body composition (lean mass), markers of insulin sensitivity (insulin, HOMA‐IR) and cardiorespiratory fitness (V˙O2max), both at baseline and in response to exercise training. Two serum aromatic microbial‐derived amino acid metabolites that were upregulated following the exercise intervention, indole‐3‐lactic acid (ILA: fold change: 1.2, FDR $p \leq 0.05$) and 4‐hydroxyphenyllactic acid (4‐HPLA: fold change: 1.3, FDR $p \leq 0.05$), share metabolic pathways within the microbiota and were associated with body composition and markers of insulin sensitivity at baseline and in response to training. These data provide evidence of physiologically relevant shifts in microbial metabolism that occur in response to exercise training, and reinforce the view that host metabolic health influences gut microbiota population and function. Future studies should consider the microbiome and xenometabolome when investigating the health benefits of exercise. Xenometabolomic responses to exercise were different between lean and obese adults, despite some shared adaptations. Microbial metabolites associated with body composition, markers of insulin sensitivity, and cardiorespiratory fitness, both at baseline and in response to exercise training. These data suggest microbial metabolites should be carefully considered as contributors to exercise adaptations and human health. ## INTRODUCTION The gut microbiota is a malleable ecosystem of bacteria and other microorganisms that are major contributors to human health. Gut microbes influence host physiology partially through the production and modification of a wide range of bioactive metabolites. In mammals, it has been estimated that ~$10\%$ of all circulating metabolites are microbially derived (Wikoff et al., 2009). These “non‐host” synthesized molecules are part of a broad class of “xenometabolites” (Campbell et al., 2014; Mercer et al., 2020) and include bioactive metabolites derived from the fermentation of carbohydrates, amino acids, bile acids, and lipids that modify host physiology. Because disturbances to gut microbiota metabolism are associated with a wide variety of human health conditions, it is imperative to understand how lifestyle factors—including exercise—can contribute to microbial metabolic output and signaling. Participation in regular, moderate exercise reduces the incidence of metabolic and inflammatory disease, including gastrointestinal diseases such as inflammatory bowel disease and colorectal cancer (Bilski et al., 2018; Lambdin et al., 2018; Ng et al., 2007). Physical activity is known to impact gut microbiome composition and function (Allen et al., 1985; 2018; Anhe et al., 2022; Barton et al., 2017; Campbell et al., 2016; Matsumoto et al., 2008), which may potentially confer some of the health benefits of exercise. For instance, a recent cross‐sectional study has shown that both fecal microbiome richness and microbe‐derived metabolites such as butyrate were significantly associated with cardiorespiratory fitness (V̇O2peak) (Estaki et al., 2016). In addition, women with high aerobic capacity, when compared to age‐ and body mass‐matched women with low fitness, displayed a significant increase in plasma concentrations of a secondary bile acid, lithocholic acid, following an oral glucose tolerance test (OGTT) (Maurer, 1985). In contrast, post‐OGTT serum total conjugated bile acids were only transiently increased in more fit women whereas in less fit women levels rose and remained high (Maurer, 1985). While longitudinal studies are relatively sparse in humans, exercise training appears to affect the microbiome function over time in previously sedentary individuals. For instance, our previous work showed that exercise training increases fecal short‐chain fatty acids (SCFAs) (Allen et al., 2018): microbe‐derived xenometabolites that have anti‐inflammatory, satiety, and insulin‐sensitizing effects (Cox et al., 2009; Larraufie et al., 2018; Zarrinpar et al., 2018). Additional evidence from fecal transfer treatments showed that exercise‐induced increases in SCFAs may contribute to improvements in insulin sensitivity in pre‐diabetic subjects (Liu et al., 2020). Furthermore, following a weight loss and fitness intervention in previously sedentary, insulin‐resistant women, a variety of plasma xenometabolite patterns were shifted despite the fact that participants were consuming the same foods leading up to the metabolomics studies (Campbell et al., 2014; Zhang, 2017). Because previous exercise training studies have primarily been limited to targeted pathway and metabolite analysis, controlled longitudinal human exercise studies focused on global xenometabolic responses are needed. Obesity rates are growing worldwide and are associated with disturbances in gut microbiota metabolism (Wang et al., 2021). In mice, 6 weeks of high‐intensity exercise training has been shown to directly oppose many of the obesity‐related changes in the gut microbiota and microbial metabolism (Denou et al., 2016). In particular, regular exercise training increased the predicted genetic capacity related to glycan biosynthesis and metabolism, carbon fixation, and the TCA cycle in the fecal microbiota. While a defined microbial signature of obesity has not yet been identified in humans, there is evidence of altered metabolic output in the gut microbiome of obese humans (de Vadder & Mithieux, 2018). Similarly, the success in weight loss interventions, including calorie restriction and bariatric surgery, have been tightly linked to changes in gut microbially derived xenometabolites, including secondary bile acids and SCFAs (Heianza et al., 2018; Ocana‐Wilhelmi et al., 2021; Seyfried et al., 2021). It is thus vital to understand whether obesity‐associated metabolites derived from the microbiome are responsive to exercise training interventions in humans, since this might contribute to the known metabolic health benefits of exercise in this population. Our previous data revealed an orthogonal microbiome response to exercise training in humans that was dependent on obesity status (Allen et al., 2018). Most notably, lean participants exhibited a more substantial increase in SCFAs than obese participants in response to exercise training, suggesting that obesity may influence metabolic responses to exercise training. However, to date, no studies have determined the effect of obesity status on global xenometabolite responses to exercise training. In the present study, we built upon our previous research to analyze how a 6 week, supervised aerobic exercise program modifies the fecal and serum metabolome of previously sedentary, lean and obese humans. Using untargeted metabolomic profiles, we sought to understand the changes in the global metabolome (including the xenometabolome) in the gut and circulation in response to exercise training. ## Participants Serum and fecal biospecimens from a previously published study were used in this analysis (Allen et al., 2018). Participants ($$n = 24$$ total) were age 20 to 45 year, had a BMI <25 kg/m2 (lean; $$n = 15$$) or a BMI >30 kg/m2 (obese; $$n = 9$$), and were previously sedentary as defined as ≤30 min of moderate‐ or high‐intensity exercise per week and ≤10 aggregate Godin‐Shepard Leisure Time Physical Activity Questionnaire (GSLTQ) score (Table 1). Participant medical history and medication use were assessed by questionnaire. Subjects who qualified for the study were free of metabolic and gastrointestinal disease, not pregnant or lactating, not taking medications that would impact bowel function, and had not taken antibiotics for at least 3 months prior to beginning the study. TABLE 1 \| Unnamed: 0 \| Baseline \| Baseline.1 \| Post‐exercise intervention \| Post‐exercise intervention.1 \| \| --- \| --- \| --- \| --- \| --- \| \| \| Lean (n = 15); 7 female \| Obese (n = 9); 7 female \| Lean (n = 15); 7 female \| Obese (n = 9); 7 female \| \| Age (year) \| 23.64 ± 0.91 \| 28.10 ± 2.72 \| \| \| \| BMI (kg m−2) \| 21.56 ± 0.73 \| 33.76 ± 1.98* \| 22.11 ± 0.91 \| 35.15 ± 3.48* \| \| Weight (kg) \| 67.34 ± 3.63 \| 92.58 ± 6.99* \| 68.11 ± 3.90 \| 91.31 ± 6.70* \| \| Body fat % \| 23.84 ± 1.03 \| 36.85 ± 1.71* \| 23.26 ± 0.96 \| 35.69 ± 1.73,* \| \| Lean mass % \| 73.78 ± 1.02 \| 60.96 ± 1.71* \| 74.33 ± 0.94 \| 62.27 ± 1.74* \| \| Bone density (g cm−2) \| 1.12 ± 0.02 \| 1.18 ± 0.03 \| 1.12 ± 0.02 \| 1.19 ± 0.03 \| \| Absolute V˙O2max \| 2.62 ± 0.19 \| 2.77 ± 0.25 \| 3.07 ± 0.20 \| 2.96 ± 0.19 \| \| Relative V˙O2max \| 40.15 ± 1.33 \| 30.49 ± 2.09 \| 45.28 ± 1.56 \| 34.17 ± 2.29,* \| \| Glucose (mg dL−2) \| 88.78 ± 2.85 \| 89.66 ± 3.19 \| 80.13 ± 3.64 \| 83.46 ± 6.31 \| \| Insulin (mIU mL−2) \| 9.90 ± 0.51 \| 13.46 ± 1.48* \| 9.75 ± 0.57 \| 12.12 ± 1.05 \| \| HOMA‐IR \| 2.16 ± 0.11 \| 3.02 ± 0.42 \| 1.94 ± 0.16 \| 2.55 ± 0.35 \| ## Exercise training protocol The exercise intervention consisted of supervised 30 to 60 min, moderate‐to‐vigorous intensity ($60\%$–$75\%$ of HR reserve [HRR]) aerobic exercise sessions. Subjects exercised 3×/week for 6 weeks and chose from a cycle ergometer or treadmill during each session. Training sessions for the first two weeks were 30–45 min at $60\%$ HRR and at week 3 were increased to 60 min at $60\%$ HRR. During weeks 4 to 6 of training, there was an increase in intensity of $5\%$ HRR per week, progressing up to $75\%$ HRR for 60 min during week 6. All participants were $100\%$ compliant in completing necessary requirements for the exercise portion of the study. The participants completed the study in two separate cohorts within a 6 month period in 2016. Study cohort was accounted for in all statistical modeling. ## Screening and diet control In consultation with a registered dietitian, participants designed a 3‐day food menu that consisted entirely of foods and drinks from a 7‐day dietary recall of detailed descriptions of the types and amounts of foods and beverages consumed. Participants were asked to follow this 3‐day menu before each fecal and blood collection to ensure lead‐in conditions were controlled and reflected typical dietary intake. In addition to the acute dietary control, subjects were instructed to maintain overall dietary patterns, including maintenance of alcoholic and caffeinated beverage consumption, and continuation of any dietary supplements that occurred before the study commenced. ## Biospecimen collection Fecal samples were collected at baseline and after 6 weeks of exercise training and were preceded by the individualized 3‐day diet control and were collected within 24 h of blood collection as previously described (Allen et al., 2018). Subjects were provided with fecal sample collection containers and were instructed to deliver samples to the laboratory within 30 min of defecation to ensure minimal degradation of volatile SCFAs. Once received, ~0.5 g of the sample was aliquoted for SCFA analysis and the rest was stored at −80°C until future analysis. Fasted (>/=10 h) blood samples were collected before 9:00 am at baseline and after the exercise intervention. ## Metabolomics Frozen serum and stool samples were shipped to the Arkansas Children's Nutrition Center (ACNC) overnight on dry ice and immediately stored at −80°C upon arrival. Full analytical details of the metabolomics methods have been previously published (Mercer et al., 2020; Piccolo et al., 2017) with slight modifications. Stool and serum samples were analyzed separately. A portion of stool sample for each participant was pulverized into powder using a SPEX CertiPrep 6750 freezer mill and then stored at −80°C until analysis. Powdered stool samples (~100 mg) were dried overnight under a slow nitrogen stream to remove water content and ~30 mg of the dried powder was homogenized in 500 μL $50\%$ aqueous methanol using a Precellys 24 homogenizer (Bertin Corp.) with 2.8 mm ceramic beads at 5300 rpm for two 30‐s cycles. Samples were then extracted in 1 mL of ice‐cold acetonitrile. Serum samples (100 μL) were extracted directly in methanol (1:4). Quality control (QC) samples were prepared by pooling equal volumes (10 μL each) of all extracted samples. Samples and QC extracts were then evaporated to dryness using a nitrogen gas evaporator. Dried extracts were reconstituted in $5\%$ aqueous methanol (150 μL for serum and 300 μL for fecal powder) containing internal standards [1 μg/mL Lorazepam, D6‐trans‐cinnamic acid, and D4 glycocholic acid (Sigma Aldrich, St. Louis, MO)]. Group pooled samples were made by mixing 10 μL of each reconstituted sample from both pre‐ and post‐exercise groups. Samples were placed in random sequence for analysis. A Dionex Ultimate 3000 UHPLC with a XSelect CSH C18 reversed phase column (2.1 × 100 mm, 2.5 μm; 49°C) was used for chromatography, followed by detection using a Q‐Exactive Hybrid Quadrupole‐Orbitrap mass spectrometer. Metabolites were eluted by use of the following step gradient at a flow rate of 0.4 mL/min: 0–2 min, $0\%$–$1\%$ solvent B; 2–6.5 min, $1\%$–$20\%$ solvent B; 6.5–11.5 min, $20\%$–$95\%$ solvent B; 11.5–13.5 min, $95\%$–$99\%$ solvent B; 13.5–16.5 min, $99\%$–$1\%$ solvent B; 16.5–20 min, $1\%$ solvent B; 20–21 min, $1\%$–$0\%$ solvent B; 21–22 min, $0\%$ solvent B (Solvent A is $0.1\%$ by volume formic acid in water and solvent B is $0.1\%$ by volume formic acid in acetonitrile). Injection volumes were set to 5 μL. All of the samples were analyzed by positive and negative electrospray ionization (ESI+/−) in full scan MS mode with the polarity switching. The QC sample was used to condition the system and was also injected between every 15 sample injections. Nitrogen as sheath, auxiliary, and sweep gas were set at 50, 13, and 3 U, respectively. Other conditions included the following: resolution, 70,000 full width at half maximum (FWHM); automatic gain control target, 3e6 ions; maximum injection time, 200 ms; scan range, 50–750 m/z; spray voltage, 3.50 kV; and capillary temperature, 320°C. ESI+/− data‐dependent MS–MS spectra were generated for QC pool samples as well as group pooled samples through the use of the following conditions: resolution, 17,500 FWHM; automatic gain control target, 1e5 ions; maximum injection time, 50 ms; isolation window, 4 Da; and NCE at 30. Data were acquired as full MS and data‐dependent MS2 using Xcalibur 4.0 software. ## Data processing and metabolite identification The acquired data set, composed of full MS and data‐dependent MS–MS raw files, was processed using Compound Discoverer 3.0 (Thermo) to perform peak alignment, peak picking, compound grouping, and quantification for each metabolite. Details of data workflow have been previously published (Piccolo et al., 2017) with a slight modification. The software parameters for detecting unknown compounds were 5 ppm mass tolerance for detection, $30\%$ intensity tolerance, 3 for the sensitivity and noise threshold, and 5e5 minimum peak intensity. Spectral information and retention times were matched to the library of authentic standards at the ACNC using MassList (accurate mass ± 5 ppm, RT ± 15 s). The ACNC authentic in‐house standard library (mzVault) contains 420 metabolites and is specifically enriched for known or putative xenometabolites; thus, the platform has been termed the XenoScan (Mercer et al., 2020). Further identification of metabolites was conducted with mzCloud and mzVault spectral libraries. Structurally identified metabolites were given the following ranking according to the standard published previously (Blazenovic et al., 2018; Schymanski et al., 2014): [1] accurate mass, retention time, and MS2 spectra matched to in‐house standard, [2] accurate mass and MS2 spectra matched to known standard (internal or cloud) without retention time matched. Metabolites that did not meet rankings 1 and 2 were removed from analysis, resulting in 189 and 103 total metabolites identified in stool and serum samples, respectively. ## Statistics Relative standard deviations (RSD) were calculated using QC replicates and metabolites with >$30\%$ RSD (Peterson, 2016) in the QC pool replicates were filtered from the analysis. This resulted in the removal of 10 and 12 metabolites in serum and stool, respectively. The effect of exercise training on the fecal and serum metabolome was assessed using a 10‐fold cross validated partial least squares discriminant analysis (PLS‐DA) with one latent variable using residuals from a linear mixed model to adjust for only BMI classification. Data were split into training and test (75/$25\%$ split), where model fit and feature selection was done entirely in the training data. Features were selected if their variable importance in projection (VIP) score was greater than 1 (Chong, 2005). These feature‐selected metabolites were then used to fit a reduced PLS‐DA model. Overall percent accuracies with $95\%$ confidence intervals were used to assess model performance and indications of overfitting using the validation data. Overfitting was considered if $95\%$ confidence intervals percent accuracy of $60\%$ or less. Each metabolite was analyzed by linear mixed models (LMMs) to assess the effect of the exercise intervention. LMMs included exercise intervention (pre/post), BMI classification (obese/lean), sex (M/F), and study cohort ($\frac{1}{2}$), and the exercise × BMI interaction as coefficients; subject ID was included as the random effect. Correction for multiple comparisons was applied using Benjamini and Hochberg's false discovery rate (FDR) correction. Statistical modeling of metabolomics data was conducted in the R Statistical Language (v4.1.0). To further unravel broader patterns between metabolites and their responsiveness to exercise, hierarchical cluster analysis (HCA) was implemented as previously described using Metaboanalyst (Metaboanalyst.com) software (Morville et al., 2020). Data were normalized (log transformation, autoscaled [mean‐centered and divided by the standard deviation of each variable]) and similarity was calculated with Euclidean distances, with Ward's minimum variance method. Hierarchical clustering was performed with the hclust function in package stat using fuzzy c‐means algorithm with the optimal parameters with $c = 6$ and $m = 3$ in Metaboanalyst. Metabolites within each cluster were then analyzed by KEGG metabolic pathway analysis to determine patterns of metabolic shifts in response to exercise in lean and obese participants. ## Exercise training modifies fecal metabolome with response partially dependent on obesity status We first implemented PLS‐DA to discern whether metabolite shifts occurred in response to the exercise intervention. Inclusion of all high‐confidence annotated metabolites [146] in the model, where intensity was determined by relative peak area, resulted in a good average predicted accuracy of held‐out samples ($83\%$). However, because the prediction accuracy confidence interval indicated insufficient model convergence ($95\%$ CI: $52\%$, $98\%$) on the validation data set, we introduced a reduced model with 16 fecal metabolites with VIP values >1, resulting in an improved confidence interval of predicted accuracy (CI: $74\%$, $100\%$; Figure 1a). FIGURE 1: Fecal metabolome responses to exercise training. (a). Partial least squares discriminant analysis (PLS‐DA) was used to assess metabolic profiles of feces collected before—(Pre‐blue) and after—(Post‐orange) a 6‐week aerobic exercise intervention. Residuals from linear mixed models accounting for obesity status, sex, and study cohort were used in PLS‐DA models. Axes indicate values for PLS‐DA scores (i.e., sample projections). Metabolites were selected for inclusion if Variable Importance in Projection (VIP) value was > 1. All model development, feature selection, and visualizations were conducted on training data (n = 18). (b) Linear mixed models (LMM) identified fecal metabolites responsive to exercise training (regardless of obesity, sex, age or study cohort). (c) Cluster analysis revealed 5 primary metabolite subgroups that shifted concordantly as a result of exercise training. Linear mixed models (LMMs) were then implemented with adjustments for study cohort, obesity status (lean or obese), and sex (male or female) to determine whether the effect of the exercise intervention on the fecal metabolome would remain apparent when accounting for these factors. In total, LMMs identified 12 fecal metabolites that were responsive to exercise training regardless of BMI (main effect exercise FDR <0.05; Figure 1b): 3‐methylxanthine, caffeine, and arachidonic acid increased following the intervention, whereas 2‐hydroxybenzoic acid, 3‐methylglutaric acid, 2‐hydroxyphenylalanine, 4‐hydroxybenzoic acid, 4‐pyridoxic acid, jasmonic acid, pimelic acid, suberic acid, and uridine decreased in response to the intervention (Figure 1b). Next, we aimed to understand whether obesity impacted functional metabolic responses to exercise training. To fully understand functional shifts in fecal metabolome in response to exercise, we implemented two‐way hierarchical cluster analysis (HCA) merged with KEGG metabolic pathway analysis. HCA allowed for stratification of metabolites grouped by pre‐ and post‐exercise conditions within lean and obese individuals. This analysis identified five main groups (clusters) of metabolites that responded to exercise training differently in lean and obese participants (Figure 1c). Z‐scores were calculated for all metabolite clusters to characterize metabolite responses to exercise followed by KEGG pathway analysis to determine the effects of exercise and obesity within each cluster (e.g., Clusters 1 and 4, Figure 2a‐e; Clusters 2, 3, and 5, Figure S1). FIGURE 2: Fecal metabolic pathway responses to exercise training. (a). Z‐scores of Cluster 1 reveals metabolites that decreased in feces as a result of exercise training. (b) No KEGG pathways were significantly modified by exercise within cluster 1. (c). Z‐score of Cluster 4 reveals a group of metabolites that responded differentially to exercise training based on obesity status. (d) KEGG pathway analysis reveals pathways within cluster 4 that differed by obesity status pre‐exercise (left, blue bars represent significant obesity effect at ‐log10 p‐value >1.25) but were no longer different post‐exercise (right). (e) Fecal metabolites within aromatic amino acid (ArAA) pathways responded differentially to exercise training based on obesity status (LMM obesity × exercise effect p < 0.05). Examination of Cluster 1 revealed a group of fecal metabolites that largely decreased in response to exercise regardless of obesity status (Figures 1c and 2a). Pathway analysis indicated ubiquinone biosynthesis and carnitine synthesis as the most affected by exercise training, although these did not reach statistical significance following FDR correction (Figure 2b). Cluster 4 contained a group of metabolites that responded differentially to exercise based on obesity status, generally increased in obese individuals but decreased in participants without obesity (Figures 1c and 2c). Metabolic pathway analysis of fecal metabolites prior to exercise training revealed significant differences, based on obesity status, in pathways related to thyroid hormone biosynthesis, catecholamine production, and aromatic amino acid (ArAA) metabolism, among others (Figure 2d; left insert). However, after exercise training, these pathway differences between lean and obese participants were abolished (Figure 2d; right insert), indicating a differential metabolic response to exercise training based on obesity status. Within Cluster 4 we identified a serotonin metabolite, 5‐hyroxyindole‐3‐acetic acid (5‐HIAA), and a microbial tyrosine metabolite, 4‐hydroxyphenyllactic acid (4‐HPLA), which responded differentially to exercise based on obesity status (Figure 2e). Clusters 2, 3, and 5 also contained metabolite groups that were responsive to exercise training, but KEGG analysis did not reveal any coordinated shifts in metabolic pathways (Figure S1). ## Exercise training‐induced modification to overnight‐fasted serum metabolome indicates changes in microbial amino acid metabolism We next used PLS‐DA to analyze serum metabolic responses to exercise training. This model resulted in a good overall accuracy average of held‐out data ($90\%$), but also had a wide confidence interval (CI: $56\%$, $100\%$). Thus, metabolites with VIP loadings >1 were utilized to refine the model (Figure 3a); however, the reduced model still had a wide confidence interval (CI: $36\%$, $100\%$). We implemented LMMs, which identified eight serum metabolites with a significant main effect of exercise ($p \leq 0.05$). Adenosine, caffeine, methionine, 1‐methyluric acid, quinic acid, and creatinine increased after the intervention and 3‐hydroxybutyric acid and palmitic acid decreased (Figure 3b). These patterns occurred regardless of obesity status and thus are attributed to the effect of exercise intervention specifically. FIGURE 3: Serum metabolome responses to exercise training. (a). Partial least squares discriminant analysis (PLS‐DA) was used to assess metabolic profiles of feces collected before—(Pre‐blue) and after—(Post‐orange) a 6‐week aerobic exercise intervention. Residuals from linear mixed models accounting for obesity status, sex, and study cohort were used in PLS‐DA models. Axes indicate values for PLS‐DA scores (i.e., sample projections). Metabolites were selected for inclusion if Variable Importance in Projection (VIP) value was >1. All model development, feature selection, and visualizations were conducted on training data (n = 18). (b) Linear mixed models (LMM) identified serum metabolites responsive to exercise (regardless of obesity, sex, age or study cohort). (c) Cluster analysis revealed serum metabolite subgroups that shifted concordantly as a result of exercise training. To interrogate functional shifts in the serum metabolome in response to exercise, and the potential impact of obesity on these patterns, we similarly implemented two‐way HCA merged with KEGG metabolic pathway analysis and performed Z‐score calculations within each cluster to determine whether groups of metabolites responded in parallel to exercise (Figure 3, Clusters 1–2, Figure 4a–e; Clusters 3–5, Figure S2). Cluster 1 analysis revealed significant increases by exercise—regardless of obesity status—on betaine, methionine, spermine, glycine/serine, purine, and selenoamino acid metabolic pathways (Figure 4a, b). Cluster 2 analysis revealed a group of metabolites that increased in response to exercise training, primarily in obese versus lean participants (Figure 4c). Pathway analysis of Cluster 2 indicated that metabolites associated with aromatic and amino acid metabolism and catecholamine biosynthesis, among others, were significantly upregulated in response to exercise training in obese participants (Figure 4d). Next, we focused on known microbial ArAA metabolites in the serum and found two microbially derived metabolites downstream of the ArAAs tyrosine and tryptophan—4‐hydroxyphenyllactic acid (HPLA) and indole‐3‐lactic acid (ILA), respectively—which were significantly increased by exercise training (Figure 4e; ANOVA main effect exercise FDR $p \leq 0.05$). ILA and 4‐HPLA increased more substantially in obese versus lean participants; however, this interaction did not reach statistical significance (exercise × obesity $p \leq 0.05$). Analysis of Clusters 3–5 revealed shifts of serum metabolites in response to exercise and in relation to obesity status, but further KEGG mapping did not reveal any significant changes in functional metabolic pathways within these clusters (Figure S2). FIGURE 4: Serum metabolic pathway responses to exercise training. (a). Z‐scores of Cluster 1 reveals a group of metabolites that responded to exercise training regardless of obesity status. (b) KEGG pathway analysis reveals pathways within cluster 1 that differed pre to post‐exercise training. (c). Analysis of Cluster 2 revealed a group of metabolites responsive to exercise training that was partially dependent on obesity status. (d) KEGG pathway analysis reveals metabolic pathways within Cluster 2 that shifted in response to exercise training. (e) Microbial‐derived aromatic amino acid (ArAA) metabolites, indole‐3‐lactic acid and 4‐hydroxyphenyllactic acid (4‐HPLA) were increased by exercise in the serum of both lean and obese participants (main effect Exercise p < 0.05). Right inserts represent percent (%) change from pre–post‐exercise in lean and obese participants. ## Fecal and serum aromatic amino acid metabolites associate with body composition, markers of insulin sensitivity, and cardiorespiratory fitness ArAA‐derived metabolites were responsive to exercise training in both feces and serum. Since exercise typically results in reduced body fat, improved insulin sensitivity, and increased levels of fitness, we next explored whether these ArAA metabolites correlated with body composition (bone density, fat mass, lean mass), markers of fasting glucose homeostasis (glucose, insulin, HOMA‐IR), and cardiorespiratory fitness (V˙O2max) across all participants. We measured relationships of ArAA and microbial derivatives to phenotypic outcomes before exercise training (Premetabolite vs. Prephenotype; left panel), and in response to exercise training (Δ Post‐Pre metabolite vs. Δ Post‐Pre phenotype; right panel) in both lean and obese participants (Figure 5a,b). FIGURE 5: Associations between microbial‐derived aromatic amino acid (ArAA) metabolites and physiological outcomes. Colorimetric scaling of significant Spearman mo (ρ) correlation coefficients relating ArAA and microbial‐derived metabolites () to body composition (bone density, fat mass, lean mass), glucose homeostasis (fasting glucose, fasting insulin and HOMA‐IR) and fitness outcome variables (V˙O2max) at baseline (Pre) and in response (Delta) to 6 weeks of exercise training in (a) fecal samples and (b) serum samples. Delta correlations are ΔPost‐Pre metabolite vs. ΔPost‐Pre phenotype. Dot size and color represent Spearman Δ coefficient (−1 Large bright blue to +1 Large bright red). Squares containing dots represent significance at p < 0.05. Blank squares p > 0.05. Microbial‐derived/modified. Globally, fecal ArAAs and downstream xenometabolites associated with phenotypic outcomes at baseline and in response to exercise (Figure 5a). For example, fecal indole‐3‐propionic acid positively associated with V˙O2max at baseline regardless of obesity status (Spearman ⍴ = 0.38, $p \leq 0.05$). However, further analysis revealed relationships between fecal ArAAs and phenotypic outcomes that were largely dependent on BMI status. For example, exercise‐induced changes in phenylacetylglutamine positively associated with changes to HOMA‐IR in lean (Spearman ⍴ = 0.833, $p \leq 0.01$), but not obese (Spearman ⍴ = 0.32, $p \leq 0.05$), participants. In the serum, correlational analysis revealed a pattern whereby many ArAAs and downstream metabolites associated with host phenotype (Figure 5b). For example, many microbially derived ArAA metabolites positively associated with lean mass at pre‐exercise timepoints, regardless of obesity status. This includes exercise‐responsive metabolites ILA and 4‐HPLA, which were strongly associated with lean mass at baseline when collapsed across both BMI participant groups (Spearman ⍴, Lean mass vs. ILA = 0.46, 4‐HPLA = 0.70, $p \leq 0.05$; Figure 5b; left panels). Changes (ΔPost‐Pre exercise) in serum ArAAs and metabolites also paralleled changes to phenotypic outcomes in response to exercise training (Figure 5b; right panels). However, these relationships were somewhat divergent in lean versus obese participants. For instance, exercise‐induced increases in ILA and 4‐HPLA strongly paralleled reductions in fasting insulin in obese participants (Spearman ⍴; Insulin Δ vs. ILA Δ = −0.71, 4‐HPLA Δ = −0.49, $p \leq 0.05$) but not in lean participants ($p \leq 0.05$; Figure 5b). Regardless of participant BMI status, ArAAs and ArAA metabolites were found in both fecal and serum samples. A total of 37 metabolites were found at both sites, including tyrosine, tryptophan, and phenylalanine, as well as 5 microbe‐derived ArAA metabolites (Figure 6a,b). Metabolite correlation analysis, collapsed across both BMI groups, revealed associations between ArAA metabolites at baseline and in response to exercise (Figure 6c). For example, in the serum, concentrations of 4‐HPLA and ILA (Pre and Δ post‐pre) were highly correlated, further suggesting shared metabolic regulation of these two compounds ($p \leq 0.01$; Figure 6c). To our surprise, however, inter‐site analysis (serum vs. fecal) of ArAA metabolites revealed fewer associations. For example, fecal concentrations of ILA and 4‐HPLA were not significantly correlated with serum concentrations of these same metabolites (Figure 6c). Accordingly, fecal and serum metabolite concentrations also had disparate correlations to body composition. For example, within obese subjects, fecal and serum ArAA metabolites showed opposite trends in relation to body composition. FIGURE 6: Metabolites identified in both feces and serum, and fecal‐serum correlations for aromatic amino acid (ArAA) metabolites. (a) Venn Diagram displaying the number of metabolites found in feces, serum, and both feces and serum samples. (b) Complete list of the 37 metabolites found in both fecal and serum samples. (c) Colorimetric scaling of significant Spearman rho (ρ) correlation coefficients relating microbial‐derived metabolites to inter and intra tissue (serum and feces) at baseline (Pre) and in response (Delta) to 6 weeks of exercise training. Dot size and color represent Spearman Δ coefficient (−1 Large bright blue to +1 Large bright red). Squares containing dots represent significance at p < 0.05. Blank squares p > 0.05. ## DISCUSSION Exercise training improves fitness while promoting metabolic and immune health, but the molecular and signaling mechanisms are not fully elucidated. These benefits may be mediated, at least in part, through exercise training‐associated adaptations in circulating and tissue‐specific metabolites. Herein, we provide evidence that microbial‐ and host‐derived metabolites found in both feces and serum respond to aerobic exercise training in previously sedentary adults. We also discovered that obesity status is an important factor that contributes to specific serum and fecal metabolite responses to exercise training. Exercise training modified several fecal metabolites regardless of obesity, age, or sex. Metabolite cluster and pathway analysis of the fecal metabolome indicated that ubiquinone pathways were most affected by exercise. This included a significant reduction in 4‐hydroxybenzoic acid (4‐HB) by exercise training regardless of obesity status. Of note, 4‐HB has been suggested to serve as a precursor to the benzoquinone ring of Coenzyme Q in animals, yeast, and bacteria (Parson & Rudney, 1964). Because 4‐HB is a cross‐species metabolite, these changes may have relevance for bacterial electron transport and/or mitochondrial turnover in the local gut environment, among other possibilities. The origin of 4‐HB (host vs. microbe) is still currently unknown. Additional fecal metabolites were altered by exercise training. Among them, 3‐methylglutaric (3‐MG) acid is a downstream product of leucine metabolism and was significantly decreased with exercise training. 3‐MG acid accumulation induces reactive oxygen species production, ultimately leading to mitochondrial dysfunction and tissue damage (Colin‐Gonzalez et al., 2016; Jones et al., 2020; Ribeiro et al., 2011). However, the physiological relevance of 3‐MG acid is not well studied, especially in context of the microbiome, and thus should be examined more closely in future studies. We also found that caffeine and downstream metabolites (e.g. 3‐methylxanthine, quinic and methyluric acid) were increased in both the feces and serum in response to exercise training. While caffeine itself is not a microbial metabolite, other xanthine metabolites, including those upregulated in this study with exercise training, are known to be modified by microbial species (Zhou et al., 2020). Recent data indicates that changes in microbial xanthine metabolism is a key feature of select chronic states including chronic fatigue syndrome, where disease progression decreases microbial xanthine metabolism (Xiong et al., 2023). Conversely, the current study provides evidence that microbial xanthine metabolite production is increased in response to exercise training. These data coincide with our previous report showing caffeic acid metabolites were upregulated in response to acute exercise without any changes to dietary caffeine intake (Grapov et al., 2019).While the mechanisms leading to exercise‐induced shifts in xanthine metabolism remain unclear, microbial xanthine metabolism is a potential regulator of host physiology and should be examined more closely in future studies. In accordance with our previous work showing differential effects of exercise on gut microbiome composition when comparing lean and obese individuals (Allen et al., 1985), obesity status appeared to affect fecal metabolite profiles in response to exercise. Cluster and pathway analysis revealed that these effects were driven primarily by metabolites within catecholamine biosynthetic and ArAA metabolic pathways. Within ArAA metabolism, 5‐hyroxyindoleacetic acid (5‐HIAA) is a downstream product of serotonin and was significantly increased in obese (but not lean) participants in response to exercise training. 4‐hydroxyphenyllatic acid (4‐HPLA) is a microbial metabolite of tyrosine and showed similar trends to 5‐HIAA in response to exercise. The physiological relevance of these findings is in need of future investigation, but the data herein illustrate that exercise effects on microbiome metabolism can be influenced by obesity and related sequela. The serum metabolome was also responsive to exercise training with some effects dependent on obesity status. Cluster analysis revealed many pathways responsive to exercise regardless of obesity status. These included metabolites involved in purine synthesis pathways (1‐methyluric acid, 3‐methylxanthine). Other studies have indicated an increase in purine metabolites and changes to metabolic pathways associated with purine metabolism in response to both acute exercise and exercise training (Dudzinska, Lubkowska, Dolegowska, & Safranow, 2010; Dudzinska, Lubkowska, Dolegowska, Safranow, & Jakubowska, 2010; Pospieszna et al., 2019). The mechanisms involved are unclear, but we speculate that the adenosine monophosphate (AMP) catabolic pathway may be upregulated with exercise training in various tissues, including the gut. Aromatic amino acid metabolites with known microbial origin, indole‐3‐lactic acid (ILA) and 4‐hydroxyphenyllactic acid (4‐HPLA), were upregulated in the serum of exercise‐trained individuals versus pre‐training levels. Both ILA and 4‐HPLA are downstream metabolites of tryptophan and tyrosine, respectively, and their production relies on similar enzymatic machinery in the gut microbiome. Phenyllactate dehydrogenase (fLDH) is an enzyme present in select lactic acid‐producing bacteria (LAB), including some species of Bifidobacterium and Lactobacillus, and contributes to the production of ILA and 4‐HPLA from ArAA precursors (Dodd et al., 2017; Sakurai et al., 2021). We have not previously observed any changes to these two LAB genera in response to exercise in human feces (Allen et al., 2018). However, Bifidobacterium and Lactobacillus species are found in highest concentrations in the ileum and thus fecal analysis may not adequately represent exercise‐induced shifts to the upper gastrointestinal microbiome. Here, we speculate that exercise‐induced increases in serum ILA and 4‐HPLA may result from an increased abundance of Bifidobacterium, Lactobacillus, and/or increased fLDH enzyme activity within the ileum. In support of this latter hypothesis, we have also previously reported increases in circulating ILA during recovery from an acute exercise bout (Grapov et al., 2019). However, the mechanisms underlying increases in serum ILA and 4‐HPLA by acute exercise and/or exercise training are currently unknown and need further investigation. Notably, we observed no correlation between fecal and serum levels of ILA and 4‐HPLA in the present study. As the fecal concentration of many gut metabolites depends on gut transit time, cross‐feeding interactions between microbes, and the rate of host absorption (Donia & Fischbach, 2015), sampling multiple regions along the GI tract in addition to serum across multiple longitudinal timepoints may be required to fully understand the impacts of these metabolites on host health. Regardless of the mechanisms underlying these responses, ILA and 4‐HPLA both exhibit immunomodulatory and metabolic tuning properties and thus signify physiologically relevant shifts in microbial metabolism induced by exercise training. For instance, ILA has been shown to regulate inflammation by scavenging free radicals and inhibiting the production of interleukin‐6, a pro‐inflammatory cytokine (Aoki‐Yoshida et al., 2013; Meng et al., 2020). In addition, 4‐HPLA has been shown to attenuate reactive oxygen species production in neutrophils (Beloborodova et al., 2012). Future studies are needed to confirm such changes and explore the relationship between microbially derived ArAA metabolites and health outcomes. In conclusion, we found that 6 weeks of aerobic exercise training shifts both fecal and serum metabolome in previously sedentary adults, despite no discernable changes in dietary patterns within individual participants or microbial community structure. Exercise‐induced changes in metabolomic profiles were partially dependent on baseline BMI status. Metabolites associated with ArAA metabolism were most responsive to exercise in both feces and serum. These included bioactive metabolites with known microbial origin and potential health‐modifying effects. These data highlight the importance of understanding the microbiome and xenometabolome when investigating responses to exercise training. Future studies are needed to further understand the potential role of microbial‐derived metabolites in mediating exercise adaptations and physiological pathways relevant to human health. Such experiments could explore the therapeutic potential of these metabolites as “postbiotics” (Swanson et al., 2020). The specific communication pathways and host‐derived signals that drive the associations remain to be determined. ## Limitations We acknowledge the limitations of this study. This study represents a relatively small sample size with only 24 participants ($$n = 15$$ lean and $$n = 9$$ obese). While participants followed 3‐day diet controls prior to all sample collections and were instructed to maintain overall dietary patterns including caffeinated beverage consumption, we cannot completely rule out variation in diet control. Furthermore, due to the heterogeneity of baseline metabolome profiles, results from these studies should be interpreted with some caution and merit follow‐up analysis with larger study populations. We also acknowledge that untargeted metabolomic analysis should be followed up with more targeted approaches in future studies. Finally, since we did not collect feces quantitatively (i.e., total dry weight per a given time or total sample), we could not calculate the total pool size or daily excretion for each metabolite. Nevertheless, the novel patterns observed here are reflective of diet‐independent exercise intervention effects on xenometabolism and the impact of obesity status on these changes. ## AUTHOR CONTRIBUTIONS Mikaela C. Kasperek, Lucy Mailing, Brian D. Piccolo, Jeffery A.Woods, Sean H. Adams, and Jacob M. Allen were responsible for conception and design of the work. Mikaela C. Kasperek, Lucy Mailing, Brian D. Piccolo, Becky Moody, Renny Lan, Lucy Mailing, Xiaotian Gao, Diego Hernandez‐Saavedra, Jeffrey A. Woods, Sean H. Adams, and Jacob M. Allen were responsible for acquisition, analysis or interpretation of data for the work. Mikaela C. Kasperek, Lucy Mailing, Brian D. Piccolo, Becky Moody, Renny Lan, Lucy Mailing, Xiaotian Gao, Diego Hernandez‐Saavedra, Jeffrey A. Woods, Sean H. Adams, and Jacob M. Allen were responsible for drafting the work or revising it critically for important intellectual content. Mikaela C. Kasperek, Lucy Mailing, Brian D. Piccolo, Becky Moody, Renny Lan, Xiaotian Gao, Diego Hernandez‐Saavedra, Jeffrey A. Woods, Sean H. Adams, and Jacob M. Allen were responsible for drafting the work or revising it critically for important intellectual content. All authors approved the final version of the manuscript; agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; and all persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. ## CONFLICT OF INTEREST STATEMENT S.H. Adams is founder and principal of XenoMed, LLC, which is focused on research and discovery in the area of microbial metabolism. 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--- title: Sex and strain differences in renal hemodynamics in mice authors: - Yu Tao - Cassandra Young‐Stubbs - Parisa Yazdizadeh Shotorbani - Dong‐Ming Su - Keisa W. Mathis - Rong Ma journal: Physiological Reports year: 2023 pmcid: PMC10031302 doi: 10.14814/phy2.15644 license: CC BY 4.0 --- # Sex and strain differences in renal hemodynamics in mice ## Abstract The present study was to examine sex and strain differences in glomerular filtration rate (GFR) and renal blood flow (RBF) in C57BL6, 129/Sv, and C57BLKS/J mice, three commonly used mouse strains in renal research. GFR was measured by transdermal measurement of FITC‐sinitrin clearance in conscious mice. RBF was measured by a flow probe placed in the renal artery under an anesthetic state. In C57BL6 mice, there were no sex differences in both GFR and RBF. In 129/Sv mice, females had significantly greater GFR than males at age of 24 weeks, but not at 8 weeks. However, males had higher RBF and lower renal vascular resistance (RVR). Similar to 129/Sv, female C57BLKS/J had significantly greater GFR at both 8 and 24 weeks, lower RBF, and higher RVR than males. Across strains, male 129/Sv had lower GFR and higher RBF than male C57BL6, but no significant difference in GFR and greater RBF than male C57BLKS/J. No significant difference in GFR or RBF was observed between C57BL6 and C57BLKS/J mice. Deletion of eNOS in C57BLKS/J mice reduced GFR in both sexes, but decreased RBF in males. Furthermore, there were no sex differences in the severity of renal injury in eNOS−/− dbdb mice. Taken together, our study suggests that sex differences in renal hemodynamics in mice are strain and age dependent. eNOS was not involved in the sex differences in GFR, but in RBF. Furthermore, the sexual dimorphism did not impact the severity of renal injury in diabetic nephropathy. There are sex and strain differences in renal hemodynamics, which may also be age dependent. The sex difference in renal hemodynamics is not attributed to eNOS, and do not impact the development of diabetic kidney disease. ## INTRODUCTION It is increasingly recognized that interactions between biological, social, and environmental factors play a significant role in the prevention, pathophysiology, and management of human disease (Mannon et al., 2020). Sex/gender differences and race/ethnicity/genetic background are among those factors with a significant impact on human health and disease. It is evident that there are sex differences in the incidence, age of onset, manifestation, severity and development, as well as response to treatment in various diseases, including heart disease, hypertension, obesity, and acute or chronic renal ischemia (Arnold et al., 2017; Gillis & Sullivan, 2016; Kher et al., 2005; Swartling et al., 2022). In kidneys, sex differences have been reported in gene expressions, transporters, mitochondrial function, circadian clock, structure and/or function, and in the occurrence and development of several kidney diseases, such as diabetic nephropathy (DN) (Clotet et al., 2016; Harris et al., 2018; Institute for Health Metrics and Evaluation, 2020; Layton & Gumz, 2022; Maric‐Bilkan, 2020; Skrtic et al., 2017; Sultanova et al., 2020; Veriras et al., 2017). In addition to sex differences, racial heterogeneity in renal function (Hsu et al., 2021), and in the incidence and prevalence of kidney diseases (GBD, 2018; Jha et al., 2013; Power, 2019; Susantitaphong et al., 2013) is evident. Animal studies demonstrated that genetic factors play a major role in determining susceptibility, as well as resistance, to kidney disease. For instance, previous studies identified significant strain differences affecting the development of diabetic renal complications in mice (Breyer et al., 2005; Brosius et al., 2009; Gurley et al., 2005, 2009). The mouse is a widely used tool for studying renal function and developing models of human kidney diseases because its genome is tractable for manipulation and its diverse and unique genetic resources are accessible (Beck et al., 2000; Bronson & Smithies, 1994; Marshall et al., 1992). In the present study, we studied the sex and strain differences in glomerular filtration rate (GFR) and renal blood flow (RBF) in three strains of mice which are commonly‐used in renal research. We further explored the role of endothelial nitric oxide synthase (eNOS) in the differences and the impact of the differences on the development of DN. The rationales of the study included [1] there are very limited data to compare sex and strain differences in renal hemodynamics in mice, [2] there are lack of studies on association of the differences in renal hemodynamics with severity of kidney disease, such as DN, and [3] there are very limited study to measure GFR in mice using a non‐invasive approach under conscious and free‐moving state. The information from this study would help researchers in the field appropriately design experiments and evaluate data when using a mouse model for studying renal physiology and pathology. ## Animal preparation Three strains with five genetic backgrounds of mice were included in this study. Except for C57BLKS/J mice, which were purchased from the Jackson Laboratory (Stock #: 000662), all other strains of mice were inbred mice and maintained at animal facility of University of North Texas Health Science Center at Fort Worth. The C57BL/6 breeders were purchased from Charles River Laboratory. The 129/Sv mouse breeders were obtained from Dr. Mary B. Humphrey at University of Oklahoma Health Sciences Center. eNOS−/− and eNOS−/− db/db mice were generated from homozygous eNOS−/− and heterozygous Leprdb (eNOS−/− db) on C57BLKS/J background. The eNOS−/− db breeding pairs were purchased from The Jackson Laboratory (JAX# 8340). The eNOS−/− and eNOS−/− db/db mice were identified by genotyping at an age of 3–4 weeks using high‐resolution melting PCR protocol provided by the vendor and publications in (Mohan et al., 2008; Shesely et al., 1996). The age of both male and female mice used in this study ranged from 8 to 24 weeks. During renal blood flow measurements, mice were placed in an induction chamber containing $4\%$ isoflurane anesthesia and thereafter maintained on isoflurane ($2\%$ in oxygen, delivered through a mask) for the duration of surgery. All procedures were approved by the University of North Texas Health Science Center Institutional Animal Care and Use Committee. All mice were maintained at the animal facility of University of North Texas Health Science Center under local and NIH guidelines. These mice were housed in a specific pathogen‐free facility with a temperature‐controlled room, and regulated with 12‐h light/dark cycle and free access to water and food (standard chow diet: LabDiet) containing $25.1\%$ fiber, $0.29\%$ Na+, $19.3\%$ protein, $13.5\%$ fat, and $15.8\%$ calories from fat. ## Blood pressure measurements A catheter was inserted into the left carotid artery of mice under isoflurane anesthesia. Blood pressure was continuously recorded during the entire period of RBF measurement in anesthetized mice using PowerLab software as previously reported (Chaudhari et al., 2022; Fairley & Mathis, 2017). The mean arterial pressure (MAP) averaged from 30‐min continuous recordings after completion of surgery and stabilization of blood pressure was presented in this study. ## Assessment of RBF and vascular resistance Following a posterior incision, the right renal artery was isolated from the corresponding vein in a subset of anesthetized mice and placed in a Transonics flow probe (Model #: 0.5 PS) in order to measure RBF. RBF (mL/min/kg body weight) and anesthetized MAP (mm Hg) were gathered simultaneously for 30 min following a 30‐min stabilization period using Powerlab software. Renal vascular resistance (RVR; mm Hg/min/kg body weight) was calculated by dividing anesthetized MAP by normalized RBF. ## Transdermal measurement of GFR GFR was measured in conscious, freely moving mice using transdermal measurement of sinistrin clearance rate as previously reported (Chaudhari et al., 2022). Anesthetized mice were positioned on a surgery plate and the right dorsal hair was shaved with an electrical shaver followed by the application of depilatory lotion and $70\%$ ethanol to remove any residual hair. The transdermal fluorescence detector (MB 0309 Mini, MediBeacon Inc.) was directly attached to the naked skin and fixed to the mouse body using medical tape. FITC‐conjugated sinistrin was then administered by retro‐orbital injection at 0.03 mg/g body weight (0.03–0.05 mL) using a 0.5 mL BD insulin syringe (28G × ½”). The mouse was recovered from isoflurane about 10–20 s. The excitation kinetics of the exogenous GFR tracer were recorded using the software provided by the vendor (MB Lab Ver. 2.18) in freely‐moving, conscious mice for 1.5 h. The recorded sinistrin clearance curve was fitted in the software provided by the vendor (MB Studio Ver. 2.1) using a two‐compartment model. GFR was calculated based on the half‐life (t$\frac{1}{2}$) of plasma FITC‐sinistrin decay using the formula 14616.8/(t$\frac{1}{2}$) and reported as μL/min/100 g of body weight as previously described (Scarfe et al., 2018; Schock‐Kusch et al., 2013; Schreiber et al., 2012). ## Assessment of urinary albumin excretion rate Urine samples were collected through metabolic cages (Catalog #: 370 0 M022, Braintree Scientific Inc.) during the period of 24 h. Urinary albumin and creatinine levels were determined using Albuwell‐M kits (Exocell). Albumin excretion rate was expressed as the ratio of urinary albumin concentration to urinary creatinine concentration (ACR, μg/mg) (Ma, Li, et al., 2019). ## Renal tissue preparation and immunohistochemical staining Mice were anesthetized by intraperitoneal injection of ketamine with xylazine (100 + 10 mg/kg). The mice were perfused with physiological saline solution through the left ventricle to wash out blood, followed by perfusion with $4\%$ paraformaldehyde. The right kidneys were excised, decapsulated, cut in half through a midsagittal plane, and fixed with $4\%$ paraformaldehyde. The fixed kidneys were dehydrated through a graded series of ethanol, infiltrated and embedded in paraffin, sectioned (4–5 μm), and mounted on glass slides. For immunohistochemical staining, kidney sections were deparaffinized. Antigen retrieval was achieved by heating the sections in 10 mM citrate buffer in a microwave for 10 min. The sections were blocked by $5\%$ goat serum for 30 min at room temperature and then incubated with anti‐Wilms' Tumor 1 (WT1) antibody (rabbit polyclonal, catalog #: MBS9203569, lot #: SA170311DC, MyBioSource) at 1:20 at 4°C overnight. Secondary antibody was Alexa 568‐conjugated antibody (goat anti‐rabbit) from Invitrogen. Nuclei were stained in blue using DAPI. Slides were coverslipped using resinous mounting medium. Sections were examined using an Olympus microscope (BX41) and an Olympus DP70 digital camera with DP manager software (version 2.2.1). Images were uniformly adjusted for brightness and contrast. Counting of WT1 positive cells was conducted by a blinded observer using ImageJ (version 1.50b; NIH). The representative photomicrographs in Figure 8c were taken with a Zeiss inverted confocal microscope (Zeiss LSM 880). ## Statistical analysis Data were reported as means ± SD. Student's unpaired t‐test was used to identify the difference between two groups (Figures 1, 2, 3, 4, 5, 6, 7 and 9a). The one‐way ANOVA plus Student–Newman–Keuls post hoc analysis was used to analyze the differences in one parameter among multiple groups (Figures 8 and 9b,c). The two‐way ANOVA plus Tukey's honestly significant difference post hoc analysis was used to analyze the differences in two parameters among multiple groups (Tables 1 and 2). $p \leq 0.05$ was considered statistically significant. Statistical analyses were performed using SigmaStat (Jandel Scientific). FIGURE 1: GFR in male and female conscious C57BL6 mice. (a, b) Representative FITC‐sinistrin clearance curves, showing plasma fluorescence signals measured transcutaneously after FITC‐sinistrin application in both male and female C57BL6 mice at 8 weeks (8 W, a) and 24 weeks (24 W, b). (c, d) Summary of calculated GFR from groups presented in a and b, respectively. ns: no significant difference, males versus. females (Unpaired Student's t‐test). “n”: the number of mice; ns, no significant difference, males versus females (Unpaired Student's t‐test). FIGURE 2: Comparison of renal hemodynamics between male and female C57BL6 mice. (a, b) Representative arterial blood pressure (ABP) and RBF recordings in a male (a) and a female (b) mouse at age of 24 weeks. (c) Summary data* of anesthetized mean arterial blood pressure (MAP) from six male and six female mice ($$n = 6$$). (d) Summary data of RBF from seven male and seven female mice ($$n = 7$$). RBF was normalized to BW. (e) Summary data of calculated renal vascular resistance (RVR) from six male and six female mice ($$n = 6$$). RVR was normalized to BW. From (c–e), “ns” indicates no significant difference (Unpaired Student's t‐test).* FIGURE 3: Sex difference in GFR in conscious 129/Sv mice. (a, b) Representative FITC‐sinistrin clearance curves, showing plasma fluorescence signals after FITC‐sinistrin application in both male and female 129/Sv mice at 8 weeks (8 W, a) and 24 weeks (24 W, b). (c, d) Summary of calculated GFR from groups presented in (a, b), respectively. ns: no significant difference. “” denotes $p \leq 0.01$, males versus females (Unpaired Student's t‐test). “n”: the number of mice. FIGURE 4: Sex difference in renal hemodynamics in 129/Sv mice. (a, b) Representative ABP and RBF recordings in a male (a) and a female (b) mouse at age of 24 weeks. (c) Average anesthetized MAP from seven male and six female mice. (d) Summary data* of RBF from seven male and six female mice. RBF was normalized to BW. (e) Summary data of calculated RVR from seven male and six female mice. RVR was normalized to BW. From (c–e), “ns” indicates no significant difference, denotes $p \leq 0.01$, and * denotes $p \leq 0.001$ (Unpaired Student's t‐test).* FIGURE 5: Sex differences in GFR in conscious C57BLKS/J mice. (a, b) Representative FITC‐sinistrin clearance curves, showing plasma fluorescence signals after FITC‐sinistrin application in both male and female C57BLKS/J mice at age of 8 weeks (8 W, a) and 24 weeks (24 W, b). (c, d) Summary of calculated GFR from groups presented in (a, b), respectively. GFR was normalized to BW. “*” denotes $p \leq 0.01$, males versus females (Unpaired Student's t‐test). “n”, the number of mice. FIGURE 6: Sex differences in renal hemodynamics in C57BLKS/J mice. (a, b) Representative ABP (upper panels) and RBF (bottom panels) recordings in a male (a) and a female (b) mouse at age of 22–24 weeks. (c) Average anesthetized MAP from six male and five female mice. (d) Summary data* of RBF from six male and five female mice. RBF was normalized to BW. (e) Summary data of calculated RVR from six male and five female mice. RVR was normalized to BW. From (c–e), “ns” indicates no significant difference, * denotes $p \leq 0.05$, and ** denotes $p \leq 0.001$ (Unpaired Student's t‐test).* FIGURE 7: GFR and renal hemodynamics in eNOS‐knocked out male and female C57BLKS/J mice. (a, b) GFR in both male and female eNOS−/− C57BLKS/J mice at ages of 8 weeks (8W) (a) and of 24 weeks (24 W) (b). “*” denotes $p \leq 0.01$, males versus females (Unpaired Student's t‐test). “n”: the numbers of mice. (c) Anesthetized MAP from eight male and six female eNOS−/− mice. (d) Summary data* of RBF from eight male and six female mice. (e) Summary data of calculated RVR from eight male and six female mice. From (b–d), the age of mice was 24 weeks. “ns” indicates no significant difference (Unpaired Student's t‐test).* FIGURE 8: Body weight (a), blood glucose level (b), and urine output (c) of male (M) and female (F) wild‐type C57BLKS/J (WT, nondiabetic control) and eNOS−/− db/db mice. “ns” indicates no significant difference; “” denotes $p \leq 0.05$ (One‐way ANOVA), compared to the groups as indicated. The mice in all groups were at age of 20 weeks. The number in parenthesis represents the number of mice each group.* FIGURE 9: Development of renal injury in male and female eNOS−/− dbdb mice. (a) GFR in male (M) and female (F) eNOS−/− dbdb mice at age of 20 weeks. “ns” denotes no significant difference (Unpaired Student's t‐test). (b) Albumin excretion rate of male and female WT and eNOS−/−dbdb mice at age of 20 weeks. Albumin excretion rate is indicated by urinary albumin creatinine ratio (ACR). “ns” indicates no difference, “” denotes $p \leq 0.001$ (One‐way ANOVA), comparisons between groups as indicated. The number in parenthesis indicates the number of mice each group. (c) Representative high‐magnification images of podocyte staining with Wilms' tumor 1 (WT1, red) in a male and a female WT and eNOS−/− dbdb mouse at 20 weeks. Nuclei were counterstained with DAPI (blue). (d) Number of podocytes (WT1‐positively stained cells) per glomerulus averaged from three male and female WT and eNOS−/−dbdb mice. About 15–20 glomeruli from one kidney section were counted and five sections were taken from one mouse. “ns” indicates no difference, “” denotes $p \leq 0.001$ (One‐way ANOVA), comparisons between groups as indicated. TABLE_PLACEHOLDER:TABLE 1 TABLE_PLACEHOLDER:TABLE 2 ## GFR in male and female C57BL6 mice C57BL6 mouse is a multipurpose model that is the most widely used for research into physiology and pathophysiology. In the present study, young adult (8 weeks old) and adult (24 weeks old) C57BL6 mice were used. Body weight (BW) of male mice was significantly greater than that of females at 8 weeks. At 24 weeks, BW from both male and female mice were significantly increased compared to themselves at 8 weeks. However, there was no significant sex difference at this age (Table 1). Transcutaneous measurement of GFR was conducted in conscious C57BL6 mice at both 8 weeks and 24 weeks. As shown in Figure 1a,b, the profiles of plasma FITC‐sinistrin decay were very similar in both male and female mice at both ages. The normalized GFR calculated from the sinistrin clearance rate was not significantly different between males and females either at 8 or 24 weeks (Figure 1c,d), suggesting there was no sex difference in GFR in conscious C57BL6 mice. ## RBF in male and female C57BL6 mice MAP and RBF were simultaneously measured in male and female C57BL6 mice under anesthesia at an age of 22–24 weeks. Representative tracings of raw MAP and RBF are shown in Figure 2 a (male) and b (female). The summary data are presented in Figure 2c,d. There were no significant sex differences in MAP, RBF (Figure 2c,d) or calculated RVR (Figure 2e) observed. In summary, the results in Figures 1 and 2 suggest that there were no significant sex differences in renal hemodynamics in C57BL6 mice. ## Sex differences in GFR in conscious 129/Sv mice 129/Sv mouse is a substrain of 129 mouse which is one of most commonly used strains of mice for renal research (Lu et al., 2012) and is an important strain of mice for creating “knockout” and other targeted mutant mice (Simpson et al., 1997). To explore sex differences in GFR in this strain, we measured GFR in conscious males and females at age of 8 weeks and 24 weeks. No sex differences in BW were observed at 8 weeks. However, at 24 weeks, the BW of female mice was significantly lower than that of male mice (Table 1). The rate of plasma sinistrin decay was similar between male and female mice at 8 weeks, but much faster in female mice at 24 weeks (Figure 3a,b). Consistently, the calculated GFR revealed no sex difference at 8 weeks, but significantly greater in female mice compared to the males at 24 weeks (Figure 3c,d). ## Sex differences in RBF in 129/Sv mice Sex differences in renal hemodynamics have not been studied in 129/Sv mice previously. We compared MAP and RBF between adult male and female 129/Sv mice under anesthetic states. As shown in Figure 4, no significant difference in MAP between males and females was observed. However, there were significant sex differences in RBF with males significantly greater than females. Thus, the calculated RVR in male mice was significantly lower than that in female mice. ## Sex differences in GFR in conscious C57BLKS/J mice Although C57BLKS/J is closely related to C57BL/6J, the two strains are phenotypically distinct. The mutations diabetes (Lepr db) and obese (Lep ob) each express a much more severe phenotype on the C57BLKS/J background than on the C57BL6 background. Since this strain is more susceptible to renal disease than C57BL6, C57BLKS/J mouse has been used to develop a widely‐accepted model of DN (Leiter et al., 1999; Norgaard et al., 2019; Stec et al., 2015; Xue et al., 2019; Zhang et al., 2012; Zhao et al., 2006). Sex differences in GFR in C57BLKS/J mice have not been studied previously. We measured GFR in young adult (8 weeks) and adult (24 weeks) male and female C57BLKS/J mice in the present study. Similar to C57BL6, but different from 129/Sv mice, the BW of female C57BLKS/J mice was significantly lower than that of males at 8 weeks. Also, the female C57BLKS/J at 24 weeks was significantly lighter than males, the same as in 129/Sv mice, but different from C57BL6 mice. Furthermore, like C57BL6 mice, both male and female C57BLKS/J mice at 24 weeks were significantly heavier than those of the same sex at 8 weeks (Table 1). The rate of plasma sinistrin clearance was much faster in female mice at both 8 and 24 weeks (Figure 5a,b). Accordingly, the calculated GFR, which was normalized to BW was significantly greater in female mice compared to males at both ages (Figure 5c,d). ## Sex differences in RBF in C57BLKS/J mice We further examined RBF in male and female C57BLKS/J mice under anesthetized state at age of 22–24 weeks. There was no sex difference in MAP (Figure 6ac). However, RBF had significant sex differences. Same as in 129/Sv mice, the normalized RBF of females was significantly lower than that of males (Figure 6a,b,d). The calculated RVR in female mice was significantly greater than that in male mice (Figure 6e). ## eNOS did not contribute to sex differences in GFR, but mediated greater RBF in male C57BLKS/J mice Nitric oxide (NO) is central to the control of vascular homeostasis, blood pressure, and renal function (Layton & Sullivan, 2019). In the kidney, eNOS is localized in vascular endothelial cells, glomerular mesangial cells, and tubules, where it is important in the maintenance of GFR, vascular tone, and RBF (Bachmann & Mundel, 1994; Kone & Baylis, 1997; Schneider et al., 2008; Yuan et al., 2012; Zhang et al., 2007). Published studies demonstrated significant sex differences in NO system with female subjects/animals having greater NOS protein levels (Neugarten et al., 1997), enzymatic activity (Sullivan et al., 2010), NO production (Forte et al., 1998), NO bioavailability (Chen et al., 2017), and a greater capacity for NO to induce vasodilation (Majmudar et al., 2000). If NO derived from endogenous eNOS underlay the sex differences in GFR and RBF, deletion of this enzyme would be expected to remove/minimize the differences. To explore the role of NO in the sex differences, we further assessed GFR and RBF in male and female eNOS‐knocked out (eNOS−/−) C57BLKS/J mice which have shown significant sex differences in GFR and RBF (Figures 5 and 6). Similar to WT C57BL6 mice, BW of female eNOS−/− mice was significantly lower than that of males at both 8 and 24 weeks. And both male and female eNOS−/− mice at 24 weeks were significantly heavier than those of the same sex at 8 weeks (Table 1). Global deletion of eNOS (eNOS−/−) significantly reduced GFR in both males and females (Table 2), but still showed the same pattern of sex differences in GFR as that in WT mice with females significantly greater than males at both 8 and 24 weeks (Figure 7a,b). Anesthetized blood pressure in eNOS−/− mice was comparable with that in WT mice and no sex difference was observed (Figure 7c). However, eNOS knockout eliminated sex differences in RBF and RVR which were observed in WT mice (Figure 7d,e). These results suggest that eNOS played a key role in the sex differences in RBF, but not in GFR. ## Comparison of renal injury between male and female C57BLKS/J mice with DN To study if the sex differences in GFR and RBF led to differences in renal injury, we evaluated and compared renal insufficiency between male and female eNOS−/− dbdb mice (on C57BLKS/J genetic background), a well‐known type II diabetes model (Brosius et al., 2009). BW, fasting blood glucose levels, and 24 h urine output of both male and female eNOS−/− db/db mice were significantly greater than those of WT (nondiabetic controls) mice with the same age and sex. However, no sex difference in those variables was observed in diabetic mice (Figure 8). Previous studies from our and other groups (Ma, Li, et al., 2019; Zhao et al., 2006) revealed severe renal injury in this mouse model at week of 20. We measured and compared GFR in both male and female eNOS−/− dbdb mice at this age. Although the female WT C57BLKS/J mice (non‐diabetes control) had a significantly greater GFR than males at both early (8 W) and middle ages (24 W) (Figure 5a), there was no sex difference in GFR in mice with severe DN (Figure 9a). These data suggest that sex differences in GFR may not contribute to the renal insufficiency of diabetes in this mouse model of DN. Consistently, both male and female diabetic mice revealed significantly greater albumin excretion rate at week 20 compared to the sex‐ and age‐matched controls. However, there were no sex differences observed in eNOS−/− dbdb mice (Figure 9b). Since podocyte injury and loss is a hallmark of DN (Ma, Chen, et al., 2019; Tao, Chaudhari, et al., 2022; Tao, Mallet, et al., 2022), we furthermore examined the number of podocytes by staining WT1 in kidney sections from male and female diabetic and control mice. As shown in Figure 9c,d, DN significantly reduced the number of podocytes in both sexes of mice. However, there was no significant difference in podocyte loss between male and female eNOS−/− dbdb mice. These data suggest that the sex differences in GFR and RBF in C57BLKS/J mice did not result in differences in renal injury in diabetic kidney. ## Comparisons of GFR and RBF among different strains of mice We then compared the basal GFR and RBF at the same sex among C57BL/6, 129/Sv, and C57BLKS/J mice. For male mice, 129/Sv had significantly lower GFR and higher RBF than C57BL/6. Although the GFR of 129/Sv mice had no significant difference from C57BLKS/J mice, the RBF of 129/Sv mice was significantly greater than that of C57BLKS/J mice. There was no significant difference in either GFR and RBF observed between C57BL/6 and C57BLKS/J mice (Table 2). ## DISCUSSION In the past few years, an explosion of data has emerged concerning sex differences in kidney function and in the development and severity of kidney disease (Dickinson et al., 2013; Layton & Gumz, 2022; Layton & Sullivan, 2019; Maric‐Bilkan, 2020; Sullivan & Gillis, 2017; Veriras et al., 2017). Although sex differences in GFR have been studied in animal models, including mice, there are valid concerns regarding the sensitivity and accuracy of the methods used, and the conditions of animals during experimental procedures. The present study is distinguished from the previous studies in several aspects. First, we used a recently‐developed mini‐fluorescence device which can detect transdermal signal of plasma FITC‐sinistrin (Scarfe et al., 2018; Schock‐Kusch et al., 2013; Schreiber et al., 2012). This non‐invasive assessment of GFR in conscious and freely‐moving mice is superior to that used in previous studies in which GFR was estimated in anesthetized mice or by collecting blood samples at multiple time points (Dickinson et al., 2013; Hackbarth & Hackbarth, 1981; Li et al., 2017; Qi et al., 2004). Second, findings from the present study suggest that sex differences in GFR were strain and age dependent. Among the three inbred strains, C57BL6 mice did not show sex differences in GFR at both young (8 weeks) and adult (24 weeks) ages. However, 129/Sv females had significantly greater GFR than males, but only at 24 weeks. Different from the two strains, both young and adult C57BLKS/J revealed significant sex differences with a greater GFR in females. Furthermore, in contrast to most studies which reported a higher GFR in male mice (Hackbarth & Hackbarth, 1981; Messow et al., 1980), the present study showed greater glomerular filtration function in female mice who had sex differences in GFR. This discrepancy was probably due to approach of analysis of GFR. In our study, GFR values were normalized to BW, different from other studies in which GFR was taken as the whole animal (Hackbarth & Hackbarth, 1981; Messow et al., 1980). Study on sex differences in renal hemodynamics by direct measurement of RBF is limited, probably due to technical difficulty. Using an ultrasound flow probe placed around the renal artery, we directly measured basal RBF in male and female mice. Similar to sexual influence on GFR, the sex differences in RBF were also strain dependent. There was no significant difference in RBF between male and female C57BL6 mice. Similar results were previously reported in C57BLK/6 mice by Schneider et al. ( Schneider et al., 2010). However, in 129/Sv and C57BLKS/J mice, females had significantly greater RBF than males. The lower RBF in female mice must be due to higher RVR as dictated by Ohm's law. In addition to sex, strain is also a factor affecting GFR and RBF. In the present study, the basal GFR in male 129/Sv mice was significantly lower than that in male C57BL6 mice. Using the same approach, Schock‐Kusch et al. also reported that male adult (12–16 weeks) 129/Sv mice had lower GFR compared to the sex‐ and age‐matched C57BL6 mice. Strain differences in RBF have not been reported previously. In the present study, we found that male 129/Sv mice had significantly higher RBF than the other two strains with the same sex. Although the mechanisms underlying the strain differences are not clear, we could assume that a variety of genetic factors are involved. In addition, inter‐strain differences in kidney weight and structure may also contribute to the differences, as reported by an earlier study (Hackbarth & Hackbarth, 1981; Messow et al., 1980). NO has been shown to play an important role in regulating renal hemodynamics (Ketteler et al., 1994; Layton & Sullivan, 2019). eNOS has been identified within the kidney and has been localized in the glomerulus and within specific tubular segments and renal vasculature (Neugarten et al., 1997; Sullivan et al., 2010). Numerous studies have demonstrated the effects of sex/gender on NO system with female subjects/animals having greater NOS protein levels (Neugarten et al., 1997), enzymatic activity (Sullivan et al., 2010), NO production (Forte et al., 1998), and NO bioavailability (Chen et al., 2017). In the present study, we found that eNOS had distinct effects on the sex differences in GFR and RBF in C57BLKS/J mice. Although deletion of eNOS reduced the basal GFR in both sexes, the pattern of sex differences was the same as that in WT mice. This suggests that eNOS does not play a significant role in the higher GFR in female mice. Our findings are consistent with a recent report that the plasma creatinine levels were comparable between male and female rats treated with NOS inhibitor, Nω‐nitro‐L‐arginine methyl ester (L‐NAME) (Ramirez et al., 2020). Different from effects on GFR, eNOS knockout dramatically reduced RBF only in male mice, possibly attributed to an increase in renal vascular tone (indicated by an increase in RVR and MAP). Our findings are consistent with an earlier study in which inhibition of NOS with L‐NAME significantly decreased RBF in male rats but had no effect in female rats (Reckelhoff et al., 1998). It is not known why the deletion of eNOS did not influence RBF in female mice. One explanation is that the renal vasculature of males is more dependent on NO than the renal vasculature in females. The other possibility includes that other isoforms of NOS, such as neuronal NOS (nNOS) and inducible forms of NOS (iNOS) are predominant in female mice, particularly in C57BLKS/J strain. Indeed, iNOS levels are significantly greater in the renal medulla of female rats compared with male rats (Neugarten et al., 1997). nNOS is predominantly localized in several regions of the kidney where they participate in the control of renal hemodynamics (Sullivan et al., 2010). Under normotensive conditions, female rats have greater renal nNOS immunoreactivity (Wang et al., 2006). Nakano and Pollock reported that in female, but not in male rats activation of nNOS led to diuresis and natriuresis (Nakano & Pollock, 2009). It is well known that there are pronounced sex differences in the development of renal disease with males developing a more severe pathology faster than age‐matched females (Clotet et al., 2016; Institute for Health Metrics and Evaluation, 2020; Layton & Sullivan, 2019; Maric‐Bilkan, 2020; Neugarten et al., 2000; Skrtic et al., 2017). However, the data from the present study suggest that the sex differences in GFR and RBF were not associated with severity of renal injury in eNOS−/− dbdb mice, a well‐known mouse model of type II diabetes. It is true that conflicting evidence exists regarding any sex difference in the development and progression of DN (Layton & Sullivan, 2019; Maric‐Bilkan, 2020). Our findings in the present study are consistent with our previous findings in the same mouse model (Ma, Li, et al., 2019). It is not known whether the lack of sexual dimorphism in this mouse model is attributed to the deletion of eNOS. The present study revealed that knockout of eNOS minimized the sex differences in RBF observed in WT C57BLKS/J mice. Comparable RBF levels between male and female eNOS−/− diabetic mice may lead to a similar course of DN in both sexes. One limitation to this study is that GFR and RBF were measured under completely different conditions. GFR was measured in awake mice and was the whole‐animal clearance (two kidneys) while RBF was measured in anesthetized, instrumented mice and was from one‐kidney. Therefore, the two parameters should be considered and interpreted separately, and that findings made under one set of conditions should not be projected to the other. For instance, in the present study, female 129/Sv and C57BLKS/J mice had greater GFR, but lower RBF. For the same reason, it is impossible to calculate filtration fraction and estimate the ratio of the afferent arteriolar tone to the efferent arteriolar tone, which are also factors indicating glomerular filtration function. In summary, the present study showed sex differences in GFR and RBF in mice. The sexual dimorphisms were strain dependent, and might also be age dependent. The sexual influences on GFR and RBF were independent of each other and were mediated by distinct mechanisms. eNOS was not involved in the sex differences in GFR, but contributed to the greater RBF in male mice. Furthermore, the sexual dimorphism did not impact the severity of renal injury in eNOS−/− dbdb mice. Since the mouse is the most common animal model used in renal research, our findings suggest that sexual impact should be considered with strain and age of mouse when studying renal function and disease progression. ## AUTHOR CONTRIBUTIONS Rong Ma and Keisa W. Mathis conceived and designed the study, analyzed data; prepared most figures, and drafted the manuscript. Yu Tao and Cassandra Young‐Stubbs performed experiments, interpreted the results of experiments, and prepared some figures; Parisa Yazdizadeh Shotorbani and Dong‐Ming Su bred and genotyped animals; all authors approved the final version of this manuscript. ## FUNDING INFORMATION The work was supported by National Institutes of Health Grant R01s (NIH/NIDDK, DK115424‐01 to R. Ma; NIH/NIHL, 1R01HL153703‐01A1 to KW. Mathis), the Translational Project Award from American Heart Association (20TPA35500045, to R. Ma), The Ideal Award from Department of Defense (LR210096 to KW. Mathis), and American Heart Association Predoctoral Fellowship (22PRE903925, to Y. Tao). ## CONFLICT OF INTEREST STATEMENT All authors declared no competing interests. ## ETHICAL APPROVAL All animal procedures were approved and performed in accordance with the guidelines and regulations of the Institutional Animal Care and Use Committee of the University of North Texas Health Science Center (UNTHSC). 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--- title: Methylcellulose colony assay and single-cell micro-manipulation reveal progenitor-like cells in adult human pancreatic ducts authors: - Janine C. Quijano - Lena Wedeken - Jose A. Ortiz - Heather N. Zook - Jeanne M. LeBon - Angela Luo - Jeffrey Rawson - Jacob R. Tremblay - Jacob M. Mares - Kassandra Lopez - Min-Hsuan Chen - Kevin Jou - Carlos Mendez-Dorantes - Ismail H. Al-Abdullah - Debbie C. Thurmond - Fouad Kandeel - Arthur D. Riggs - Hsun Teresa Ku journal: Stem Cell Reports year: 2023 pmcid: PMC10031308 doi: 10.1016/j.stemcr.2023.02.001 license: CC BY 4.0 --- # Methylcellulose colony assay and single-cell micro-manipulation reveal progenitor-like cells in adult human pancreatic ducts ## Summary Progenitor cells capable of self-renewal and differentiation in the adult human pancreas are an under-explored resource for regenerative medicine. Using micro-manipulation and three-dimensional colony assays we identify cells within the adult human exocrine pancreas that resemble progenitor cells. Exocrine tissues were dissociated into single cells and plated into a colony assay containing methylcellulose and $5\%$ Matrigel. A subpopulation of ductal cells formed colonies containing differentiated ductal, acinar, and endocrine lineage cells, and expanded up to 300-fold with a ROCK inhibitor. When transplanted into diabetic mice, colonies pre-treated with a NOTCH inhibitor gave rise to insulin-expressing cells. Both colonies and primary human ducts contained cells that simultaneously express progenitor transcription factors SOX9, NKX6.1, and PDX1. In addition, in silico analysis identified progenitor-like cells within ductal clusters in a single-cell RNA sequencing dataset. Therefore, progenitor-like cells capable of self-renewal and tri-lineage differentiation either pre-exist in the adult human exocrine pancreas, or readily adapt in culture. ## Graphical abstract ## Highlights •Single cells from human pancreatic ducts form colonies in a 3D culture•Ductal progenitor cells self-renew and differentiate to multiple lineages•Transplanted colonies further differentiate toward insulin-expressing cells•Single-cell RNA sequencing confirms some ductal cells have progenitor phenotypes ## Abstract In this work, Quijano, Wedeken, and colleagues isolated and characterized progenitor-like cells from the ducts of adult human pancreas. These ductal cells self-renew and differentiate into multiple pancreatic lineages in culture. When transplanted into mice, these cells further differentiate into insulin-expressing cells essential for regulating blood glucose levels, thus suggesting a potential utility for clinical use. ## Introduction Progenitor cells are distinguished by their ability to both self-renew and differentiate. These cells have been identified in many adult organs and can maintain tissue homeostasis and initiate repair of injuries. In the adult pancreas, there are three major cell lineages: ductal, acinar, and endocrine cells that include insulin-secreting beta cells. Studies of mouse embryos revealed that, in early (<E12.5) pancreas development, multi-potent progenitor cells (MPCs), expressing Sox9, Pdx1, and Nkx6.1 (Gu et al., 2002; Kopp et al., 2011; Nelson et al., 2007), can give rise to these three lineages in vivo (Gu et al., 2002; Kopp et al., 2011) as well as in vitro using a three-dimensional (3D) culture assay (Greggio et al., 2013). Using in vivo lineage-tracing strategies, some studies found that in adult mice ductal cells can also give rise to beta cells (Al-Hasani et al., 2013; Dirice et al., 2019; Gribben et al., 2021; Inada et al., 2008; Xu et al., 2008), while others refuted these findings (Kopp et al., 2011; Solar et al., 2009; Zhao et al., 2021). Although in vivo studies remain inconclusive, the use of certain 3D cultures has shown that some of the adult murine ductal cells self-renew and differentiate in vitro (Dorrell et al., 2014; Huch et al., 2013). For example, the 3D organoid assay established by Huch et al. [ 2013] showed that dissociated adult murine ductal cells and duct fragments can differentiate into endocrine and ductal, but not acinar, cell lineages in the presence of high concentrations of Matrigel. In contrast, the 3D colony assay system developed by our laboratory (Jin et al., 2013) uses methylcellulose, a biologically inert and viscous material, which allows us to lower Matrigel concentration to $5\%$ v/v and detect tri-lineage differentiation. In a methylcellulose-containing semisolid medium, cells cannot move and aggregate. Following the tradition of hematologists who call hematopoietic progenitor cells grown in a methylcellulose-containing culture medium “colony-forming units,” we named a pancreatic progenitor cell capable of giving rise to a colony a pancreatic colony-forming unit (PCFU). Using this system, quantifying colony-forming progenitor cells can be done with relative ease. In cadaveric human pancreatic ducts, previous reports have identified progenitor-like cells that are capable of duct and endocrine differentiation and some expansion (Bonner-Weir et al., 2000; Georgakopoulos et al., 2020; Lee et al., 2013; Loomans et al., 2018; Qadir et al., 2018). However, no human study of pancreas tissue thus far has utilized micro-manipulation of a single cell or colony to address lineage potential or lineage composition, respectively. Micro-manipulation is a technique that utilizes tools such as a pipette with a narrow opening to aspirate a cell or a colony of interest, one at a time, for subsequent downstream analysis (Tremblay et al., 2016). Such a clonal analysis is critical to ascertain multi-potency because a population of uni-potent progenitor cells for different lineages may collectively appear to be multipotent. Thus, despite the advances made in the aforementioned studies, no definitive evidence exists yet to demonstrate self-renewal and tri-lineage differentiation of adult human pancreatic progenitor cells. In this study, we describe a human colony assay system that reveals the self-renewal and tri-lineage differentiation abilities of an adult human ductal subpopulation. Single-cell RNA sequencing (scRNA-seq) analysis on dissociated exocrine cells confirms ductal cell heterogeneity, with a sub-cluster expressing genes consistent with progenitor cell phenotype. ## Establishment of a methylcellulose-based colony assay for adult human PCFUs We studied pancreatic exocrine tissues, which include ductal and acinar cells, from 41 cadaveric human donors without apparent disease (Table S1). These donors had an average age of 36 ± 14 years, body mass index of 30.4 ± 6.6 kg/m2, and hemoglobin A1c of 5.1 ± $0.3\%$ (Table S2). After islets were isolated, the pancreas tissue was dissociated into a single-cell suspension and was either cryopreserved or immediately plated into our colony assay system (Figure 1A). Our “standard” culture medium (Table S3) contains methylcellulose ($1\%$ w/v), non-defined extracellular matrix proteins (Matrigel; $5\%$ v/v) (Jin et al., 2013), and defined soluble factors (Nicotinamide, EGF, Noggin, Exendin4, SB202190, Gastrin, RSPO1, VEGF, and A83-01) that were inspired by culture conditions for adult human gastrointestinal stem cells (Bartfeld et al., 2015; Sato et al., 2011) and adult murine ductal progenitor cells (Huch et al., 2013; Wedeken et al., 2017). Using this colony assay system, we achieved $100\%$ isolation efficiency from every human exocrine tissue obtained to date, in contrast to $75\%$–$80\%$ shown by others (Boj et al., 2015).Figure 1Methylcellulose-based colony assay for adult human pancreatic progenitor cells capable of tri-lineage differentiation(A) Experimental diagram.(B) Representative bright-field image of colonies. Scale bar, 200 μm.(C) % PCFUs in dissociated exocrine tissues is $9.8\%$ ± $0.7\%$ ($$n = 31$$ donors).(D) Colony diameter = 394 ± 37 μm; mean ± SEM, ≥10 colonies per donor, $$n = 6$$ donors with 4 technical replicates.(E) Diameters of colonies between different donors ($$n = 5$$).(F) Mean diameter of colonies is positively correlated with the total number of cells per colony (R2 = 0.9801); mean ± SEM from 2 independent experiments and 20 individual colonies per data point.(G) TEM of 3-wo colonies displaying microvilli on the apical side (top, yellow arrow), nuclear invaginations (top, red arrow), and desmosomes (bottom, yellow arrows). Scale bars, 1 μm.(H) Micro-manipulation of individual colony for microfluidic qRT-PCR. Representative heatmap of lineage markers; $$n = 58$$ colonies, $$n = 7$$ donors. Gene expression frequency; mean ± SD.(I) IF staining confirms protein expression. Scale bar, 50 μm (insets enlarged 4×). Yellow arrow points to a representative cell that is triple-positive (TP) for SOX9, PDX1, and NKX6.1 and a red arrow for a non-TP cell. TP quantification represents mean ± SEM ($19.5\%$ ± $3.5\%$) from a total of 31 colonies from $$n = 3$$ donors. See also Figure S1B. After 3 weeks of culture morphologically distinct colonies formed, mostly appearing as hollow spheres (Figure 1B). The % PCFU, or colony-forming efficiency, varied among different donors with an average of $9.8\%$ ± $0.7\%$ ($$n = 31$$; range $2.4\%$–$18.6\%$) (Figure 1C). The mean diameter of a colony was 380 ± 42 μm (Figure 1D), with individual donors showing high variability of sizes (Figure 1E). Colonies were segregated into small, medium, and large categories (10 colonies each) and were dispersed into single cells to quantify the number of cells per category. We observed a strong positive correlation between colony size and cell number (Figure 1F; R2 = 0.98), indicating that colony size is predictive of the number of cells in that colony. The colony size can be indicative of the proliferative potential of the originating PCFU. Alternatively, the differences in colony size may be due to variable delay of cells entering replication or time needed for differentiation. Transmission electron microscopy (TEM) revealed that cells in colonies displayed microvilli facing the lumen (Figure 1G), suggesting apical polarization. Cells had nuclear invaginations and desmosomes at cell-cell junctions. These results indicate that a colony is composed of duct-like cells. Furthermore, 3D scanning electron microscopy (3D-SEM) analysis clarified that the walls of the colonies contained individual cells that were flat and elongated (Video S1). Also, microvilli were facing lumen and nuclei contained invaginations (Figure S1C); confirming that most cells in a colony are ductal and exhibit apical-basal polarity. Video S1. Cells in a 3-week-old colony, grown from dissociated pancreatic exocrine cells (unsorted) in the standard colony assay, are arranged mostly as a single-cell layerSerial block-face scanning electron microscopy (SBF-SEM), also known as 3D-SEM, was used to generate 500 serial images of a colony. Using Amira software, the images were stitched sequentially and presented as a movie. The movie starts by scanning through the cells from the top of the stacked images to the bottom, then scans from the bottom of the stacked images to the top. Next, the borders (including microvilli) of individual cells ($$n = 11$$) were segmented; each cell was painted a unique color. Translucent colors were rendered initially and then changed to solid colors to increase visualization of the arrangement of cells. Note a smooth side of the cell surface, which is facing away from the lumen and is adjacent to the methylcellulose and Matrigel-containing semisolid medium. The yellow cell has an elongated shape with cytoplasmic protrusions. The lineage potential of a PCFU is reflected in colonies expressing markers for various lineages. To determine the lineage potential of each PCFU, we micro-manipulated each 3-week-old (3-wo) colony by identifying it under a light microscope, picking the colony up with a pipette and placing that volume into a microcentrifuge tube (Figure 1H) pre-loaded with reagents for microfluidic qRT-PCR analysis (Jin et al., 2013). All individual colonies expressed high levels of markers for ductal (MUC1, KRT19, KRT7, HNF1B, SOX9, and PROM1), and multi-potent and endocrine progenitor cells (PDX1, NEUROG3, NEUROD2, MAFB, and NKX6.1; Figures 1H and S1A). The acinar cell marker AMY2A was also consistently expressed by all colonies. In contrast, the frequency of colonies that displayed at least one of the combined five endocrine markers (INS, GCG, PPY, SST, and GHRL) was only $41.7\%$ ± $15.5\%$, with ghrelin being the most frequent ($30.6\%$ ± $16.9\%$). Because all colonies expressed markers for ductal and acinar lineages, and $41.7\%$ of colonies expressed combined endocrine markers, these results demonstrate that approximately $40\%$ of adult human PCFUs are tri-potent. The lower expression of INS in colonies at this stage reflects suboptimal culture conditions rather than a lack of lineage potential, as is shown later in the transplantation study. Immunofluorescence (IF) staining verified protein expression of MUCIN1, KRT19, amylase, ghrelin, chromogranin A (a pan-endocrine marker), and CDH1 (a pan-epithelial marker) in 3-wo colonies (Figures 1I and S1B). MUCIN1 was detected at the surface of cells facing the lumen, confirming apical polarization. Because the transcription factors PDX1, SOX9, and NKX6.1 are known markers for the mouse and human embryonic MPCs (Jennings et al., 2013), we co-stained for these markers. On average, one-fifth of the cells in 3-wo colonies were triple-positive (TP) for SOX9+/PDX1+/NKX6.1+ (Figure 1I, $19.5\%$ ± $3.5\%$), demonstrating that a subset of cells within colonies display a progenitor cell phenotype. ## A micro-manipulated single PCFU is sufficient to give rise to a 3-wo colony expressing the three major pancreatic lineages To further ascertain the tri-lineage potential of PCFUs, we micro-manipulated freshly dissociated cells before culture by identifying single cells under a microscope and placing them into a 96-well plate at 1 cell per well (Figure 2A). Cells from the same donor were also plated into a standard colony assay as a control. Tracking each well of the 96-well plate for 3 weeks confirmed that a colony originated from one cell (Figures 2B and S1D). Compared with the control colonies, colonies derived from micro-manipulated single cells showed no significant difference in % PCFU (Figure 2C) and diameter (Figure 2D), suggesting that the formation of a colony is cell autonomous. Figure 2A micro-manipulated single PCFU is sufficient to give rise to a colony expressing the three major pancreas lineages, and identification of SOX9+/PDX1+/NKX6.1+ cells in endogenous ducts(A) Experimental diagram.(B) Time course bright-field imaging of a single PCFU grown into a colony. Scale bar, 20 μm (on day 0) and 50 μm (for all other days).(C and D) (C) % PCFUs and (D) mean diameter of colonies grown from 1 cell per well vs. multiple cells per well; mean ± SEM from 4 independent experiments using 3 donor tissues. Paired t test determined significance.(E) Microfluidic qRT-PCR analysis of colonies grown from unsorted cells plated at multiple cells per well (black) ($$n = 24$$ colonies, 3 independent experiments from 2 donors), vs. 1 cell per well (red) ($$n = 22$$ colonies); mean ± SD. Significance was determined by two-way ANOVA with Sidak’s multiple comparison.(F) IF staining of Pan-CK (green), CK19 (red), and MUC1 (white) in human pancreas. The image contains interlobular, intralobular, and intercalated ducts. Scale bar, 50 μm.(G) The same region in a sequential slide to (F) is stained with NKX6.1 (green), PDX1 (white), and SOX9 (red). Insets 1 and 2 highlight an interlobular and an intercalated duct, respectively; both contained TP cells (yellow arrows point to representatives of SOX9+PDX1+NKX6.1+ cells) and non-TP cells (red arrow points to representative of a SOX9+PDX1+NKX6.1− cell). Scale bar, 50 μm (insets enlarged 4×).(H) Quantification of % TP cells among total cells within stitched images; mean ± SEM ($10.2\%$ ± $2.6\%$), $$n = 4$$ donor tissues. See also Figures S1F and S2. Microfluidic qRT-PCR analysis on individual 3-wo colonies demonstrated that the frequency of colonies expressing tri-lineage markers was similar between colonies derived from plating with single vs. multiple cells per well (Figures 2E and S1E). Overall, a total of 306 micro-manipulated single cells from 3 independent experiments resulted in an average of 6.0 ± $2.1\%$ colony formation. Of those colonies, more than $15\%$ gave rise to 3-wo colonies expressing duct, acinar, and endocrine lineage markers (example colonies nos. 3 and 7 in Figure S1E), confirming the tri-potency of those PCFUs. ## Endogenous ducts contain TP cells The presence of TP cells in 3-wo colonies (Figure 1I) and the tri-potency of individually micro-manipulated PCFUs (Figure 2E) prompted us to examine the existence of TP cells in the adult human pancreas. Large and small ducts were identified by pan-CK, CK19, and MUC1 staining (Figures 2F and S2A). In the sequential slide, TP cells were identified in ductal areas (Figure 2G, yellow arrows; Figures S1F andS2B). Some SOX9+/PDX1+ ductal cells lacked NKX6.1 (Figure 2G, red arrows), suggesting heterogeneity. Overall, the TP cells constituted 10.2 ± $2.6\%$ of total pancreatic cells (Figure 2H), a frequency consistent to % PCFUs among dissociated exocrine cells (Figure 1C). ## Live-sorted human pancreatic CD133+CD49flow cells are enriched for ductal cells CD133 (PROM1) is a known ductal marker, and CD49f (ITGA6) co-expresses with CD133 in human fetal pancreas (Sugiyama et al., 2007). We therefore tested the utility of these cell surface markers in fluorescence-activated cell sorting (Figure 3A). Freshly dissociated exocrine cells were stained with antibodies against CD133 and CD49f and analyzed using flow cytometry (Figures 3B and S3A). All CD133+ cells expressed low levels of CD49f, indicating that CD49f did not improve adult ductal cell identification, but CD49f separated a CD133− (non-ductal) subpopulation (see population 8 [P8] below). The CD133+CD49flow cells (marked as P5) comprised 36.2 ± $3.5\%$ of total dissociated exocrine cells (Figure 3B). We next sorted four subpopulations: CD133+CD49flow (P5), CD133lowCD49flow (P6), CD133−CD49f− (P7), and CD133−CD49f+ (P8). Conventional qRT-PCR analyses revealed that, compared with unsorted cells (U), freshly sorted P5 cells expressed higher levels of the ductal marker KRT19 and low-to-undetectable levels of endocrine (INS) and acinar (AMY2A) markers (Figure 3C). Markers for leukocytes (CD45) and endothelial cells (KDR) were also expressed lower in P5 cells compared with unsorted cells (Figure 3C). Microfluidic qRT-PCR confirmed that freshly sorted, micro-manipulated single P5 cells expressed KRT7 and low levels of AMY2A (Figure 3D). Although most of the islets were already removed, infrequent GCG expressing cells remained in the unsorted population (Figure 3D). These results confirm that CD133+CD49flow cells are specifically enriched for ductal cells. Figure 3Live-sorted human pancreatic CD133+CD49flow ductal cells are enriched for PCFUs(A) Experimental diagram.(B) Representative sorting windows for four cell populations (P5, P6, P7, and P8) and their percentages; mean ± SEM, $$n = 5$$ donors.(C) qRT-PCR analysis of freshly sorted populations, compared with gene expression levels of the unsorted population (U) as fold change for ductal (KRT19, $$n = 6$$ donors), acinar (AMY2A, $$n = 5$$ donors), endocrine (INS, $$n = 5$$), endothelial (KDR, $$n = 3$$ donors), and leukocyte (PTPRC, $$n = 3$$ donors) cells; mean ± SEM.(D) Micro-manipulation of freshly sorted individual cells for microfluidic qRT-PCR analysis. Data are from $$n = 1$$ donor with n ≥ 11 single cells per population.(E) PCFU among sorted populations compared with unsorted (U) population, expressed as % PCFU (left) or fold change (right) ($$n = 7$$ donors; mean ± SEM).(F) Gene expression frequencies of colonies grown from unsorted (U, black) or sorted (P5, red) cells. U: $$n = 32$$ colonies, $$n = 4$$ donors. P5: $$n = 38$$ colonies, $$n = 5$$ donors, mean ± SD. Significance was determined by two-way ANOVA, with Sidak’s multiple comparison.(G) Ultrastructure analysis of P5-derived colonies displaying microvilli on the apical side (left, yellow arrow), nucleus invagination (left, red arrow), and desmosomes (right, yellow arrows). Scale bar, 1 μm (left) or 0.5 μm (right). ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001.$ See also Figures S3A–S3E. ## Sorted human pancreatic CD133+CD49flow ductal cells are enriched for PCFUs To assess which pancreatic subpopulation(s) were enriched for PCFUs, sorted cells were plated in our standard colony assay. Unsorted cells from this cohort displayed an overall % PCFU of 7.8 ± $1.3\%$ (Figure 3E, left). Compared with unsorted cells, only P5 displayed a higher (1.9- ± 0.4-fold) % PCFU (Figure 3E, right). The 3-wo colonies grown from P5 cells also appeared as hollow spheres (Figure S3B), with no donor-to-donor variation in colony diameter (Figure S3C). The mean diameter of colonies grown from P5 cells was 316 ± 34 μm (Figure S3D), which is comparable to that of colonies grown from unsorted cells (compare Figure S3D with 1D; $p \leq 0.05$). Gene expression patterns and frequencies of P5-derived individual colonies were similar to those derived from unsorted cells (Figures 3F and S3E), suggesting that P5-derived PCFUs are tri-potent. Finally, TEM of colonies grown from P5 cells displayed microvilli facing the lumen, nuclei with invaginations, and desmosomes at cell-cell junctions (Figure 3G). Overall, these results demonstrate that sorting does not significantly impact the growth, differentiation, or colony morphology of human PCFUs, and that PCFUs are derived from the ducts. Due to the unchanged colony phenotypes and logistic limitation with cadaveric tissues, unsorted cells were used for subsequent experiments. ## Adult human PCFUs self-renew and expand up to 300-fold over 9 weeks in the presence of a ROCK inhibitor We assessed the self-renewal abilities of PCFUs using serial dissociation of colonies into single cells and re-plating from 1° through 3° cultures (Figure 4A). After 9 weeks, the number of PCFUs only expanded about 3-fold in our standard culture (Figures 4B and 4D). Prior studies found that inhibition of Rho-associated protein kinase (ROCK) enhances the survival of fetal murine pancreatic progenitor cells in vitro (Greggio et al., 2013), and that Notch activation is required for maintaining the progenitor cell pool (Apelqvist et al., 1999). We therefore tested the effects of Y-27632, a ROCK inhibitor, and Jag1/Fc, a Notch activator, on PCFU self-renewal. Compared with the control, addition of Y-27632 with or without Jag1/Fc increased the number of PCFUs over 9 weeks by 302- ± 126-fold or 136- ± 78-fold, respectively (Figure 4B). Y-27632 treatment also increased 1° colonies (Figure 4C), indicating enhanced PCFU survival. In contrast, Jag1/Fc alone did not affect PCFU self-renewal (Figures 4B and 4D) or survival (Figure 4C), even though Jag1/Fc could increase MKI67 expression in colonies at days 14 and 21 (Figure S3F). In the 3° culture, % PCFUs among total cells plated with Y-27632 was significantly higher than the no addition control (Figure S3G), suggesting PCFU exhaustion over time without ROCK inhibition. These results demonstrate that ROCK inhibition, but not Notch activation, is sufficient for self-renewal of human PCFUs. Figure 4Adult human PCFUs self-renew and expand up to 300-fold(A) Experimental diagram.(B) PCFU fold change (left) and total PCFU (right); mean ± SEM, $$n = 4$$ donors from 5 (groups containing Y-27632 ± Jag1/Fc) or 6 (control and Jag1/Fc alone) independent experiments, with 4 technical replicates per plating.(C and D) Further analysis of data from (B); PCFUs in the 1° culture (C) or on the 3° culture (D).(E) Microfluidic qRT-PCR analysis of n ≥ 8 individually handpicked colonies per donor collected from the 3° culture, grown in the presence of Y-27632 (red; $$n = 28$$ total colonies from $$n = 3$$ donors) or Y-27632 and Jag1/Fc (green; $$n = 28$$ total colonies from $$n = 3$$ donors). Black: same data as Figure 1H; mean ± SD. Significance was determined by two-way ANOVA, with Tukey’s multiple comparison. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001.$ See also Figures S3F–S3H. To determine whether PCFU tri-potency was preserved after expansion, individual 3° colonies from three donors were micro-manipulated and analyzed using microfluidic qRT-PCR. Only 3° colonies cultured with Y-27632, with or without Jag1/Fc, were analyzed. Similar to 1° colonies (Figure 1H), 3° colonies collectively expressed the three main pancreatic lineage markers (Figures 4E and S3H). There was no difference in the frequency of 3° colonies expressing pancreas lineage markers between colonies grown with Y-27632 and with the combination of Y-27632 + Jag1/Fc (Figure 4E). Interestingly, when comparing the 3° colonies grown with Y-27632 to 1° colonies grown in our standard culture, there was a reduction in the frequency of colonies expressing AMY2A and GHRL, but an increase of INS (Figure 4E). Overall, these data demonstrate that tri-potent PCFUs are preserved over 9 weeks in culture. ## Notch inhibition enhances endocrine progenitor gene expression profiles in human colonies Many micro-manipulated individual CD133+CD49flow cells expressed HES1 (Figure 3D), a known Notch target gene. The relative low expression of endocrine genes in colonies grown in our standard culture (Figure 1H) prompted us to test Notch inhibition, because the reduction of HES1 can de-repress NEUROG3 gene expression (Lee et al., 2001; Shih et al., 2012), which is necessary for endocrine lineage commitment (Apelqvist et al., 1999; Jensen et al., 2000; Murtaugh et al., 2003). DAPT is a small molecule that inhibits gamma-secretase and is known to reduce HES1 expression levels (Kopinke et al., 2011). It is known that the timing of Notch inhibition is critical for proper endocrine differentiation in vivo (Cras-Meneur et al., 2009) and in vitro (Shih et al., 2012; Wedeken et al., 2017). Addition of DAPT to human colonies on day 10 (Figure 5A) decreased HES1 and increased NEUROG3 expression compared with the vehicle control (Figure 5B). Quantification of IF staining confirmed a reduction of HES1+ cells and an increase of NEUROG3+ cells in DAPT-treated colonies (Figures 5C, 5D, and S4A). DAPT treatment reduced % PCFU (Figure 5E) and colony size (Figure 5F), suggesting that Notch signaling is necessary for the survival and growth of PCFUs, similar to behavior from pancreatic progenitors (Apelqvist et al., 1999).Figure 5Notch inhibition enhances endocrine gene expression in human colonies(A) Experimental diagram.(B) Colonies analyzed for HES1 and NEUROG3 gene expression by qRT-PCR; mean ± SEM, $$n = 13$$ experiments, $$n = 9$$ donors.(C) IF of colonies treated with DMSO (left) or DAPT (right) with HES1 (green, top) or NEUROG3 (green, bottom) and DAPI (blue). Yellow star (∗, bottom-right) denotes an area of non-specific staining. Scale bar, 50 μm (box enlarged 4× to the right).(D) IF quantification of percent positive cells. $$n = 30$$–31 colonies treated with DMSO or DAPT from $$n = 3$$ donors. Each dot represents a colony.(E) % PCFU (fold change); mean ± SEM, $$n = 13$$ donors.(F) Average colony diameter; mean ± SEM, $$n = 8$$ donors.(G) Fold change of gene expression from individual colonies examined by microfluidic qRT-PCR analysis; mean ± SD, $$n = 73$$–78 colonies treated with DMSO or DAPT, $$n = 5$$ donors. Each dot represents a colony.(H) IF analysis of endocrine markers in DMSO-treated colonies (left) or DAPT (right). Scale bar, 50 μm (insets enlarged 4× to the right).(I) IF quantification of percent positive cells; mean ± SD, $$n = 30$$ DMSO and $$n = 34$$ DAPT, $$n = 3$$ donors. Each dot represents a colony.(J) DAPT-treated colonies were analyzed using TEM and three-dimensional scanning electron microscopy (3D-SEM). Left: a representative TEM photomicrograph of a portion of a cell showing vesicles with granules. Scale bar, 500 nm. Right: the area of the vesicles containing insulin-like granules were measured in 3 cells. Data represent areas from 55, 67, and 56 individual vesicles from cells 1, 2, and 3, respectively. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001.$ See also Figure S4. Microfluidic qRT-PCR analysis of individual colonies revealed that DAPT did not increase the frequency of tri-lineage colonies (Figure S4B) but increased the expression of several beta cell maturation markers, including SLC2A1, NKX6.2, NEUROD2, and UCN3 (Figure 5G). IF staining and quantification confirmed that an increased proportion of cells within DAPT-treated colonies express these maturation markers (Figures 5H, 5I, and S4C). These results demonstrate that Notch inhibition directs differentiation toward an endocrine phenotype in our colonies but does not change a PCFU from bi- to tri-potent. To clarify if DAPT induced the formation of insulin vesicles, we performed TEM analysis of DAPT-treated colonies and found condensed insulin granules in vesicles (Figure 5J), which were not observed in control colonies. Using 3D-SEM, we analyzed 178 non-overlapping insulin vesicles from 3 cells and found that the mean area was 0.17 ± 0.01 μm2/vesicle (Figure 5J). The vesicles in DAPT-treated cells were slightly smaller than reported endogenous insulin vesicles (0.19 μm2/vesicle) (Fava et al., 2012). Also, the insulin granules appeared faint compared with adult beta cells, suggesting functional immaturity (Ni et al., 2017). ## DAPT-treated human colonies give rise to insulin-expressing cells in hyperglycemic mice We determined whether DAPT-treated human colonies may further differentiate and become functionally mature in insulin-dependent diabetic NOD-SCID mice (Figure 6A). Streptozotocin was injected to mice to destroy beta cells and induce hyperglycemia (fasting blood glucose >200 mg/dL). Subsequently, 3-wo DAPT-treated colonies were pooled and placed under the kidney capsule at 1–2.5 million cells per mouse. An insulin pellet was inserted when fasting blood glucose exceeded 450 mg/dL to minimize the detrimental effects of overt hyperglycemia (Brereton et al., 2016). To test graft function without interference, insulin pellets were not inserted 60 days post-transplantation. Colonies from 4 different donor tissues were independently transplanted into multiple mice (n ≥ 2 mice per donor tissue, 15 mice total).Figure 6DAPT-treated human colonies give rise to insulin-expressing cells in insulin-dependent diabetic mice(A–C) (A) Experimental diagram. Data were analyzed between day 90 and 120 post-transplantation for (B) blood glucose and (C) body weight, with individual mice separated by sham-operated control (black, $$n = 11$$), responder (red, $$n = 7$$) and non-responder (blue, $$n = 8$$) mice. Data represent median blood glucose or average body weight ± SEM.(D) IP-GTT analysis on control (black, $$n = 5$$), responder (red, $$n = 6$$), and non-responder (blue, $$n = 5$$) mice. AUC was analyzed as mean ± SD.(E) Human C-peptide in serum, expressed as relative C-peptide fold change between time 0 and 60 min post glucose challenge from control (black, $$n = 4$$), responder (red, $$n = 6$$), and non-responder (blue, $$n = 3$$) mice.(F) Bright-field image of a kidney grafted with DAPT-treated human colonies. Grafted cells are outlined (white dashed line). Scale bars, 3 mm (left) or 1 mm (right).(G) H&E staining of a kidney graft, with kidney tissue shown at the bottom of the image. Scale bar, 200 μm.(H–K) IF staining of grafted cells with INS (green) (G and H), GCG (white) (H), UCN3 (white) (J), or SLC2A1 (white) (K). Scale bar, 20 μm. ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ See also Figures S5 and S6. Between days 90 and 120 post-transplantation, body weight was similar (Figure S5A) while median fasting blood glucose trended lower but did not reach significance in diabetic mice transplanted with colonies compared with sham controls (Figure S5B; individual mouse data in Figure S5C). However, a difference was found when we ranked transplant recipients based on their median fasting blood glucose, separating the top 8 from the bottom 7 mice—the bottom 7 mice (herein “responders”) displayed a median fasting blood glucose at ∼200 mg/dL (Figure 6B). Control diabetic mice did not approach a median fasting blood glucose of 200 mg/dL (Figure 6B), suggesting a lack of beta cells; this was confirmed using H&E staining (Figure S5D). Again, body weights were not different between responders and non-responders (Figure 6C). Importantly, colonies from each donor tissue transplanted into diabetic mice resulted in at least one responder mouse (Figure S5E); this suggests that 1–2.5 million DAPT-treated cells per mouse are the marginal mass, the minimum number of cells necessary to reverse hyperglycemia in some but not all transplanted diabetic mice. To further analyze graft function, we performed an intra-peritoneal glucose tolerance test (IP-GTT), which revealed a trend of better glucose clearance in transplanted mice (Figure S5F), with a significant decrease in mean area under the curve (AUC) in the responder mice (Figure 6D). Human C-peptide levels in blood 1 h after glucose stimulation significantly increased in the responder mice (Figure 6E). However, stimulated human C-peptide concentration was 2.8 ± 1.9 pmol/L, which was much lower than the 366 ± 154 pmol/L of human C-peptide detected in hyperglycemic NOD-SCID mice transplanted with 1,200 human islets (Table S1) 90–150 days post-transplantation (Figure S5G). This result prompted us to calculate the ratio of blood glucose to human C-peptide in individual mice; a lower ratio indicates a better function of the graft (Takita and Matusmoto, 2012). The colony transplanted mice showed higher ratio than the mice with human islet grafts (Figure S5H), suggesting functional immaturity of our grafted colonies in vivo compared with adult islets. To detect beta-like cells in the transplant mice, we dissected the kidney grafts (Figures 6F and 6G) 3–4 months post-transplantation for IF analysis. INS+ cell clusters that did not co-express GCG were found (Figures 6H and 6I), suggesting the presence of mono-hormonal beta-like cells (Herrera, 2000). Some INS+ cells also co-expressed beta cell maturation markers UCN3 or SLC2A1 (Figures 6J, 6K, S6A, and S6B). Together, these results show that colonies pre-treated with DAPT can differentiate into beta-like cells after transplantation into diabetic mice. ## scRNA-seq analysis reveals a subset of ductal cells as progenitor-like cells To gain insight into gene expression patterns of human pancreatic ducts, we performed scRNA-seq analysis using freshly dissociated exocrine tissue. An estimated 14,822 cells were read at 103,333 mean reads per cell. Events that passed quality control (7,812 cells, Figures S7A and S7B) were subsequently analyzed using the Seurat R package. Using Uniform Manifold Approximation and Projection dimensional reduction, we identified clusters of acinar (PTF1A: 5,094 cells), ductal (KRT19: 2,119 cells), immune (PTPRC: 326 cells), endothelial (KDR: 185 cells), stellate (RGS5: 50 cells), and endocrine cells (GCG: 38 cells) (Figures 7A and 7B). Consistent with the sorting results (Figure 3), the ductal cell cluster expressed PROM1 (CD133) with minimal expression of ITGA6 (CD49f) (Figure S7C).Figure 7Human ducts are heterogeneous with a subset resembling progenitor-like cells(A) scRNA-seq of dissociated adult human exocrine tissue identifies 6 distinct clusters.(B) Violin plots of representative gene markers.(C) The ductal cluster was re-analyzed by principal-component analysis and segregated into four unique clusters (0–3).(D) Heatmap of cluster-specific genes identified in the four ductal clusters.(E) IPA predicted up and downregulated pathways using DE genes from the four ductal clusters.(F) Cells simultaneously expressing SOX9, PDX1, and NKX6.1 are $3.6\%$ of the total cells, and only in the ductal cell population.(G) Uniform Manifold Approximation and Projections (UMAPs) of the ductal cluster showing the expression of the SOX9, PDX1, or NKX6.1, with percent positive cells calculated in each ductal cluster.(H) TP cells are enriched in ductal cluster 0.(I) IPA-predicted upregulated pathways in TP cells.(J) StemID cluster 4 cells (blue) are overlaid with TP cells (red) within the ductal cluster.(K) IPA-predicted upregulated pathways in StemID cluster 4. See also Figure S7 and Data S1. To identify potential progenitor cell population within ductal cells, we performed unbiased principal-component analysis of the ductal cluster and found 4 distinct subpopulations (Figure 7C); the top 10 cluster-specific genes are presented in a heatmap (Figure 7D; differentially expressed [DE] genes in Data S1). Cluster 0 was identified with progenitor genes such as JUN and FOS (Goncalves et al., 2021), whereas cluster 3 showed more mature ductal genes such as KRT19 and KRT7. Bioinformatics analysis using Ingenuity Pathway Analysis (IPA) software revealed the top 6 predicted upregulated and top 5 downregulated canonical pathways for cluster 0, which showed both similar and divergent pathways against other ductal clusters (Figure 7E). These results support the heterogeneity hypothesis on adult human pancreatic ductal cells. TP cells were exclusively found in the ductal cluster (Figure 7F), consistent with IF analysis in ducts (Figures 2G and 2H). Independent assessment of SOX9, PDX1, or NKX6.1 expression in total cells (Figure S7D) and within the four ductal clusters revealed that PDX1 and NKX6.1 were the limiting factors (Figure 7G). Overall, TP cells were $3.6\%$ of total cells (Figure 7F) and $13.4\%$ of ductal cells (Figure 7H), with a majority found in cluster 0 (Figure 7H); these percentages were within range of colony-forming efficiency of unsorted and sorted CD133+CD49flow ductal (P5) cells (Figure 3E, left). DE genes between the TP cells and other non-TP ductal cells (Data S1) were analyzed by IPA, which revealed upregulated Sirtuin, HIPPO, PTEN, and Wnt-β catenin pathways in TP cells (Figure 7I). These pathways are known to be involved in pancreas development. The StemID algorithm (Grun et al., 2016) was applied to independently re-cluster all of the ductal cells, and each new cluster according to their stem potential was scored. We selected the cluster with the highest score, StemID cluster 4 (Figures S7E–S7I), and re-mapped those cells to the original clustering analysis. StemID cluster 4 was mapped to $19.7\%$ of the total ductal cells and $36.5\%$ of cluster 0 ductal cells (Figure 7J). DE gene expression of StemID cluster 4 compared with other ductal cells (Data S1) was analyzed in IPA. Predicted upregulated pathways, such as BAG2, Sirtuin, and PTEN signaling, were identified in the StemID cells (Figure 7K); they were also found in the TP cell analysis, indicating similarities between the two populations. Together, these data provide evidence that a subset of ductal cells have progenitor properties. ## Discussion In this study, we show evidence for the existence of self-renewing progenitor-like cells from the adult human pancreatic ducts, which we call PCFUs. By using single-cell micro-manipulation, we provide a rigorous demonstration of multi-lineage differentiation potential of PCFUs in vitro. In addition, we show for the first time that colonies and adult human ducts in vivo contain cells capable of expressing embryonic MPC markers (SOX9+/PDX1+/NKX6.1+), which we call TP cells. Tremendous progress has been made in 3D organoid technology. Many current organoid culture techniques are modeled after a study that established epithelial organoid culture using Matrigel (Sato et al., 2011), where high concentrations (>$90\%$ v/v) of Matrigel are used to embed and immobilize cells. However, it is difficult to micro-manipulate individual organoids; one reason is the elevated viscosity and rigidity caused by the high Matrigel concentrations. Our 3D colony assay differs in that viscous methylcellulose is added (Perko et al., 2011), allowing the dilution of Matrigel to a much lower concentration ($5\%$ v/v in this study) and aiding micro-manipulation. Using organoid culture systems based on the intestinal organoid platform from Sato et al., several studies show that pancreatic ductal cells can expand in vitro, but those ductal cells possess only two lineage potential (duct and endocrine) or with limited self-renewal. Loomans et al. [ 2018] found that the net expansion of total ductal cells was approximately 20-fold over 20 weeks. Lee et al. [ 2013] observed an 8-fold increase of total cells over 6–9 weeks, and Georgakopoulos et al. [ 2020] showed an impressive expansion of organoids for over 15 weeks. In contrast, rather than mechanical digestion into cell clumps as in the other studies, we expanded our colonies by enzymatic digestion to single cells during passaging, observing up to 300-fold expansion of PCFUs over 9 weeks (Figure 4). In addition, PCFUs comprise ∼$8\%$ of the total cells in the 3° culture (Figure S3G); therefore, total expansion of our ductal cells is calculated to be about 3,750-fold. We also report that colonies after expansion maintain the same gene expression patterns as colonies from the first culture, showing preservation of tri-lineage potency (Figure 4E). Thus, PCFUs may represent the true self-renewing progenitor cells from the adult human pancreas. With respect to lineage potential, Loomans et al. [ 2018] showed that progenitor-like cells marked by ALDHhigh staining possess duct and endocrine lineage potential. In contrast, our PCFUs possess duct, endocrine, and acinar lineage potential. Lee et al. [ 2013] reported that their ductal cells cannot be transdifferentiated into INS+ cells in organoid culture unless forced to express NGN3, MAFA, and PDX1. Interestingly, Qadir et al. [ 2018, 2020] demonstrated that sorted adult human P2RY1+/ALK3bright cells, which are found in the main ducts, can give rise to the three major pancreatic lineages using a 2D attachment culture system. Our data agree with their findings that some ductal cells possess tri-lineage potential. However, Qadir et al. did not report the self-renewal capacity of the P2RY1+/ALK3bright cells, which is an important aspect of progenitor cells. Cellular compartments in the adult pancreas had been largely considered homogeneous, but increasing evidence suggests that endocrine (Baron et al., 2016; Butler et al., 2010), acinar (Kusmartseva et al., 2020), and ductal cells (Baron et al., 2016; Grun et al., 2016; Qadir et al., 2020) are heterogeneous. In this study, we not only confirm ductal cell heterogeneity among adult human exocrine tissue (Figures 2G and 7), but we also add functional heterogeneity in colony formation among sorted human ductal cells (Figure 3E). This ductal cell heterogeneity may explain the difficulties of Cre-lox lineage tracing using a pan-duct marker, such as Sox9 (Kopp et al., 2011) or Hnf1b (Solar et al., 2009), to detect significant activities of adult murine pancreatic progenitor cells due to the relatively minor population of progenitor cells. In addition, subtle differences in the expression levels of progenitor cell markers may dictate functionality (Rezanejad et al., 2018). Thus, to address ductal cell heterogeneity further, future experiments are needed to identify unique cell surface markers in combination with CD133, but not CD49f, that enrich or purify the ductal progenitor cells. A potential clinically relevant finding of our study is that DAPT-treated colonies grafted into diabetic mice give rise to beta-like cells in vivo. Although, our grafts did not result in high levels of human C-peptide, our transplant mice did show an observable drop in blood glucose levels (i.e., in responder mice) between 3 and 4 months post-transplantation (Figure 6B). These results raise the possibility for proinsulin, rather than C-peptide or insulin, as the predominant form of the insulin gene product that is secreted from our colony grafts—a possibility that requires future investigation. Proinsulin has been reported to exert biological effects in development and various adult cell types (Malaguarnera et al., 2012) and therefore may provide clinically beneficial effects to the hyperglycemic mice. Loomans et al. [ 2018] transplanted ductal organoids under the kidney capsule of mice (up to 4.5 × 105 cells per mouse) and detected INS+KRT19− cells. However, their mice were followed for only 1 month post-transplantation; it remains unknown whether their INS+ cells improve glucose regulation over a longer period. Pluripotent stem cell (PSC)-derived insulin-expressing cells have been shown to regulate blood glucose levels in insulin-dependent diabetic mice after transplantation (Migliorini et al., 2021). However, there is the concern of teratoma formation from undifferentiated PSCs (Cunningham et al., 2012). In contrast to PSCs, adult stem cells do not give rise to teratomas. Thus, should PSC-derived products raise safety concerns in future clinical trials, adult PCFUs can be a suitable alternative source of insulin-expressing cells. In summary, we have shown in functional in vitro assays that some adult human ductal cells, resembling progenitor cells, are capable of tri-lineage differentiation and self-renewal in a unique 3D methylcellulose-containing culture system. Also, we identified a subset of human pancreatic ductal cells capable of expressing TP progenitor markers through IF and in silico analysis. Given the severe shortage of donor organs, our results suggest a potential utility of human cadaveric ductal tissues for therapy in insulin-dependent diabetic patients. ## Corresponding author The data that support the findings of this study are available from the corresponding author, Janine C. Quijano (jquijano@coh.org), upon reasonable request. ## Materials availability This study did not generate new unique reagents. ## Single-cell suspension Donated pancreata were procured and shipped to City of Hope for isolation of islets (Qi et al., 2015). All tissues used in this study had consent for research from close relatives of the donors. After islet removal, de-identified human pancreata were obtained from the Southern California Islet Cell Resource (SC-ICR) Center at the City of Hope. The exocrine tissue was dissociated to yield a single-cell suspension before cryopreservation, culture, or other procedures. ## Mice Mice used in this study were maintained according to protocols approved by the City of Hope Institutional Animal Care and Use Committee. Additional detailed experimental methods are provided in the supplemental information. ## Author contributions Conceptualization, J.C.Q., L.W., and H.T.K.; methodology, J.C.Q., L.W., and H.T.K.; software, M.-H.C.; formal analysis, J.C.Q. and L.W.; investigation, J.C.Q., L.W., J.A.O., J.M.L., A.L., J.R., J.M.M., K.L., H.N.Z., J.R.T., K.J., and C.M.-D.; resources, I.H.A. and F.K.; writing – original draft, J.C.Q., L.W., and H.T.K.; writing – review & editing, J.C.Q., L.W., J.A.O., H.N.Z., J.R.T., I.H.A., D.C.T., F.K., A.D.R., and H.T.K.; supervision, J.C.Q., L.W., and H.T.K.; funding acquisition, J.C.Q., L.W., and H.T.K. ## Supplemental information Document S1. Figures S1–S7, Tables S1–S5, and supplemental experimental procedures Data S1. Differential expressed (DE) genes between various ductal clusters, related to Figure 7 Document S2. Article plus supplemental information ## Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Single-cell RNA sequencing (scRNA-seq) data are available from Gene Expression Omnibus (GEO) database: GSE153834. ## Conflict of interests H.T.K. maintains a patent no. 9,783,784 titled “Methods for establishing and improving the survival of a population of pancreatic progenitor or stem cells.” The other authors declare no competing interests. ## References 1. Al-Hasani K., Pfeifer A., Courtney M., Ben-Othman N., Gjernes E., Vieira A., Druelle N., Avolio F., Ravassard P., Leuckx G.. Adult duct-lining cells can reprogram into beta-like cells able to counter repeated cycles of toxin-induced diabetes. Dev. Cell (2013) 26 86-100. 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--- title: Biomarkers for the severity of periodontal disease in patients with obstructive sleep apnea:IL-1 β, IL-6, IL-17A, and IL-33 authors: - Mayra A. Téllez Corral - Eddy Herrera Daza - Natalia Arango Jimenez - Darena Z. Morales Vera - Juliana Velosa Porras - Catalina Latorre Uriza - Francina M. Escobar Arregoces - Patricia Hidalgo Martinez - María E. Cortés - Liliana Otero - Claudia M. Parra Giraldo - Nelly S. Roa Molina journal: Heliyon year: 2023 pmcid: PMC10031375 doi: 10.1016/j.heliyon.2023.e14340 license: CC BY 4.0 --- # Biomarkers for the severity of periodontal disease in patients with obstructive sleep apnea:IL-1 β, IL-6, IL-17A, and IL-33 ## Abstract Clinic significance: Periodontitis and OSA have a bidirectional-relationship: OSA may increase the risk of developing periodontitis, and periodontitis worsens the inflammatory process in OSA. ### Objective This study aims to compare the salivary and gingival crevicular fluid (GCF) concentrations of five cytokines: IL-1β, IL-6, IL-17A, IL-33, and Tumor Necrosis Factor-alpha (TNF-α) in patients with OSA and their association with periodontitis. ### Methods Samples of saliva and GCF were obtained from 84 patients classified into four groups according to periodontal and OSA diagnosis: G1(H) healthy patients, G2(P) periodontitis and non-OSA patients, G3(OSA) OSA and non-periodontitis patients, and G4(P-OSA) periodontitis and OSA patients. The cytokines in the samples were quantified using multiplexed bead immunoassays. Data were analyzed with the Kruskal-Wallis test, Dunn's multiple comparisons test, and the Spearman correlation test. ### Results Stage III periodontitis was the highest in patients with severe OSA ($69\%$; $$p \leq 0.0142$$). Similar levels of IL-1β and IL-6 in saliva were noted in G2(P) and G4(P-OSA). The IL-6, IL-17A and IL-33 levels were higher in the GCF of G4(P-OSA). There was a significant positive correlation between IL-33 in saliva and stage IV periodontitis in G4(P-OSA) (rs = 0.531). The cytokine profile of the patients in G4(P-OSA) with Candida spp. had an increase of the cytokine's levels compared to patients who did not have the yeast. ### Conclusions OSA may increase the risk of developing periodontitis due to increase of IL-1β and IL-6 in saliva and IL-6, IL-17A and IL-33 in GCF that share the activation of the osteoclastogenesis. Those cytokines may be considered as biomarkers of OSA and periodontitis. ## Highlights •There was a higher prevalence of stage III periodontitis and severe OSA.•Higher levels of periodontal parameters were correlated with the cytokine's expression.•Pro-inflammatory cytokines were increased in patients with periodontitis and OSA.•The IL-33 was correlated to stage IV periodontitis. ## Introduction Periodontitis is a chronic infectious disease caused mainly by strict anaerobic microorganisms [1] that cause an inflammatory response in periodontal tissue, developing periodontal pockets and progressive loss of periodontal insertion and destruction of teeth's supporting structure. These events may be a risk factor for systemic disorders such as cardiovascular disease [2], diabetes [3], rheumatic arthritis [4], and obstructive sleep apnea (OSA) [5,6]. OSA is characterized by the collapse of the upper airway during sleep, resulting in partial or complete obstruction of airflow [7]. OSA is the most common disturbance of sleep condition, with almost 1 billion people affected in the world, and with a prevalence exceeding $50\%$ in some countries, like China, USA, Brazil and India. In Colombia, the prevalence of OSA is approximately 19–$26\%$ [8,9]. OSA is associated with a considerably higher risk of cardiovascular illness, such as high blood pressure, coronary heart disease, atrial fibrillation, stroke, diabetes, cancer and death, as well as a severe deterioration in the quality of life and functional capacity [10]. Moreover, OSA has also been associated with periodontitis. Studies in different countries determined that the prevalence of periodontitis is 60–$96\%$ in patients with OSA [5,[11], [12], [13], [14]]. Sanders et al. [ 2015] found that Latin Americans with OSA have a higher risk of severe periodontitis than those without OSA [15]. Latorre et al. [ 2018] determined the clinical association between periodontitis and OSA in patients with hypertension [16]. The prevalence of periodontitis and OSA may be linked to systemic inflammation. Patients with OSA have higher levels of inflammatory cytokines, adhesion molecules, and activation of circulating neutrophils [17]. The chronic inflammation that underlies OSA might be linked to hypoxia and elevated CO2 levels [18], which could activate transcription factors like NF-κβ, promote the generation of cytokines, reactive oxidant species, and other systemic inflammatory mediators [11,19]. Serum and saliva cytokine levels have been evaluated as candidate biomarkers for the association between OSA and periodontal disease. The association of both conditions might increase the severity of disease by rising the levels of salivary IL-6 [18] and IL-33 [20], changing the composition of biofilm microorganisms, particularly in patients with moderate or severe OSA [21,22]. Il-6 levels in the gingival crevicular fluid (GCF) of OSA patients have not been reported. Other cytokines with pro-osteoclastogenic effects, such as IL-17, may also contribute to the pathogenesis of periodontitis and other diseases [23], but their role in the relationship between periodontitis and OSA has not been investigated. Furthermore, higher IL-1β levels in GCF and similar levels of TNF-α have been demonstrated in patients with OSA and periodontitis [5,18]. Although proinflammatory cytokine concentrations in OSA patients have been documented, some published saliva and serum results are inconsistent. Additionally, there are no studies comparing the levels of cytokines in saliva and GCF in individuals with periodontitis and OSA, to individuals who only have periodontitis or OSA, therefore it is unknown what function IL-17A plays in OSA patients. Téllez et al. [ 2022] found that patients with OSA and periodontitis are associated with medical records and that the microorganisms of the orange and red complexes participate in this association [21]. The formation of the dysbiotic biofilm was mainly related to the presence of these complexes in association with Candida spp., and it could be related with the inflammation as a factor shared between OSA and periodontitis. Further, the pro-inflammatory cytokine levels are increased in serum, saliva and Gingival Crevicular Fluid (GCF) in patients with OSA due to their periodontal condition or indicating a bidirectional relationship between the two conditions, linking periodontitis with OSA. Since both periodontitis and OSA are associated with systemic inflammations possibly involving similar pathways, the objective of this study was to compare the expression of pro-inflammatory cytokines IL-1β, IL-6, IL-17A, IL-33, and TNF-α simultaneously in saliva and GCF in patients with OSA as biomarkers associated with the periodontal condition and its clinical status. ## Study population and samples A convenience sample of 84 elegible patients that fulfilled the inclusion criteria (48 women and 36 men; aged between 30 and 71 years) were referred from the Sleep Clinic of the Hospital Universitario San Ignacio and the Sleep Clinic of the Faculty of Dentistry at the Pontificia Universidad Javeriana-PUJ, Bogotá, D.C., Colombia. The present study was carried out following the Declaration of Helsinki of 1975, revised in 2000, and approved by the Research and Ethics Committee of the Faculty of Dentistry (CIEFOUJ No. 005). Following the explanation of their conditions and before to the clinical evaluation, all patients gave written their informed consent. Oral samples (Saliva and GCF) from the patients were collected between May 2019 and March 2021. The Sleep Clinic of the Hospital Universitario San Ignacio and the Sleep Clinic of the Faculty of Dentistry at the Pontificia Universidad Javeriana-PUJ, Bogotá, D.C., Colombia, referred a convenience sample of 84 eligible patients who fulfilled the inclusion criteria (48 women and 36 men; aged between 30 and 71 years). The current study was conducted in accordance to the Declaration of Helsinki of 1975, revised in 2000 and was given the go-ahead by the Faculty of Dentistry's Research and Ethics Committee (CIEFOUJ No. 005). All patients provided written informed permission following the description of their conditions and before to the clinical evaluation. Patients' oral samples (saliva and GCF) were taken between May 2019 and June 2019. The following were the inclusion criteria: adults who were 30 years old; who had at least six teeth in their mouth; and had recently undergone a polysomnographic exam (no more than six months before). The following standards for exclusion were stablished: smokers; diabetics; patients who had recently taken antibiotics (in the previous three months); patients who had periodontal treatment previous periodontal treatment (in the last three months); patients who had been treated with continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BPAP); patients who hada pharmacological or surgical treatment for OSA; patients with autoimmune disorders and acute respiratory conditions; and patients who were pregnant. The presence and severity of OSA in the patients were determined by a polysomnographic study and the Apnea-Hypopnea Index (AHI: mean of apneas or hypopneas per hour). Thus, 5< AHI <15 was considered mild OSA, 15< AHI <30 was considered moderate OSA, and AHI ≥30 was considered severe OSA [24]. For periodontal diagnosis all patients were examined at the Faculty of Dentistry-PUJ through clinic evaluation and panoramic X-rays and classified according to the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions [25]. In the dental records the patients' demographic and periodontal parameters were recorded, including age, sex, body mass index (BMI), probing depth (PD), clinical attachment loss (CAL), plaque index (PI), bleeding of probing (BOP), and medical records. According to the severity of OSA and periodontal diagnosis, the patients were classified into four groups: Group 1 (H) healthy patients ($$n = 23$$) – G1; Group 2 (P) periodontitis and non-OSA patients ($$n = 17$$) – G2; Group 3 (OSA) OSA and non-periodontitis patients ($$n = 18$$) – G3; and Group 4 (P-OSA) periodontitis and OSA patients ($$n = 26$$) – G4. ## Oral samples collection A 2-mL sample of unstimulated saliva collection was taken and stored at 4 °C. Then samples were centrifuged for 20 min at 4 °C at 10,000 rpm. The supernatant was collected and a protease inhibitor cocktail (Sigma-Aldrich®) was added before being stored at −20 °C until processing. The GCF sample was taken by performing relative isolation of the tooth of interest with gauze and cotton rolls, as well as constant drying with cotton swabs to avoid saliva contamination. The sample was obtained from the deepest pocket in periodontitis patients and from any place in healthy patients by inserting standardized absorbent paper strips (Periopapers®, Oral Flow, Plainview, New York, USA) into the periodontal sulcus for 30 s. Bleeding-contaminated paper strips were discarded. The amount of GCF obtained was measured using Periotron 8010®, an electronic micro-moisture meter instrument (Ora Flow R Inc., New York, USA) (volume measure: periotron units), and the paper strips were immediately placed into 1.5 mL tubes containing 200 μL of sterile phosphate-buffered saline (PBS) solution and a protease inhibitor cocktail (Sigma-Aldrich®). The GCF was vortexed for 10 s before being centrifuged at 2000 rpm for 5 min at 4 °C. The supernatants were then frozen at −20 °C until use [26,27]. ## Analysis of salivary and GCF cytokines The levels of IL-1β, IL-6, IL-17A, IL-33, and TNF-α were measured in 50 μL of saliva and GCF using multiplex bead immunoassays (Luminex® Multiplex Assays - Immunoassays, Austin, Texas, USA) according to the manufacturer's instructions (Milliplex MAP Humano TH17, Millipore, Cat. No. HTH17MAG-14 K) and quantification was done in the MAGPIX equipment (Luminex Corporation, Austin, Texas, USA). The standard calibration curve, Software Luminex IS2.3 and Sotfware xPONENT 3.1 (Austin, Texas, USA) were used to determine the concentrations of each cytokine (pg/mL). ## Statistical analysis Data were analyzed with statistical software GraphPad Prism 9.0.2 (GraphPad Software, California, USA), XLSTAT statistical and data analysis solution (Addinsoft, New York, USA) and Software R 4.01 license GPNU (Free Software Foundation, Boston, USA). A descriptive analysis using means, medians, and interquartile ranges, as well as a two-way ANOVA with Tukey's multiple comparisons test, were used to analyze the demographic variables, periodontal parameters of the patients, and cytokine concentrations. The non-parametric Kruskal-Wallis test and the Dunn test for multiple comparisons were used to evaluate the quantitative values of cytokines and the differences among groups (GraphPad Prism 9.0.2). Spearman r rank correlation test was used to determine the correlation between periodontal parameters and cytokine concentrations in saliva and GCF (XLSTAT). All tests were performed with p-value <0.05 as significance level. ## Demographic data and clinical periodontal parameters The demographic variables and periodontal parameters of the study population are displayed in Table 1. There was a higher frequency of men in G4(P-OSA) than in the other groups. The probing depth - PD (mm) was significantly higher in G4(P-OSA) compared with the other groups ($$p \leq 0.0049$$). Furthermore, sites (%) PD ≥ 4 mm and BOP (%) showed statistically significant differences between G2(P) and G4(P-OSA) ($p \leq 0.001$) vs. G1(H).Table 1Demographic variables and periodontal parameters of the patients. Table 1Clinical variableGroup 1 (H) ($$n = 23$$)Group 2 (P)($$n = 17$$)Group 3 (OSA) ($$n = 18$$)Group 4 (P-OSA) ($$n = 32$$)Age (years)45 ± 1341 ± 1051 ± 1350 ± 11Sex (Females/Males) (n)$\frac{16}{710}$/$\frac{712}{611}$/21Teeth present26 ± 427 ± 224 ± 626 ± 6PD (mm)1.79 ± 0.472.6 ± 0.432.57 ± 4.7713.12 ± 9.71a,b,cSites (%) PD ≥ 4 mm0.27 ± 0.4715.45 ± 9.94a2.01 ± 0.15b13.12 ± 9.71a,cCAL (mm)1.33 ± 0.732.10 ± 0.991.52 ± 0.952.18 ± 1.04BOP (%)12.04 ± 11.8651.31 ± 26.35a25.16 ± 22.78a,b48.07 ± 25.39a,cPI19.93 ± 12.0645.24 ± 25.95a38.63 ± 21.23a39.25 ± 18.59aBMI (kg/m2)25.65 ± 3.3824.86 ± 3.7426.18 ± 4.5428.45 ± 4.09Values are given as mean ± standard deviation. PD: probing depth; CAL: clinical attachment loss; PI: plaque index; BOP: bleeding of probing; BMI: body mass index. The statistical analysis was performed with Two-way ANOVA. Tukey's multiple comparisons test. $p \leq 0.05.$aSignificantly different from Group 1.bSignificantly different from Group 2.cSignificantly different from Group 3. It was found that $31\%$ of individuals with mild OSA had periodontitis, $77\%$ of individuals with moderate OSA had periodontitis, and $75\%$ with severe OSA had periodontitis. The stage III periodontitis was statistically significant with severe OSA ($$p \leq 0.0142$$) (Fig. 1).Fig. 1Percentage of the periodontal condition of the patients according to apnea diagnosis; Two-way ANOVA. * p-value = 0.0142.Fig. 1 ## Cytokines concentration in saliva and GCF Salivary IL-1β was the highest of all in G4 (P-OSA) with a slight significance ($$p \leq 0.057$$), and there were no significant differences in GCF IL-1β. Salivary IL-6 was significantly highest in G2 (P) ($$p \leq 0.0044$$), with differences between G1 (H) ($$p \leq 0.026$$) and G3(OSA) ($$p \leq 0.0168$$). There was no significant difference with G4 (P-OSA). In addition, salivary TNF-α was highest in G2 (P) ($p \leq 0.0001$), with differences with G3 (OSA) and G4 (P-OSA) ($$p \leq 0.0004$$ and $$p \leq 0.0009$$, respectively). In GFC, G3 (OSA) and G4 (P-OSA) had the greatest IL-6 levels; GCF IL-17A was highest in G3 (OSA) patients while GCF IL-33 was elevated in G4 (P-OSA) patients, but with no statistically significant differences. In GCF, patients of G2 (P) and G4 (P-OSA) exhibited similar levels of salivary IL-1β; IL-1β level was higher in G2 (P), followed by G3 (OSA). Similarly, levels of salivary IL-6 were higher in G2(P) and G4(P-OSA), whereas levels of GCF IL-6 were higher in G3 (OSA) and G4 (P-OSA) (Table 2).Table 2Descriptive statistics of cytokines concentration in saliva and GCF.Table 2Group 1 (H)($$n = 23$$)Group 2 (P)($$n = 17$$)Group 3 (OSA)($$n = 18$$)Group 4 (P-OSA)($$n = 26$$)p-valueCytokines (pg/mL)SampleMeanMedianRangeMeanMedianRangeMeanMedianRangeMeanMedianRangeIL-1βSaliva24.289.930–238.736.8718.460–159.224.8212.250.39–159.252.9719.450–816.60.057GCF52.7130.407.1–290.972.1332.10–555.836.2232.604.9–128.483.7922.104.8–568.30.84IL-6Saliva1.170.000–7.983.852.78a,b0–15.823.810.000–29.721.601.350–7.210.0044GCF20.217.26.49–55.9315.4411.726.13–33.0726.924.496.53–73.8326.5424.456.59–53.190.156IL-17ASaliva–––––––––––––GCF35.2227.9914.82–110.728.4422.4111.77–69.5342.3932.3111.86–149.336.8927.0613.72–92.030.1128IL-33Saliva–––––––––––––GCF29.8526.189.47–81.9621.7715.539.95–47.9139.9336.410.06–11437.5637.7110.16–73.070.071TNF-aSaliva1.421.230.59–2.651.371.47b,c0.57–2.121.190.540–6.570.840.260–5.45<0.0001GCF19.0914.360–74.3311.469.030–45.4021.5212.600–98.1116.565.980–64.110.3957The statistical analysis was performed with the Kruskal-Wallis test and post hoc test: Multiple comparisons using Dunn's multiple comparisons test. The median concentrations per cytokine were compared between groups.† Significantly different from Group 2.aSignificantly different from Group 1.bSignificantly different from Group 3.cSignificantly different from Group 4. No concentrations of IL-17A were detected in saliva; however, GCF IL-17A levels were higher in G3 (OSA) and G4 (P-OSA). Salivary IL-33 levels were lower in all groups of patients, but G4 (P-OSA), followed by G3 (OSA) exhibited higher levels in GCF. Similarly, salivary TNF-α levels were lower in all groups of patients; however, G1 (H) and G3 (OSA) exhibited higher TNF-α levels, whereas G2 (P) and G4 (P-OSA) exhibited lower levels. There was a statistically significant difference in IL-6 and IL-33 concentrations between GCF and salivary cytokines being more evident in GCF (Fig. 2).Fig. 2Salivary and GCF concentrations of cytokines in the study groups and samples. Box and whiskers plots represent the median and minimum–maximum values, horizontal bars indicate the significant differences between groups. G1: Group 1 (H); G2: Group 2 (P); G3: Group 3 (OSA); G4: Group 4 (P-OSA). Saliva: S; Gingival Crevicular Fluid: GCF. Kruskal-Wallis test with Dunn's multiple comparisons test: * p-value <0.05; p-value <0.01; * p-value <0.001; **** p-value <0.0001.Fig. 2 ## The tendency of the cytokine concentrations in each group of patients The percentile plots indicate the tendency of the cytokine concentrations (pg/mL) in each group of patients evaluated. Higher salivary concentrations of IL-1β, IL-6, and TNF-α (62, 5, and 1.4 pg/mL, respectively) were detected in $80\%$ of patients of G2 (P). Regarding GCF, $80\%$ of patients of G4 (P-OSA) showed the highest concentration of IL-1β (>100 pg/mL). Also, highest concentrations of IL-6 (27 pg/mL), IL-17A (50 pg/mL) and IL-33 (40 pg/mL) were detected in $80\%$ of patients of G3 (OSA), followed by G4 (P-OSA): IL-6 (27 pg/mL), IL-17A (49 pg/mL) and IL-33 (38 pg/mL). A higher concentration of TNF-α was detected in $80\%$ of patients of G3 (OSA) and G4 (P-OSA) (34 pg/mL) (Fig. 3).Fig. 3Percentile graph of the cytokine concentrations in saliva and GCF in each group of patients evaluated. Each point represents the cytokine concentration in all patients of each group. The x-axis represents the percentiles and the y-axis the cytokine concentrations in pg/mL. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)Fig. 3 ## Correlations of periodontal parameters and cytokine concentrations in saliva and GCF There were significant positive correlations of periodontal parameters and cytokine concentrations in saliva between PI and IL-6 in G3 (OSA) (rs = 0.675), PD and TNF-α in G1 (H) (rs = 0.572), and a significant negative correlation between BOP (%) and TNF-α in G3 (OSA) (rs = -0.543). Significant negative correlations between periodontal parameters and cytokine concentrations in GCF were found between PI and IL-6 (rs = −0.549) and PI and IL-33 in G1 (H) (rs = −0.556) and between all periodontal parameters and IL-6, IL-17A, IL-33 and TNF-α in G2 (P). Positive correlations between IL-1β and all the periodontal parameters were found only in G2 (P), whereas positive correlations between IL-6, IL -17A, IL-33, TNF-α and periodontal parameters PD and BOP (%) were found in G3 (OSA) and G4 (P-OSA) in GCF and saliva (Table 3). Furthermore, Fig. S1 shows graphically the linear positive association ($p \leq 0.05$), how cytokines change in saliva and GCF in each group of patients, and how they correlate with each other (Fig. S1).Table 3Correlations (rs) of periodontal parameters and cytokines concentrations. Table 3 ## Cytokines levels in saliva and GCF, periodontal disease stage and severity of OSA Periodontal disease stages and severity of OSA were related with the cytokines levels in G2 (P), G3 (OSA) and G4 (P-OSA) (Table 4). There was a significant positive correlation between stage IV periodontitis and salivary IL-33 (rs = 0.531) in G4 (P-OSA), and there was a significant negative correlation between stage II periodontitis and GCF IL-1β (rs = −0.510, $p \leq 0.05$) in G2 (P). Moreover, there were only significant positive correlations between mild OSA and IL-1β (rs = 0.516), IL-6 (rs = 0.487), IL-33 (rs = 0.487), and TNF-α (rs = 0.539) in GCF of G3 (OSA).Table 4Correlations (rs) between cytokines levels in saliva and GCF vs. periodontal disease status and grade of OSA.Table 4 ## Concentrations of cytokines and presence of Candida spp. in oral microbiota Based on the microbiological profiles of six G4 (P-OSA) patients, three of them positive for Candida spp. and three of them negative [21], the cytokine concentrations profile showed that the patients with Candida spp. had higher cytokine levels relative to those patients who did not have the yeast. In particular, all cytokines concentrations in GCF were the highest (>40 pg/mL), and only IL-1β in saliva was higher in one patient (>60 pg/mL). Regarding to the patients without Candida spp., the cytokine concentration profiles were similar in the three patients, with higher levels of cytokines in GCF (20–50 pg/mL) (Fig. 4).Fig. 4Cytokine concentrations in saliva and GCF in three patients without Candida spp. and three patients with Candida spp. These patients were classified in the G4 (P-OSA).Fig. 4 ## Discussion To elucidate the association between OSA and periodontitis, this study compared the salivary and gingival crevicular fluid (GCF) concentrations of five pro-inflammatory cytokines (IL-1β, IL-6, IL-17A, IL-33, and TNF-α) in patients with and without OSA and their association with periodontal condition. The present study demonstrated a high prevalence of stage III periodontitis in patients with severe OSA ($69\%$, $$p \leq 0.0142$$). This result is comparable to what Gamsiz-Isik et al. reported [2017] [5], who demonstrated a higher prevalence of severe periodontitis in patients with OSA ($48.2\%$, $p \leq 0.001$), with moderate-to-severe OSA being more prevalent ($52.2\%$), similarly to results reported by Seo et al. [ 2013] [12] who determined that $60\%$ of patients with periodontitis had OSA, and Stazić et al. [ 2022] [28] who concluded that patients with moderate-to-severe OSA had more severe stages of periodontitis (stages III and IV) ($$p \leq 0.043$$). Moreover, in the present study, PD (>4 mm) and BOP (%) were significantly increased in patients with OSA (G3) compared to healthy patients (G1), as reported by Seo et al. [ 2013] [12] who determined that PD (≥4 mm; $p \leq 0.005$) and CAL (≥6 mm; $p \leq 0.008$) were significantly higher in patients with OSA. This evidence suggests that the OSA condition and its physiopathology might be a risk factor for the progression of periodontitis. Furthermore, different studies have evaluated the immunological relationship between periodontitis and OSA, determining the levels of pro-inflammatory cytokines in saliva, GCF and serum [5,18,20]. As well as the present study, Nizam et al. [ 2014] reported no statistically significant difference in salivary IL-1β concentrations between a group of healthy patients and a group of patients with OSA [20]. However, our study found high concentrations of IL-1β in saliva ($$p \leq 0.057$$) from G2 (P) and G4 (P-OSA) compared to G1 (H) and found that $80\%$ of patients of both groups had the highest concentration of IL-1β (40–60 pg/mL), a determinant cytokine in periodontitis. Although there were no significant differences between the concentrations of IL-1β from G2 (P) and G4 (P-OSA), there was a high level of this cytokine in patients with periodontitis and OSA (G4, P-OSA), suggesting a relationship between OSA and periodontitis. Similarly, the salivary IL-6 was highest in G2 (P) and G4 (P-OSA), as reported by Nizam et al. [ 2014, 2016] in patients with OSA, and its concentration was statistically significant in G2 (P) [18,20]. On the other hand, a positive correlation was observed between salivary IL-6 and PI (rs = 0.675; $p \leq 0.05$) in G3 (OSA), and BOP (%) and PI in G4 (P-OSA), which may be explained by the existence of a biofilm in these individuals that increases IL-6 levels as well as the risk of inducing a local inflammatory response; thus, IL-6 could serve as an early salivary biomarker of periodontitis associated to OSA. Otherwise, the IL-6 in GCF was higher in patients of the G3 (OSA) and G4 (P-OSA) and positively correlated with PD in G3 (OSA) and with PD and BOP (%) in G4 (P-OSA), in comparison with G2 (P) which had lower levels and negative correlations with all periodontal parameters. Our finding of a high concentration of IL-6 in GCF from patients with OSA and periodontitis has not been reported before in literature. Regarding IL-1β in GCF, a possible explanation for the local inflammation is that dryness of mouth caused by OSA, leads to a bacterial colonization as reported by Gamsiz-Isik et al. [ 2017] [5]. In addition, the present study reports for the first time that concentrations of IL-17A in GCF were high in patients with OSA (G3) and (P-OSA) (G4), with a positive correlation in PD (mm) and BOP (%) in both groups. The IL-17A is secreted by resident memory T helper 17 cells (TH17 cells), which have a homeostatic oral accumulation in presence of IL-6. However, the periodontitis-associated TH17 cells proliferation requires of IL-6 and IL-23 [29] to increase the production of IL-17A. This cytokine has potent osteoclastogenic and inflammatory bone destruction properties due to its capacity to stimulate the expression of RANKL in osteoblasts because it mediates the destruction of connective tissue by inducing the expression of matrix metalloproteinases −3, −9 and −13 (MMP-3, 9, 13) in fibroblasts [30]. Furthermore, there are studies that have shown a significant increase of IL-23 levels in serum of patients with OSA, and its positive correlation with AHI and C-reactive protein (CRP) [31,32], suggesting that high levels of IL-23 in patients with OSA can stimulate the production of pro-inflammatory cytokines by TH17 cells, like IL-17A, which is involved in the development of periodontitis [29]. The present study found lower concentrations of IL-33 in saliva in all groups of patients, while Nizam et al. [ 2014] found that the concentrations of IL-33 in saliva were higher in patients with OSA, regardless of the severity of the OSA [20]. However, the levels of IL-33 in GCF in the present study were higher in patients from G3 (OSA) and G4 (P-OSA) with positive correlations with periodontal parameters in PD in G3 (OSA) and PD and BOP (%) in G4 (P-OSA). In periodontitis, the expression of IL-33 in gingival epithelial and connective tissue cells acts as an alarmin of tissue damage for the immune system, inducing RANKL expression and triggering the recruitment of RANKL-expressing B and T cells [33,34]. Sozer et al. [ 2018] reported serum IL-33 concentrations significantly higher in patients with OSA, and hypothesized that OSA influences the levels of IL-33 and is involved in the systemic inflammation produced in OSA [35]. Nizam et al. [ 2014] have also shown high levels of IL-33 in saliva of patients with OSA [20]. These findings may help explain the bidirectional relationship between periodontitis and OSA, which results from their comorbidity, where the presence of one may cause the other to become more inflammatory. Both conditions increase the presence of IL-17A and IL-33 that share the activation of osteoclastogenesis mediated by RANKL/OPG axis that cause periodontal bone resorption [36,37]. Interestingly, TNF-α plays both anti-inflammatory and pro-inflammatory roles in inflammation: initiating a strong inflammatory response and regulating the intensity and duration of inflammatory processes, respectively. This cytokine plays a variety of roles in the development of periodontitis, including attracting cells to sites of tissue damage and inflammation, and promoting the breakdown of extracellular matrix and bone resorption by increasing the production of IL-1β, IL-6, collagenases, MMPs, and RANKL in gingival epithelial cells [33]. TNF-α levels in saliva and serum were not significantly different between patients with periodontitis and healthy patients; in fact, TNF-α levels were lower in periodontitis patients. Regarding the levels of TNF-α in OSA, earlier investigations found that both OSA patients and healthy individuals had the same amounts of TNF-α in GCF [5], saliva [18], and plasma [32]. In contrast, other research found a strong relationship between TNF-α and indicators of the severity of OSA and oxygen desaturation [38] among recently diagnosed OSA patients [39]. According to the current findings, with the exception of individuals who only had periodontitis (G2), the patients with OSA and periodontitis and OSA had similarly low levels of TNF-α in saliva. In contrast, the levels of TNF-α in GCF were higher in healthy patients in comparison to patients with OSA and with both conditions. Given the dual functions of TNF-α as an inflammatory mediator, these findings show that there is still a discrepancy in the determination of the levels of TNF-α in patients with periodontitis associated with OSA. This discrepancy may be explained by variations in the demographic and clinical characteristics of the individuals. Therefore, further investigations are required to establish a link between TNF-α levels in saliva, GCF, serum and periodontal condition in OSA assessed simultaneously in the same patients. According to the correlations of periodontal parameters and cytokine concentrations in saliva and GCF, there was a positive correlation between PD and salivary TNF-α in G1 (H) (rs = 0.572; $p \leq 0.05$). TNF-α may be acting as an anti-inflammatory cytokine in the homeostatic process in healthy individuals. The anti-inflammatory function of TNF-α in patients without periodontitis may also explain the negative correlation between BOP (%) and TNF-α in G3 (OSA) (rs = −0.543; $p \leq 0.05$). Furthermore, there was a negative correlation between PI and GCF IL-6 (rs = −0.549; $p \leq 0.05$) and between PI and GCF IL-33 in G1 (H) (rs = −0.556; $p \leq 0.05$). These findings might support the homeostatic function of IL-6, although it is unclear whether IL-33 works on the same principle in healthy individuals. Nevertheless, it is important to emphasize the positive correlation between levels of IL-33 and the other cytokines analyzed in patients with OSA and individuals who also have periodontitis, and in the same way, the positive correlation between salivary IL-33 with stage IV periodontitis and moderate OSA in G4 (P-OSA). Further research is necessary to support the role of IL-33 in the physiopathology of OSA in patients with periodontitis. Despite some authors still concluding that there is little evidence of a possible relationship between periodontitis and OSA, both cause-and-effect and their pathophysiological mechanisms [40], other studies have proposed different mechanisms involving genetic, microbiological, and immunological factors as possible cause-and-effect relationships between OSA and periodontitis [6,18,21,22,41,42]. Both conditions, periodontitis and OSA, may be related by systemic inflammation. Patients with OSA exhibit an increased proliferative potential of natural killer (NK) and CD4 T cells and a decreased capacity of neutrophils to phagocytose bacteria and produce ROS [43,44]. Even the levels of the other enzymes released by neutrophils during inflammation, such as matrix metalloproteinases (MMPs), are significantly lower in patients with severe OSA [44]. Hypoxia and hypercapnia present in patients with OSA have been shown to be related to the apoptosis of periodontal cells [45], that may increase the production of pro-inflammatory cytokines and stimulate transcription factors such as the nuclear transcription factor (NF-κβ) [46]; this factor is involved in the periodontal bone loss [47,48] mediated by the increase of RANKL/OPG (Receptor activator of NF-κβligand/Osteoprotegerin) axis ratio that activates the osteoclastogenic process [37]. Additionally, the presence of Candida spp. in the oral microbiota of patients with periodontitis and OSA [21] might well be responsible for the high levels of IL-17A and IL-33 in GCF, worsening periodontal disease. Previous studies have reported the role of IL-17A and IL-33 in response to systemic Candida albicans infections, promoting antifungal immunity [49,50]; however, other authors have described the pro-inflammatory function of these cytokines, demonstrating how IL-33 stimulates the production of other cytokines like IL-1 and IL-6 [51], and how the presence of a dysbiotic microbiota causes an increase in IL-17A, which in turn triggers the osteoclastogenic process [52]. In spite of using sensitive multiplex bead immunoassays, the present study faced a limitation in determining values smaller than 5 pg/mL of IL-17A and IL-33 in saliva. Also, comparisons between the two sexes are not possible using the data from this study; hence it is advised to do additional research in the future with a larger sample size and age- and sex-stratified analyses. Analyses based on oxygen saturation and microarousals based on the severity of OSA should also be included. However, the findings of the present study suggest that periodontitis and OSA have a bidirectional-relationship: OSA may increase the risk of developing periodontitis, and periodontitis worsens the inflammatory process in OSA. The pro-inflammatory cytokines IL-6, IL-33 and IL-17A can be proposed as biomarkers of both conditions in GCF due to their ability to trigger the activation of osteoclastogenesis in periodontitis (Fig. S2). ## Conclusions The present study suggests a bidirectional and comorbid link between OSA and periodontitis. There was a higher prevalence of stage III periodontitis and severe OSA, as well as higher levels of periodontal parameters (PI and BOP), and expression of IL-1β and IL-6 in saliva and IL-6, IL-17A, and IL-33 in GCF, in patients with periodontitis and OSA. These cytokines may be considered biomarkers of both conditions, emphasizing the association between IL-33 and stage IV periodontitis. Our results suggest that there is a potential diagnosis of association between individuals with OSA and periodontitis; individuals with OSA should get a periodontal screening; and individuals with periodontitis should get a sleep study to determine OSA. Further studies are necessary to analyze the role of pro-inflammatory cytokines in the physiopathology of OSA in patients with periodontitis. ## Author contribution statement Mayra A. Téllez Corral: Conceived and designed the experiments; Performed the experiments, Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Eddy Herrera Daza; Juliana Velosa Porras; María E. Cortés; Liliana Otero; Claudia M. Parra Giraldo; Nelly S. Roa Molina: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. Natalia Arango Jimenez; Darena Z. Morales Vera; Catalina Latorre-Uriza; Francina M. Escobar Arregoces; Patricia Hidalgo Martinez: Contributed reagents,Materials, analysis tools or data. Nelly S. Roa Molina: Conceived and designed the experiments. ## Funding statement Dr. Mayra Alexandra Téllez Corral was supported by Banco de la República [4.509]. ## Data availability statement Data included in article/supp. material/referenced in article. ## Declaration of interest's statement The authors declare no competing interests. ## Supplementary data The following is the Supplementary data to this article:Multimedia component 1Multimedia component 1 ## References 1. Cortelli S.C., Cortelli J.R., Romeiro R.L.. Frequency of periodontal pathogens in equivalent peri- implant and periodontal clinical statuses. Arch. Oral Biol. (2013) 58 67-74. DOI: 10.1016/j.archoralbio.2012.09.004 2. Bui F.Q., Almeida-da-Silva C.L.C., Huynh B.. Association between periodontal pathogens and systemic disease. Biomed. J. (2019) 42 27-35. DOI: 10.1016/j.bj.2018.12.001 3. Escobar F., Latorre C., Velosa J.. 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--- title: 'Impact of an eHealth Smartphone App on Quality of Life and Clinical Outcome of Patients With Hand and Foot Eczema: Prospective Randomized Controlled Intervention Study' journal: JMIR mHealth and uHealth year: 2023 pmcid: PMC10031439 doi: 10.2196/38506 license: CC BY 4.0 --- # Impact of an eHealth Smartphone App on Quality of Life and Clinical Outcome of Patients With Hand and Foot Eczema: Prospective Randomized Controlled Intervention Study ## Abstract ### Background Chronic hand and foot eczema is a polyetiological dermatological condition. Patients experience pain, itching, and sleep disturbances and have a reduced quality of life. Skin care programs and patient education can improve the clinical outcome. eHealth devices offer a new opportunity to better inform and monitor patients. ### Objective This study aimed to systematically analyze the effect of a monitoring smartphone app combined with patient education on the quality of life and clinical outcome of patients with hand and foot eczema. ### Methods Patients in the intervention group received an educational program; attended study visits on weeks 0, 12, and 24; and had access to the study app. Patients in the control group attended the study visits only. The primary end point was a statistically significant reduction in Dermatology Life Quality Index, pruritus, and pain at weeks 12 and 24. The secondary end point was a statistically significant reduction in the modified Hand Eczema Severity Index (HECSI) score at weeks 12 and 24. This is an interim analysis at week 24 of the 60-week randomized controlled study. ### Results In total, 87 patients were included in the study and randomized to the intervention group ($$n = 43$$, $49\%$) or control group ($$n = 44$$, $51\%$). Of the 87 patients, 59 ($68\%$) completed the study visit at week 24. There were no significant differences between the intervention and control groups regarding quality of life, pain, itch, activity, and clinical outcome at weeks 12 and 24. Subgroup analysis revealed that, compared with the control group, the intervention group with an app use frequency of fewer than once every 5 weeks had a significant improvement in the Dermatology Life Quality Index at weeks 12 ($$P \leq .001$$) and 24 ($$P \leq .05$$), in pain measured on a numeric rating scale at weeks 12 ($$P \leq .02$$) and 24 ($$P \leq .02$$), and in the HECSI score at week 12 ($$P \leq .02$$). In addition, the HECSI scores assessed on the basis of pictures taken by the patients of their hands and feet correlated strongly with the HECSI scores recorded by physicians during regular personal visits ($r = 0.898$; $$P \leq .002$$) even when the quality of the images was not that good. ### Conclusions An educational program combined with a monitoring app that connects patients with their treating dermatologists can improve quality of life if the app is not used too frequently. In addition, telemedical care can at least partially replace personal care in patients with hand and foot eczema because the analysis of the pictures taken by the patients correlates strongly with that of the in vivo images. A monitoring app such as the one presented in this study has the potential to improve patient care and should be implemented in daily practice. ### Trial Registration Deutsches Register Klinischer Studien DRKS00020963; https://drks.de/search/de/trial/DRKS00020963 ## Background The prevalence of combined chronic hand and foot eczema in industrialized cities is $5.4\%$ [1]. Women are more frequently affected than men, with an incidence of 9.6 per 1000 compared with 4.0 per 1000 [2]. Hand and foot eczema is considered to be chronic if it persists for >3 months despite adequate therapy or recurs with a frequency of more than twice a year [3]. It does not represent a homogeneous disease entity. The clinical picture, morphology, localization, and etiology can be very different. In general, 4 different etiologies of hand and foot eczema exist: allergic contact, acute-toxic, cumulative-toxic, and atopic hand and foot eczema [4]. Allergic contact hand and foot eczema is typically a type IV sensitization to diverse allergens such as nickel, cobalt, chromates, and fragrancies [5]. Cumulative-toxic hand and foot eczema occurs after repeated exposure to substances that only mildly irritate the skin. Over time, the regenerative capacity of the skin is exceeded, and the eczematous reaction becomes visible. Atopic hand and foot eczema develops on the basis of a genetic predisposition called atopic diathesis. It is therefore a localized variant of atopic eczema with a corresponding etiology [3,6]. The severity of eczema ranges from very mild to very severe, with therapy-refractory courses associated with intense pain and itching [7]. In addition, patients with eczema often have to face social stigmatization and struggle with feelings of shame [8]. These physical and psychological circumstances often lead to a radical reduction in quality of life and may even result in depression [9]. More often than not, patients with eczema have limited knowledge of the pathogenesis of their skin condition and the correct disease management [10]. In many other diseases such as type 2 diabetes mellitus, patient education has proven to be an effective method to increase knowledge of the disease, thereby improving the clinical outcome. Coppola et al [11] have shown that patient education is usually associated with an improvement in clinical knowledge, lifestyle, and psychosocial outcomes in comparison with usual care. In Germany, there are skin protection seminars run by employers’ liability insurance associations, but these are reserved for people whose eczema is caused or exacerbated by their professional activity. In our department of dermatology, patient education alone for patients with psoriasis had no significant effect on the clinical outcome [12]. We therefore assume that one-time education of patients with chronic inflammatory skin conditions may not suffice to ameliorate the disease in the long term. eHealth-based supporting systems for patients are becoming popular and are incorporated more frequently into patient care. Germany recently set up the German acronym for Digital Health Applications (DiGA) directory, which lists Conformité Européenne–marked medical devices that aim to detect, monitor, treat, or alleviate diseases or to detect, treat, alleviate, or compensate for injuries or disabilities [13]. Physicians (MDs) in Germany can prescribe eHealth devices listed in the DiGA directory. There are currently no DiGA directory–listed eHealth devices for patients experiencing hand and foot eczema in Germany, and scientific data on the beneficial effect of eHealth applications for these patients are missing. ## Objectives The aim of this prospective randomized controlled intervention study was to analyze whether a monitoring smartphone app combined with patient education would improve the quality of life and clinical outcome of patients with hand and foot eczema. The study app was developed specifically for this study. With the app, our patients were able to periodically measure Dermatology Life Quality Index (DLQI) and Hand Eczema Severity Index (HECSI; modified version for foot eczema) scores, as well as the impact on activity and pain (both measured on a numeric rating scale [NRS]), and document the progression of their disease through photographs [14-16]. In addition, the app allowed patients to directly contact their own treating physicians through a chat function. Furthermore, the DLQI, HECSI, and NRS (for activity and pain) scores were assessed by the treating physicians during personal visits at weeks 0 (before the intervention), 12, and 24. The final aim behind the development of the app was to reduce waiting time for a physician’s appointment in case of an emergency by expanding teledermatological services for patients with hand and foot eczema and to allow precise self-monitoring by the patients. ## Study Design The aim of this 60-week randomized controlled intervention study was to investigate the effect of patient education in combination with a monitoring smartphone app on patients experiencing chronic hand and foot eczema. This is an interim analysis of the data from study weeks 0, 12, and 24. The study was carried out at the department of dermatology, venereology, and allergology at the University Medical Center Mannheim in Mannheim, Germany, from August 13, 2018, to August 30, 2021. The inclusion criteria included a physician-confirmed diagnosis of chronic hand and foot eczema, ability to give informed consent, access to a smartphone, and patient age between 18 and 75 years. During the first study visit (week 0 [V1]), the study participants were randomly assigned to the control or intervention group in a ratio of 1:1. To assign patients to a group, we created 50 lots for the intervention group and 50 lots for the control group. These were sealed in an urn, and the patients were asked to draw lots. In total, 90 participants were included in the study, but 3 ($3\%$) dropped out of the study before they were assigned to a group. Of the 87 remaining participants, 43 ($49\%$) were assigned to the intervention group and 44 ($51\%$) to the control group. The control group started the first study visit at week 0. Information on sociodemographic data, preexisting conditions, and previous and current therapies were collected, and standardized questionnaires such as the DLQI administered. In addition, patients’ current level of knowledge about their disease, severity of the disease measured using the HECSI or a modified form of the HECSI for foot eczema, and the intensity of the pain and itch measured using an NRS ranging from 0 to 10 were recorded. Furthermore, the negative impact on the activity measured using the NRS of patients was assessed. In-person follow-up visits were carried out at V2 and V3. The same parameters were recorded for the intervention group. In addition, these patients received a 2-hour detailed training session on pathogenesis, classification, therapeutic options, and behavioral recommendations from 2 dermatological specialists at our clinic. Each patient also received a personal access code to our app, DermaScope Mobile. Using this app, patients were able to take pictures of their hands and feet, use a chat function to ask questions that were answered by their treating dermatologists, and complete questionnaires on quality of life (DLQI) and current symptoms (NRS for itch and pain). Screenshots of the app can be found in the paper by Domogalla et al [17]. The highest possible app use frequency was once a week. The quality of each image uploaded in the app by the patients was categorized by the rater (YS) as good or bad based on the following criteria: well-illuminated picture, sharp and focused image, and complete presentation of the hands and feet. All 3 criteria had to be met for the image to be rated as good. Each image was assigned to the rater (YS), who checked its quality based on these 3 criteria. If all criteria were met, the image was rated as of good quality. We then calculated an electronic HECSI (eHECSI) score based on these images and statistically examined the extent to which this score correlated with the HECSI score collected in person. The primary end point of the study was to determine the effect of extensive patient training, physician-patient contact on demand, and our app on quality of life as well as itching and pain at weeks 12 and 24. The secondary end points were the effect on the disease outcome assessed with the HECSI at weeks 12 and 24. Modulating effects of sex, age, and disease duration were evaluated for each end point. ## Ethics Approval The medical ethics committee of the Medical Faculty Mannheim, Heidelberg University, approved the study (2017-655N-MA), and the implementation complied with the Declaration of Helsinki. All participants were instructed in detail regarding the study design and gave their informed consent before participating in the study. ## Statistical Analysis Linear panel data regression analyses estimated the trajectories in the outcomes. Random effect regressions determined the main and interaction effects of group membership (intervention vs control group) and visit time point (V1, V2, and V3) on DLQI, pain, daily activity, and HECSI scores. Two models of adjustment were calculated. The first model was unadjusted, whereas the second model was adjusted for sex, age, and disease duration. In additional analyses, the effects of app use frequency over 24 weeks were included (group membership: control vs <$20\%$ app use frequency vs ≥$20\%$ app use frequency). Therefore, the intervention group was divided into 2 groups: one comprised patients with app use frequency <$20\%$, and the other was made up of patients with app use frequency ≥$20\%$ during the observation period of 24 weeks. The chosen cutoff of $20\%$ equals app use frequency of once every 5 weeks. Variables were tested for normal distribution, and where relevant, they were transformed to approach normal distribution (power transform square root of DLQI and log10 of HECSI). All statistical analyses were performed using STATA Special Edition (version 14.0; StataCorp LLC). To determine the extent to which the eHECSI score correlated with the HECSI score assessed at the face-to-face visit, we calculated Spearman correlation coefficients. We also examined within the intervention group the socioeconomic factors that influenced the course of HECSI and DLQI. Table 1 shows mean values of the scales, Figure 1 shows the flowchart of the study, and Figures 2-4 show predictive margins (delta method). ## Patient Demographics In total, 90 patients were included in the study. The main reasons for declining participation were lack of time, amelioration of hand and foot eczema, or distance to our outpatient clinic. Of the 90 patients who signed the informed consent form, 87 ($97\%$) took part in the baseline visit and were randomized 1:1 to the intervention ($$n = 43$$, $49\%$) or control ($$n = 44$$, $51\%$) groups. Of the 90 patients initially included in the study, 3 ($3\%$) dropped out of the study before the baseline visit. Of the 87 remaining patients, 23 ($26\%$) discontinued the study after the baseline visit or the educational program (intervention group: $\frac{17}{43}$, $40\%$, and control group: $\frac{6}{44}$, $14\%$). Leading up to week 24, of the 64 remaining patients, 5 ($8\%$) discontinued the study, resulting in 59 ($92\%$) patients completing the week 24 visit (Figure 1). ## Effects of the Intervention on Quality of Life, Pain, Activity, and Clinical Outcome Patients in both the intervention and control groups showed an improvement in quality of life (DLQI) at weeks 12 (V2) and 24 (V3; week 12 [V2]: r=–0.56; $$P \leq .006$$; week 24 [V3]: r=–0.86; $P \leq .001$; Figure 2; Table 2) compared with the baseline visits. No significant differences were observed between the control and intervention groups (r=–0.23; $$P \leq .42$$) and their progress (week 12 [V2]: $r = 0.45$; $$P \leq .09$$; week 24 [V3]: $r = 0.42$; $$P \leq .11$$; Table 2), although the intervention group showed a greater improvement than the control group. Regarding pain, patients in both groups showed no significant amelioration over time compared with the baseline visits (V2: $r = 0.48$; $$P \leq .48$$; V3: r=–0.74; $$P \leq .28$$; Figure 2; Table 2). There were no significant differences between the intervention and control groups ($r = 0.46$; $$P \leq .53$$) and their trajectories (V2: $r = 0.11$; $$P \leq .90$$; V3: $r = 0.96$; $$P \leq .27$$; Table 2). A significant improvement was observed in the activity score from V1 until V3 (V2: r=–1.39; $$P \leq .04$$; V3: r=–2.35; $$P \leq .001$$; Figure 2; Table 2). There was no difference between the 2 groups ($r = 0.08$; $$P \leq .92$$) and their progress (V2: $r = 1.09$; $$P \leq .21$$; V3: $r = 0.99$; $$P \leq .26$$; Table 2). There was also a significant improvement in the severity of eczema as assessed by the HECSI in both groups compared with the baseline visits (V2: r=–0.51; $$P \leq .02$$; V3: r=–0.72; $$P \leq .002$$; Figure 2; Table 2). There was no difference between the groups (r=–0.16; $$P \leq .56$$) or their trajectories (V2: $r = 0.33$; $$P \leq .26$$; V3: $r = 0.43$; $$P \leq .14$$; Table 2). All results were independent of sex, age, or disease duration (model 1; Table 2). Table 1 shows mean values of the scales, whereas Figure 2 shows predictive margins (delta method). Table 2 \| Assessment \| Assessment.1 \| Assessment.2 \| Model 0 \| Model 0.1 \| Model 0.2 \| Model 0.3 \| Model 0.4 \| Model 1 \| Model 1.1 \| Model 1.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| r (SE) \| r (SE) \| P value \| P value \| r (SE) \| r (SE) \| r (SE) \| P value \| \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| \| \| Week 0 \| Refb \| Refb \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –0.561 (0.205) \| –0.561 (0.205) \| .006 \| .006 \| –0.561 (0.205) \| –0.561 (0.205) \| –0.561 (0.205) \| .006 \| .006 \| \| \| Week 24 \| –0.855 (0.205) \| –0.855 (0.205) \| <.001 \| <.001 \| –0.855 (0.205) \| –0.855 (0.205) \| –0.855 (0.205) \| <.001 \| <.001 \| \| \| Intervention group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Control group \| –0.234 (0.288) \| –0.234 (0.288) \| .42 \| .42 \| –0.128 (0.288) \| –0.128 (0.288) \| –0.128 (0.288) \| .66 \| .66 \| \| \| Week 0 × control \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × control \| 0.450 (0.262) \| 0.450 (0.262) \| .09 \| .09 \| 0.450 (0.262) \| 0.450 (0.262) \| 0.450 (0.262) \| .09 \| .09 \| \| \| Week 24 × control \| 0.420 (0.262) \| 0.420 (0.262) \| .11 \| .11 \| 0.420 (0.262) \| 0.420 (0.262) \| 0.420 (0.262) \| .11 \| .11 \| \| \| R2: within \| 0.184 (N/Ac) \| 0.184 (N/Ac) \| \| \| 0.184 (N/A) \| 0.184 (N/A) \| 0.184 (N/A) \| \| \| \| \| R2: between \| 0.01 (N/A) \| 0.01 (N/A) \| \| \| 0.097 (N/A) \| 0.097 (N/A) \| 0.097 (N/A) \| \| \| \| \| R2: overall \| 0.059 (N/A) \| 0.059 (N/A) \| \| \| 0.125 (N/A) \| 0.125 (N/A) \| 0.125 (N/A) \| \| \| \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| 0.478 (0.676) \| 0.478 (0.676) \| .48 \| .48 \| 0.478 (0.676) \| 0.478 (0.676) \| 0.478 (0.676) \| .48 \| .48 \| \| \| Week 24 \| –0.739 (0.676) \| –0.739 (0.676) \| .28 \| .28 \| –0.739 (0.676) \| –0.739 (0.676) \| –0.739 (0.676) \| .28 \| .28 \| \| \| Intervention group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Control group \| 0.459 (0.723) \| 0.459 (0.723) \| .53 \| .53 \| 0.468 (0.737) \| 0.468 (0.737) \| 0.468 (0.737) \| .53 \| .53 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × control group \| 0.105 (0.866) \| 0.105 (0.866) \| .90 \| .90 \| 0.105 (0.866) \| 0.105 (0.866) \| 0.105 (0.866) \| .90 \| .90 \| \| \| Week 24 × control group \| 0.961 (0.866) \| 0.961 (0.866) \| .27 \| .27 \| 0.961 (0.866) \| 0.961 (0.866) \| 0.961 (0.866) \| .27 \| .27 \| \| \| R2: within \| 0.038 (N/A) \| 0.038 (N/A) \| \| \| 0.038 (N/A) \| 0.038 (N/A) \| 0.038 (N/A) \| \| \| \| \| R2: between \| 0.040 (N/A) \| 0.040 (N/A) \| \| \| 0.086 (N/A) \| 0.086 (N/A) \| 0.086 (N/A) \| \| \| \| \| R2: overall \| 0.039 (N/A) \| 0.039 (N/A) \| \| \| 0.064 (N/A) \| 0.064 (N/A) \| 0.064 (N/A) \| \| \| \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –1.390 (0.677) \| –1.390 (0.677) \| .04 \| .04 \| –1.390 (0.677) \| –1.390 (0.677) \| –1.390 (0.677) \| .04 \| .04 \| \| \| Week 24 \| –2.350 (0.677) \| –2.350 (0.677) \| .001 \| .001 \| –2.350 (0.677) \| –2.350 (0.677) \| –2.350 (0.677) \| <.001 \| <.001 \| \| \| Intervention group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Control group \| 0.079 (0.765) \| 0.079 (0.765) \| .92 \| .92 \| 0.393 (0.755) \| 0.393 (0.755) \| 0.393 (0.755) \| .60 \| .60 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × control group \| 1.090 (0.867) \| 1.090 (0.867) \| .21 \| .21 \| 1.090 (0.867) \| 1.090 (0.867) \| 1.090 (0.867) \| .21 \| .21 \| \| \| Week 24 × control group \| 0.987 (0.867) \| 0.987 (0.867) \| .26 \| .26 \| 0.987 (0.867) \| 0.987 (0.867) \| 0.987 (0.867) \| .26 \| .26 \| \| \| R2: within \| 0.144 (N/A) \| 0.144 (N/A) \| \| \| 0.144 (N/A) \| 0.144 (N/A) \| 0.144 (N/A) \| \| \| \| \| R2: between \| 0.030 (N/A) \| 0.030 (N/A) \| \| \| 0.157 (N/A) \| 0.157 (N/A) \| 0.157 (N/A) \| \| \| \| \| R2: overall \| 0.082 (N/A) \| 0.082 (N/A) \| \| \| 0.151 (N/A) \| 0.151 (N/A) \| 0.151 (N/A) \| \| \| \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| HECSId \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –0.513 (0.229) \| –0.513 (0.229) \| .03 \| .03 \| –0.513 (0.229) \| –0.513 (0.229) \| –0.513 (0.229) \| .03 \| .03 \| \| \| Week 24 \| –0.715 (0.229) \| –0.715 (0.229) \| .002 \| .002 \| –0.715 (0.229) \| –0.715 (0.229) \| –0.715 (0.229) \| .002 \| .002 \| \| \| Intervention group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Control group \| –0.158 (0.273) \| –0.158 (0.273) \| .56 \| .56 \| –0.062 (0.254) \| –0.062 (0.254) \| –0.062 (0.254) \| .81 \| .81 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × control group \| 0.327 (0.293) \| 0.327 (0.293) \| .26 \| .26 \| 0.327 (0.293) \| 0.327 (0.293) \| 0.327 (0.293) \| .26 \| .26 \| \| \| Week 24 × control group \| 0.429 (0.293) \| 0.429 (0.293) \| .14 \| .14 \| 0.429 (0.293) \| 0.429 (0.293) \| 0.429 (0.293) \| .14 \| .14 \| \| \| R2: within \| 0.102 (N/A) \| 0.102 (N/A) \| \| \| 0.102 (N/A) \| 0.102 (N/A) \| 0.102 (N/A) \| \| \| \| \| R2: between \| 0.01 (N/A) \| 0.01 (N/A) \| \| \| 0.286 (N/A) \| 0.286 (N/A) \| 0.286 (N/A) \| \| \| \| \| R2: overall \| 0.044 (N/A) \| 0.044 (N/A) \| \| \| 0.211 (N/A) \| 0.211 (N/A) \| 0.211 (N/A) \| \| \| ## An App Use Frequency of Fewer Than Once Every 5 Weeks Leads to a Significant Amelioration of Quality of Life, Pain, Activity, and Extent of Eczema When analyzing the outcomes in regard to app use frequency, the subgroup with an app use frequency of <$20\%$ showed a highly significant improvement in quality of life (DLQI) compared with the control group (V2: r=–1.23; $$P \leq .001$$; V3: r=–0.73; $$P \leq .05$$; Figure 3; Table 3). Overall, <$20\%$ app use means an app use frequency of <5 times over the study period. For the subgroup with ≥$20\%$ app use, there was no significant difference in the DLQI score compared with the control group (V2: r=–0.03; $$P \leq .91$$; V3: $r = 0.25$; $$P \leq .39$$; Figure 3; Table 3). The pain also improved significantly in the subgroup with <$20\%$ app use frequency compared with the control group (V2: r=–2.96; $$P \leq .02$$; V3: r=–2.97; $$P \leq .02$$; Figure 3; Table 3). In the subgroup with ≥$20\%$ app use frequency, there was again no significant effect (V2: $r = 1.41$; $$P \leq .14$$; V3: r=–0.11; $$P \leq .91$$; Figure 3; Table 3). In regard to the activity score of the patients, a significant improvement in the subgroup with <$20\%$ app use frequency in comparison with the control group was noted for V2, but not for V3 (V2: r=–3.07; $$P \leq .01$$; V3: r=–1.76; $$P \leq .17$$; Figure 3; Table 3). There were no significant differences in the subgroup with ≥$20\%$ app use frequency (V2: r=–0.03; $$P \leq .98$$; V3: r=–0.57; $$P \leq .56$$; Figure 3; Table 3). The HECSI showed a significant improvement in the subgroup with <$20\%$ app use frequency in comparison with the control group at V2 but again not at V3 (V2: r=–0.99; $$P \leq .02$$; V3: r=–0.65; $$P \leq .12$$; Figure 3; Table 3). There were again no significant differences in the subgroup with ≥$20\%$ app use frequency in comparison with the control group (V2: $r = 0.03$; $$P \leq .94$$; V3: r=–0.31; $$P \leq .35$$; Figure 3; Table 3). Again, all results were independent of sex, age, or disease duration (model 1; Table 3). Table 3 \| Assessment \| Assessment.1 \| Model 0 \| Model 0.1 \| Model 0.2 \| Model 1 \| Model 1.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| r (SE) \| P value \| r (SE) \| r (SE) \| P value \| \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| DLQIa \| \| \| Week 0 \| Refb \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –0.110 (0.159) \| .49 \| –0.110 (0.159) \| –0.110 (0.159) \| .49 \| \| \| Week 24 \| –0.435 (0.159) \| .006 \| –0.440 (0.159) \| –0.440 (0.159) \| .006 \| \| \| Control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Intervention group with <20% app use frequency \| 0.524 (0.421) \| .21 \| 0.531 (0.419) \| 0.531 (0.419) \| .21 \| \| \| Intervention group with ≥20% app use frequency \| 0.079 (0.331) \| .81 \| –0.089 (0.337) \| –0.089 (0.337) \| .79 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × intervention group with <20% app use frequency \| –1.230 (0.373) \| .001 \| –1.230 (0.373) \| –1.230 (0.373) \| .001 \| \| \| Week 12 × intervention group with ≥20% app use frequency \| –0.032 (0.293) \| .91 \| –0.032 (0.292) \| –0.032 (0.292) \| .91 \| \| \| Week 24 × intervention group with <20% app use frequency \| –0.733 (0.373) \| .049 \| –0.733 (0.373) \| –0.733 (0.373) \| .049 \| \| \| Week 24 × intervention group with ≥20% app use frequency \| –0.253 (0.293) \| .39 \| –0.253 (0.292) \| –0.253 (0.292) \| .39 \| \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| Pain \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| 0.583 (0.521) \| .26 \| 0.583 (0.521) \| 0.583 (0.521) \| .26 \| \| \| Week 24 \| 0.222 (0.521) \| .67 \| 0.222 (0.521) \| 0.222 (0.521) \| .67 \| \| \| Control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Intervention group with <20% app use frequency \| 1.390 (1.050) \| .19 \| 1.600 (1.060) \| 1.600 (1.060) \| .13 \| \| \| Intervention group with ≥20% app use frequency \| –1.440 (0.827) \| .08 \| –1.590 (0.846) \| –1.590 (0.846) \| .06 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × intervention group with <20% app use frequency \| –2.960 (1.220) \| .02 \| –2.960 (1.220) \| –2.960 (1.220) \| .02 \| \| \| Week 12 × intervention group with ≥20% app use frequency \| 1.420 (0.961) \| .14 \| 1.420 (0.961) \| 1.420 (0.961) \| .14 \| \| \| Week 24 × intervention group with <20% app use frequency \| –2.970 (1.220) \| .02 \| –2.970 (1.220) \| –2.970 (1.220) \| .02 \| \| \| Week 24 × intervention group with ≥20% app use frequency \| 0.111 (0.961) \| .91 \| 0.111 (0.961) \| 0.111 (0.961) \| .91 \| \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| Activity \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –0.306 (0.535) \| .57 \| –0.306 (0.535) \| –0.306 (0.535) \| .57 \| \| \| Week 24 \| –1.360 (0.534) \| .01 \| –1.360 (0.535) \| –1.360 (0.535) \| .01 \| \| \| Control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Intervention group with <20% app use frequency \| 0.764 (1.120) \| .50 \| 0.792 (1.100) \| 0.792 (1.100) \| .47 \| \| \| Intervention group with ≥20% app use frequency \| –0.572 (0.880) \| .55 \| –1.04 (0.880) \| –1.04 (0.880) \| .24 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × intervention group with <20% app use frequency \| –3.070 (1.250) \| .01 \| –3.070 (1.250) \| –3.070 (1.250) \| .01 \| \| \| Week 12 × intervention group with ≥20% app use frequency \| –0.028 (0.986) \| .98 \| –0.028 (0.986) \| –0.028 (0.986) \| .98 \| \| \| Week 24 × intervention group with <20% app use frequency \| –1.760 (1.250) \| .17 \| –1.760 (1.250) \| –1.760 (1.250) \| .16 \| \| \| Week 24 × intervention group with ≥20% app use frequency \| –0.572 (0.986) \| .56 \| –0.572 (0.986) \| –0.572 (0.986) \| .56 \| \| HECSIc \| HECSIc \| HECSIc \| HECSIc \| HECSIc \| HECSIc \| HECSIc \| \| \| Week 0 \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 \| –0.185 (0.181) \| .31 \| –0.185 (0.181) \| –0.185 (0.181) \| .31 \| \| \| Week 24 \| –0.286 (0.181) \| .11 \| –0.286 (0.181) \| –0.286 (0.181) \| .11 \| \| \| Control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Intervention group with <20% app use frequency \| 0.383 (0.399) \| .34 \| 0.466 (0.371) \| 0.466 (0.371) \| .21 \| \| \| Intervention group with ≥20% app use frequency \| 0.037 (0.314) \| .91 \| –0.160 (0.296) \| –0.160 (0.296) \| .59 \| \| \| Week 0 × control group \| Ref \| Ref \| Ref \| Ref \| Ref \| \| \| Week 12 × intervention group with <20% app use frequency \| –0.990 (0.423) \| .02 \| –0.990 (0.423) \| –0.990 (0.423) \| .02 \| \| \| Week 12 × intervention group with ≥20% app use frequency \| 0.026 (0.333) \| .94 \| 0.026 (0.333) \| 0.026 (0.333) \| .94 \| \| \| Week 24 × intervention group with <20% app use frequency \| –0.652 (0.423) \| .12 \| –0.652 (0.423) \| –0.652 (0.423) \| .12 \| \| \| Week 24 × intervention group with ≥20% app use frequency \| –0.310 (0.333) \| .35 \| –0.310 (0.333) \| –0.310 (0.333) \| .35 \| ## Male Patients Profit More From the Intervention Regarding the Clinical Outcome In a further subgroup analysis of the intervention group in regard to the sex-specific development of the HECSI, we found a significant improvement in the HECSI compared with baseline only for male participants (V2: r=–1.06; $$P \leq .008$$; V3: r=–1.21; $$P \leq .003$$). ## Correlation of the eHECSI With the HECSI Correlating the eHECSI assessed on the basis of pictures taken by the patients of their hands and feet with the HECSI recorded by physicians during regular personal visits, the eHECSI correlated strongly with the in-person–assessed HECSI ($r = 0.898$; $$P \leq .002$$) even when the quality of the images was not that good. If the pictures were of good quality, the correlation of the eHECSI with the HECSI was also highly significant ($r = 0.875$; $P \leq .001$). ## Principal Findings In our intervention study, we showed that the use of our monitoring app in combination with a patient education session has a significant effect on quality of life, pain, activity, and clinical outcome if the app is not used more than once every 5 weeks. In addition, men seem to profit more from app use frequency than women regarding the clinical outcome. We first analyzed differences between the intervention and control groups in regard to amelioration of quality of life, pain, activity, and eczema. All our study patients, independent of group membership, had less pain, showed an enhanced quality of life, and participated more actively in life; in addition, their skin condition improved over time. Although the intervention group showed a stronger improvement at all times, the difference between the 2 groups never reached significance. As our patients received a physician’s appointment every 3 months regardless of their skin condition, we conclude that the regular physician-patient contact was crucial for the amelioration of the disease in both groups. This aligns with the observations of Riedl et al [18] who showed that regular physician-patient contact leads to improvement in subjective and objective symptoms. Direct physician-patient contact seems to be more effective than an educational program combined with a monitoring app in the short term regarding our whole study population. In this case, the final evaluation of the study data at week 60 will provide better knowledge about the long-term effects achieved by our intervention. In our previous intervention study involving a 60-week monitoring app for patients with psoriasis, we were able to show that patient education in combination with a monitoring app resulted in a significant amelioration of depressive and anxiety symptoms in patients who used the app fewer than once a month [17]. In that study, we concluded that patients who were chronically ill do not wish to be reminded of their disease too often. Moreover, it seemed that patients do not want to invest too much time in documenting their disease because they already need to spend considerable time in taking care of their eczematous skin. Furthermore, in this study, an app use frequency of fewer than once every 5 weeks led to a significant amelioration of quality of life, pain, activity, and extent of eczema in the subgroup using the app fewer than once a month (<$20\%$ app use frequency) compared with the control group. The mainstay of hand and foot eczema management is still topical therapy, which needs to be applied several times a day. For patients with psoriasis, process aspects such as application time have been associated with nonadherence and a negative impact on quality of life [19,20]. In line with this observation, Retzler et al [21] showed that topical treatment regimens in patients with atopic dermatitis have a detrimental effect on quality of life that increases with treatment duration and frequency of application. Therefore, an additional time-consuming burden imposed on patients with hand and foot eczema such as a too-frequent app-based documentation of their skin disease might generate no additional benefits regarding quality of life and disease outcome. It should be noted that the collected data do not allow differentiating whether patients who used the app less frequently simply experienced an improvement in their skin condition. This group could have profited solely from the patient education, which enhanced knowledge, provided in the study. This observation is in concordance with the study by Ahn et al [22], who were able to show that patient education and web-based resources in dermatology increase compliance and adherence to therapy. We cannot rule out that the education provided by the 2 dermatological specialists led to the assessed significant improvement in the subgroup using the app fewer than once every 5 weeks, but in our previous study [12] for patients with psoriasis, the education alone had no effect on the outcome. Therefore, we assume that the same is true for patients with chronic hand and foot eczema. We additionally assessed whether patients reduce the app use frequency as their outcomes improve, but the subgroup with <$20\%$ app use frequency showed lower app use frequency from the start, with no decrease in the use in the course of time. By contrast, the app provided in the study allowed direct contact between patients and their treating physicians, which probably reassured patients and improved quality of life in the intervention group when using the app fewer than once a week. We believe that the mere possibility of being able to contact the supervising physician if needed rather than the frequency of physician-patient contact is decisive to improved quality of life. In our clinical perception, frequent physician’s appointments to obtain a follow-up prescription may become a burden, in particular for younger patients who have less time because of their jobs. Such patients might benefit significantly from additional teledermatological care. Another finding of our study was that the HECSI of male participants decreased faster than those of female participants, independent of app use frequency, although women show higher adherence to topical therapy [23]. We assume that men may benefit more from a constant reminder to apply their topical therapy provided by an eHealth device even when they avoid frequent documentation of their eczema in the app. A positive benefit for reminder apps has already been demonstrated for therapy adherence in patients with cardiovascular disease [24]. Further studies addressing this point are needed in patients with hand and foot eczema. One of the study’s great strengths was that we were able to show that telemedical care can at least partially replace personal care in patients with hand and foot eczema because the analysis of pictures taken by the patients correlates strongly with that of the in vivo images. Therefore, the HECSI assessed in the face-to-face visit correlated significantly with the eHECSI. This is surely not the case for all dermatological diseases in which the disease can affect the whole body, especially the genital area and the capillitium. A study by Zabludovska et al [25] concluded that only significant changes were detected by photographs; however, in the study, the number of participants was very small ($$n = 33$$). Whether photographs can be used to monitor the progression of chronic hand eczema and reliably determine HECSI should be further investigated. Our study includes some limitations. A major limitation is the monocentric design and the small study cohort, which limits generalizability of the results. In particular, the group with <$20\%$ app use frequency is very small, which could have led to missed or overinterpreted differences between the groups, especially as we compared this subgroup of the intervention group with the control group. Further studies are necessary to verify our findings on a broader scale. ## Conclusions Overall, our intervention had a positive effect on quality of life, pain, activity, and possibly the clinical outcome in a subgroup of patients with hand and foot eczema. We were able to show that a monitoring app for patients with hand and foot eczema that allows direct contact with their treating physicians combined with patient education may have the potential to improve the eczema outcome of these patients, especially if the app is not used too frequently. We believe that a monitoring app such as the one presented in this study has the potential to improve patient care and should be implemented in daily practice. However, because of the small number of participants, especially in the subgroups of the intervention group, as well as missing data on treatment adherence of the control group, these data need to be re-examined in a larger sample with consideration of individual factors. ## References 1. Brans R, Hübner A, Gediga G, John SM. Prevalence of foot eczema and associated occupational and non-occupational factors in patients with hand eczema. Contact Dermatitis (2015) 73 100-7. DOI: 10.1111/cod.12370 2. 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--- title: 'Health System–Level Barriers to Living Donor Kidney Transplantation: Protocol for a Comparative Case Study Analysis' journal: JMIR Research Protocols year: 2023 pmcid: PMC10031444 doi: 10.2196/44172 license: CC BY 4.0 --- # Health System–Level Barriers to Living Donor Kidney Transplantation: Protocol for a Comparative Case Study Analysis ## Abstract ### Background Living donor kidney transplantation (LDKT) is the best treatment option for patients with kidney failure and offers significant medical and economic advantages for both patients and health systems. Despite this, rates of LDKT in Canada have stagnated and vary significantly across Canadian provinces, the reasons for which are not well understood. Our prior work has suggested that system-level factors may be contributing to these differences. Identifying these factors can help inform system-level interventions to increase LDKT. ### Objective Our objective is to generate a systemic interpretation of LDKT delivery across provincial health systems with variable performance. We aim to identify the attributes and processes that facilitate the delivery of LDKT to patients, and those that create barriers and compare these across systems with variable performance. These objectives are contextualized within our broader goal of increasing rates of LDKT in Canada, particularly in lower-performing provinces. ### Methods This research takes the form of a qualitative comparative case study analysis of 3 provincial health systems in Canada that have high, moderate, and low rates of LDKT performance (the percentage of LDKT to all kidney transplantations performed). Our approach is underpinned by an understanding of health systems as complex adaptive systems that are multilevel and interconnected, and involve nonlinear interactions between people and organizations, operating within a loosely bounded network. Data collection will comprise semistructured interviews, document reviews, and focus groups. Individual case studies will be conducted and analyzed using inductive thematic analysis. Following this, our comparative analysis will operationalize resource-based theory to compare case study data and generate explanations for our research question. ### Results This project was funded from 2020 to 2023. Individual case studies were carried out between November 2020 and August 2022. The comparative case analysis will begin in December 2022 and is expected to conclude in April 2023. Submission of the publication is projected for June 2023. ### Conclusions By investigating health systems as complex adaptive systems and making comparisons across provinces, this study will identify how health systems can improve the delivery of LDKT to patients with kidney failure. Our resource-based theory framework will provide a granular analysis of the attributes and processes that facilitate or create barriers to LDKT delivery across multiple organizations and levels of practice. Our findings will have practice and policy implications and help inform transferrable competencies and system-level interventions conducive to increasing LDKT. ### International Registered Report Identifier (IRRID) DERR1-$\frac{10.2196}{44172}$ ## Introduction End-stage renal disease represents a major public health burden. Patients needing dialysis have extremely poor survival rates when compared with the general population [1,2]. This is because these patients have a higher cardiovascular disease burden, greater susceptibility to infections, and demonstrate a decreased response to vaccination [3-6]. The intrusiveness of dialysis can significantly impede normal facets of life, such as work, vitality, and freedom to travel [7]. Kidney transplantation, in particular living donor kidney transplant (LDKT), is widely regarded as the best therapeutic option for patients with kidney failure. When compared with patients undergoing dialysis, those who have undergone kidney transplantation experience a $64\%$-$75\%$ lower risk of death by the first year following transplantation [8-13]. LDKT is a surgery that involves a healthy individual donating one kidney to a patient (recipient) with kidney failure. Given that there is a finite pool of deceased donors in any given year, and that the demand for organs far exceeds its supply [11,14], pursuing LDKT can narrow this gap and provide early access to a transplant. There are also many medical benefits to LDKT. The median survival of a kidney transplant from a living donor is longer than that from a deceased donor [8-11]. Those with LDKT also experience lower rates of acute rejection, spend less time on dialysis, and have an improved quality of life [10,15-21]. Thus, there is considerable interest in increasing the rate of LDKT [15]. Despite its significant benefits, LDKT rates in Canada have stagnated over the past decade and continue to average around 12-14 living donors per 1 million population. This is despite national efforts to increase LDKT, such as the paired kidney exchange program [1,10,22,23]. There are also significant interprovincial variations across provinces. For example, in Quebec, Ontario, and British Columbia, Canada’s most populous provinces, <$15\%$, $30\%$-$40\%$, and $50\%$-$60\%$ of kidney transplants performed annually are from living donors, respectively [11]. Similar trends are noted when comparing the living donor rates per million population across these provinces [1,10,23,24]. The reason for this significant disparity in a country with universal health care is not known. Currently, the impetus of finding living donors is largely placed on the patient, and much of the present work to increase LDKT focuses on patients and addressing these microlevel barriers [25]. We conducted a qualitative study, exploring the perspectives of health professionals on the provision of LDKT [26,27] and identified poor communication between treating teams, absence of consistent guidelines, and lack of resources as barriers. Notably, some of these barriers were more prominent in provinces of Canada that have lower rates of LDKT. This work alluded to the existence of systemic attributes that impede the effective delivery of LDKT, thus driving the need to understand the factors driving these differences to inform system-level interventions to increase LDKT. As such, the objective of the study described in this protocol is to generate a systemic interpretation of LDKT by identifying the attributes and processes that facilitate the delivery of LDKT in a provincial health system and those that create barriers. We also aim to identify the differences between these attributes and processes by comparing higher- and lower-performing systems. These objectives are contextualized within our broader goal of increasing rates of LDKT in Canada, particularly in lower-performing provinces. Our primary research question is the following: what are the attributes and processes of provincial health systems that account for variability in LDKT rates? ## Research Approach This study takes the form of a comparative case study analysis as described by Yin [28,29] and illustrated in Figure 1. Case study research is an in-depth, noninterventional examination of a single case over time to investigate a contemporary phenomenon in its natural context [29,30]. Case studies have been identified as the preferred methodology for examining high-performing health systems [31-39]. As quantitative data concerning rates of LDKT across provinces have been well-documented elsewhere [40-4], our study follows a qualitative design to investigate how and why these differences in performance exist. Qualitative methods have established relevance to answering these questions in health research [43]. Understanding the system as an integrated whole lies in understanding the patterns and relationships between its levels and key players [44]. Our qualitative design will investigate these real-world behaviors and perspectives at multiorganizational levels, in order to understand full system function. Thus, our case study analysis will be explanatory because it will explore and connect how certain attributes and processes of a provincial health system are linked to the provision of LDKT [28]. It will be inductive—that is, allowing themes and explanations to be derived primarily from a close reading of the data—without trying to fit it into a priori concepts. Conceptually, our approach is underpinned by an understanding of health systems as complex adaptive systems (CASs). The concept of CAS stems from the complexity theory and takes a dynamic systems approach. A CAS is “an entity composed of many different parts that are interconnected in a way that gives the whole capabilities that the parts don’t have on their own” [45]. A provincial health system that delivers LDKT can be understood as a CAS, in that, it is a multilevel, interconnected system that involves nonlinear interactions between people and organizations, operating within a loosely bounded network. CAS approaches have been used increasingly as an analysis and research development tool in health care, with favorable results [44,46,47]. There also exists a tight fit between a CAS approach and case study methodology [44]. Researchers have identified that multiple methods that are often used in case study research lend themselves to understanding emergent elements and system dynamics [44]. Complexity theory also suggests that comparing the best to the worst in multiple case comparison can be a fruitful way of understanding the source of new structural arrangements and patterns of behavior [44,48]. Accordingly, our comparative method entails the comparison and synthesis of the similarities, differences, and patterns across multiple cases that share a common goal [39,49]. Figure 1: Comparative case study analysis design (adapted from Yin’s [28,29] methods of case study research). BC: British Columbia; ON: Ontario; QC: Quebec. ## Case Definition In accordance with the CAS theory, we defined each provincial “case” as the health system involved in facilitating LDKT. Adapted from the 4-level model proposed by leading agencies [50] we mapped a whole-system model of LDKT, composed of macro-, meso-, and microlevels of practice that are interconnected, dynamic, and nested, with the patient at its core (Figure 2) [51]. These levels include organizations, service providers, recipients, and donors, representing the human and nonhuman agents that are implicated in the delivery of LDKT and thus form the elements for our analysis. Figure 2: Envisioning the health system that delivers living donor kidney transplantation to patients as a complex adaptive system (adapted from the 4-level model proposed by the National Academy of Engineering [United States] and Institute of Medicine [United States] Committee on Engineering and the Health Care System). ## Case Selection We will conduct a comparative case study between British Columbia, Ontario, and Quebec, which represent respectively high, moderate, and low performance in LDKT as defined by the percentage of LDKTs to all transplantation performed annually [11]. These three provinces represent $75\%$ of Canada’s population, and over $70\%$ of the patients with end-stage renal disease reside in these provinces. Thus, performance in these provinces significantly influences the country’s overall transplant results. Additionally, since these provinces have low, moderate, and high rates of LDKT, we have an ideal case mix to facilitate interprovincial learning, as well as for theoretical replication, reliability, and external validity [28]. ## Design Our study follows sequential stages of data collection and analysis (Figure 1). We have conducted independent case studies of the health systems in British Columbia, Ontario, and Quebec using the data collection methods discussed below. Following data collection and independent analysis in all 3 provinces, we have recently conducted focus groups with stakeholders from across Canada, asking their opinions and experiences of the themes derived from the preliminary analysis of these cases. We will now conduct a comparative case study of British Columbia, Ontario, and Quebec. We will use the focus group data to develop and refine the themes from our comparative case study to form a final analysis. ## Participants Participants from different levels of the health system, as shown in Figure 2, were recruited for interviews. As LDKT is organized largely by each province, representatives from federal bodies were invited to participate in focus groups, where we discussed and refined the national relevance of our findings. Table 1 shows a breakdown of participants invited for an interview. The composition of interviewees seen in Table 1 comprises an approach to “studying through,” tracing relations among actors, institutions, and discourses across spaces through interview data [52,53]. In accordance with our CAS approach, our participants represent macro-, meso-, and microlevels of practice. As the organizations implicated in LDKT vary among British Columbia, Ontario, and Quebec—for example, Quebec does not have a provincial renal program—the list of participants invited was adjusted accordingly. Table 1 \| Participant category \| Number for each province \| \| --- \| --- \| \| Ministry of Health representative \| 1-2 \| \| Organ Donation Organization representatives \| 2-3 \| \| Renal program representatives \| 2-3 \| \| Health care professionals at transplant centers \| 8-10 \| \| Health care professionals at nephrology clinics or dialysis centers \| 8-12 \| \| Living donor kidney transplant recipients \| 2-4 \| \| Living donors \| 2-4 \| ## Recruitment To recruit participants, purposive criterion sampling was used to invite key leadership at organ donation organizations, provincial renal programs, and transplant centers. Participants were considered to have key leadership roles if they held decision-making authority with interorganizational impact. Thereafter, snowball sampling was used to recruit providers from kidney care clinics and dialysis centers [52,55]. Data gathering continued until data saturation was reached; that is, when new interviews and document reviews did not provide additional information [56]. ## Semistructured Interviews Semistructured interviews were conducted to understand the dynamic organization, governance, and care entailed in LDKT delivery and the interdependencies between the elements of each provincial health system. We also sought to understand what aspects of the system variously promoted or hindered patient access to LDKT. Interview guides for professional participants addressed their involvement in facilitating LDKT for patients, their interactions with other professionals in this process, their attitude toward LDKT, and which phenomena helped and which ones posed challenges in their work. Interview guides for donors and recipients of LDKT focused on their experiences of LDKT, their perception of care, and what helped and hindered their care path. Distinct interview guides with open-ended questions were developed for each category of participant with the combined expertise of our research team and preliminary document review (see sample guide in Multimedia Appendix 1). We followed an iterative approach whereby issues or ideas identified by participants were discussed with subsequent participants to enable further definition and refinement of themes [57]. Interviews were conducted remotely in English or French by our bilingual research coordinator (AH). All interviews were digitally recorded and transcribed. Participants were compensated with a CAD $50 (US $37.57) gift card following their participation in the interview. ## Document Review Document review served as complementary data collection to inform our understanding of programs, policies, and resources concerning LDKT in each province and as means of triangulation with interview data [58]. Documents for review were identified in consultation with our collaborators in each province, during interviews, and using web searches of governmental, organ donation organizations, renal programs, and hospital platforms. Documents were included if they were a policy, guideline, resource, program outline, presentation, announcement, or report pertaining to LDKT. Searches were conducted in both English and French. ## Focus Groups Following data collection in all 3 provinces and the initial coding of individual case studies, we conducted 4 focus groups remotely with the purpose of gleaning opinions about our preliminary themes from key stakeholders (Table 2). Focus groups comprise a small group of people brought together to discuss a particular issue, under the direction of a facilitator [59]. They are widely used in health research and are recognized to produce considerable information in a short space of time [59]. We recruited previous interview participants for focus groups, as well as patients, patient partners, and professionals from other provinces of Canada. In these focus groups, we presented themes from our preliminary analysis of the 3 case studies to participants and asked them about their opinions and experiences. Focus group guides were developed from our initial data analysis and reviewed by the research team. Each focus group lasted approximately 60 minutes, comprised 5-13 participants, and was conducted by the research team experienced in facilitating discussions in this setting (AH and KL). The focus groups were audio-recorded and transcribed. Participants were compensated with a CAD $50 (US $37.57) gift card following their participation in the focus group. Table 2 \| Focus group \| Approximate number of participants \| Participant types \| Language of conduct \| \| --- \| --- \| --- \| --- \| \| 1 \| 10-12 \| A health care professional working in transplantation or nephrology; or a representative from a provincial renal program, organ donation organization, or provincial health ministry, who has previously participated as an interviewee in this study \| English \| \| 2 \| 4-6 \| A health care professional working in transplantation or nephrology; or a representative from a provincial renal program, organ donation organization, or provincial health ministry, who works in a province outside of British Columbia, Ontario, and Quebec \| English \| \| 3 \| 4-6 \| An LDKTa recipient or living donor who has experienced LDKT in the last 7 years and whose preferred language of conduct is English \| English \| \| 4 \| 4-6 \| An LDKT recipient or donor who has experienced LDKT in the last 7 years and whose preferred language of conduct is French \| French \| ## Individual Case Studies Data from each case study were analyzed using inductive thematic analysis [43]. Thematic analysis involves identifying and analyzing patterns of meaning [60], and mapping regularities and variations across different accounts [59,61]. It is best suited to studying the processes and attributes of a system because it provides a “map” of the content and patterns across a data set [59], preserving our whole-system approach to understanding LDKT delivery [44]. Interview transcripts were analyzed independently by 2 research associates experienced in qualitative research (AH and KL). NVivo (version 12; QSR International) was used to support data management and analysis. Transcribed interview data were read and highlighted line-by-line to openly derive preliminary codes that emerged iteratively from the data set. These codes were organized into categories and subcodes to form an initial coding scheme. Codes were then compared across the data set for regularities and divergences and modified accordingly. Through this process of inductive analysis, a coding scheme evolved, which retained strong links with the original data set [62]. The resultant codebooks for each individual case study have been retained as the basis for cross-comparative analysis. Coding, emerging themes, links between themes, and any disagreements between the 2 research associates were discussed at regular research team meetings. Our analytic procedure of documents entailed appraising and synthesizing the data contained in documents, followed by clustering the documents thematically [58]. The 2 research associates compared interview data to verify and corroborate findings from each case study. Analyses from each province are being published as individual case studies as the research progresses. ## Comparative Case Analysis Our comparative analysis will operationalize the resource-based theory (RBT) to compare case study data and generate explanations for our research question. The RBT is a strategic management theory that provides a framework for explaining and predicting the basis of an organization’s competitive performance and advantage [63]. RBT involves a broad classification of resources as tangible and intangible assets and aims to assess how resources create strategic advantage by examining how they are combined and managed [64,65]. It is a framework with established relevance in health systems and health management research to understand problems of high organizational complexity [66]. Thus, there is a high level of theoretical congruence between the complexity theory and the RBT, as the RBT engages with the social complexity of how resources—physical, human, and organizational—are combined [67]. The importance of network competence, dynamic capabilities, and strategic alliances between organizations is also well-recognized in this framework for achieving strategic advantage [67-69]. This is particularly pertinent for the study of the CASs that provide LDKT, where management is distributed between organizations and interorganizational relationships [69]. As such, the RBT provides relevant and useful concepts to understand the full-system function of LDKT, as well as identify the attributes and processes that characterize a high-performing system. Our analysis will follow the principles of RBT, with an emphasis on collaborative organizational relationships, in order to understand and compare the whole-system function of LDKT provision. Following inductive coding and individual analysis of data collected from British Columbia, Ontario, and Quebec, we will use an RBT framework to analyze and compare our case study data and generate explanations for our research question. To do this, we will organize codebooks from each province into capabilities identified from the RBT literature, following questions stemming from these capabilities to guide our organization (Table 3). According to the RBT, a capability is what can be done as a result of resources working together [70]. We will therefore delineate and compare the attributes and processes from each province that determine their capabilities to deliver LDKT. The same 2 research associates who coded individual case studies will go through codebooks from each case study to extract information about the RBT capacities identified above and assign existing codes to the relevant RBT capability, to build themes. Through an iterative process, we will assign codes to emerging themes, create new themes where needed, and merge themes if they replicate each other until saturation is reached. We will then compare and contrast the themes in each RBT capability among provinces to identify “distinctive competencies” [71]; that is, attributes and processes that exist in certain provinces and not in others. We will compare our focus group data (analyzed using inductive thematic analysis, following the same process described above [43]) to themes from the comparative analysis, adding these data to existing themes where it is concurrent and creating new themes where it diverges. This process will deepen our understanding of the attributes and processes we have identified, and their relevance to other Canadian provinces. Following this approach, we will develop explanations for how resources in each province are deployed to achieve strategic advantage and to explain one province’s strategic advantage relative to others in LDKT performance. We will contextualize these capabilities in characteristics described by interviewees that operate externally from the provincial organization of LDKT, which influence delivery. We will situate our analysis in a discussion of these external dependencies. Table 3 \| Capacity \| Guiding questions \| \| --- \| --- \| \| Resources \| What, where, and how are resources deployed in LDKTa delivery? \| \| Competition for resources \| What competition exists for resources to facilitate LDKT? \| \| Organizational capacity \| What are the organizational capacities of the organizations involved in LDKT delivery? \| \| Collaborative capacity \| What collaborative capacities exist in and between organizations? \| \| Value creation \| What activities create value for LDKT? \| \| Dynamic capabilities \| What are the dynamic capabilities of organizations? \| ## Ethics Approval Ethics approval for this study was obtained from the McGill University Health Centre Research Ethics Committee (MP-37-2021-7126/LDKT Case Study). This study is being conducted in accordance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans [2014], and the Declaration of Istanbul. ## Results This project was funded by a grant from a Gift of Life Institute, a Clinical Faculty Development Research Grant from the American Society of Transplantation from 2020 to 2021, and by a Health Research Grant from the Kidney Foundation of Canada. Individual case studies of British Columbia, Ontario, and Quebec were carried out between November 2020 and August 2022. The individual case study findings of LDKT delivery in British Columbia have been published [72,73]. Focus groups were carried out between June and November 2022. The comparative case analysis began in December 2022 and is expected to conclude in April 2023. Writing up of our findings is projected for May 2023, and manuscripts are expected to be submitted for publication by July 2023. A patient partner from Quebec was consulted regarding participant selection, study materials, and analysis and was involved in the publication of the case study [73]. ## Expected Findings This study aims to produce a system-level understanding of LDKT delivery in Canada’s 3 most populous provinces that have variable rates of LDKT, presenting a unique opportunity for comparative analysis. Informed by quantitative data [11], we are using qualitative methods to explain these differences. We want to identify the attributes and processes that facilitate or create barriers to LDKT using the RBT. Our preliminary findings suggest that barriers to effective LDKT delivery exist at different organizational levels of the health system and, critically, in the relationships between these organizations. LDKT delivery is aided by supportive governance organizations with a provincial overview, which increase the collaborative capacities of the system, boost organizational capacity, and generate value for LDKT across the different organizations involved [72,73]. To our knowledge, a comparative case analysis approach has not been used in the field of nephrology or kidney transplantation. Our approach has implications in these disciplines where there exists a poor understanding of system-level factors leading to inferior outcomes, inequities in access to therapy, and fractured transitions of care. This work also has global implications as LDKT is the main way to obtain a transplant in many countries that lack infrastructure for deceased donation. Based on our preliminary results and background work, we believe that to make significant improvements to LDKT delivery, interventions must target the dynamic relationships between different elements of a system. Much of the current work has focused on microlevel interventions to improve LDKT delivery [25,51], missing the important influence of meso- and macropractices and the dynamic interdependencies that exist between these levels of a health system [51]. Our findings will build on other studies that focus on implementing interventions to improve patient education and comfort on LDKT [16,74-78], and will help reenforce it by informing effective implementation strategies that encompass all levels of a health system. ## Limitations The following limitations to our study may apply. First, our data collection largely pertains to 3 provinces of Canada and our findings may not be applicable to other regions and countries. Nonetheless, it should be noted that Quebec, Ontario, and British Columbia are Canada’s most populous provinces and represent $75\%$ of the Canadian population, and our focus group data also go some way to establish the pertinence of our findings to other provinces. Our data also lay the foundations to extend our work across Canada and to other countries. Second, our research will not comprehensively explore the system-level factors leading to disparities in LDKT, such as gender and sex disparities and low rates of LDKT in Indigenous and other vulnerable populations. However, the data collected in this study will inform future systematic approaches needed to address this complex issue. Another limitation may pertain to the challenge of delineating the “boundaries” of the health systems that form the basis for our cases. Identifying the unit of analysis for case study research has long been identified as a challenge [79], and in the complex field of health care, it may be difficult to differentiate between organizational and system boundaries and their environments [80]. We have delineated our cases by drawing on the extensive expertise of our team and collaborators, many of whom are practitioners in this field, as well as following a snowballing technique to iteratively identify the actors and organizations implicated in LDKT delivery. However, some perspectives may have been missed in this process, for example, family physicians who are primarily involved in living donor care for and may exercise influence over the LDKT process. To mitigate this challenge, we will situate our analysis in a detailed discussion of factors external to the boundaries of our case, which impact LDKT delivery. Where relevant, we will highlight areas for further research. Relatedly, there is some variation in our sample size and organizational representation between cases, given the heterogeneity in how care is structured in different provinces. This may have some impact on how we are able to compare across provinces. Our analysis will include an in-depth discussion of the structural differences in renal care among British Columbia, Ontario, and Quebec to situate our findings. Finally, though we believe our document review serves as useful complementary data collection and triangulation with interview data, we acknowledge that it may not be exhaustive. ## Conclusions LDKT is the optimal treatment option for patients with kidney failure; yet, rates of LDKT have stagnated in Canada and vary significantly across provinces. There is a need to better understand how health systems deliver LDKT to patients. Following our prior work that has suggested system-level differences contributing to variability in LDKT performance, we will generate a systemic interpretation of LDKT delivery by identifying the attributes and processes that facilitate or create barriers to the delivery of LDKT. We will also identify the differences between these attributes and processes by comparing higher- and lower-performing provincial health systems. This qualitative comparative case study analysis is informed by CASs, and data analysis will be carried out in accordance with the RBT. Our findings will have practice and policy implications and help inform specific strategies, regulations, and infrastructure that are transferrable competencies and conducive to promoting the service delivery of LDKT. ## Data Availability The data sets generated and analyzed during this study are not publicly available, as informed consent to share transcribed data for secondary use beyond this research was not obtained from the participants. ## References 1. Organ replacement in Canada: CORR annual statistics 2020. Canadian Institute for Health Information 2. US Renal Data System 2015 Annual Data Report: epidemiology of kidney disease in the United States. Am J Kidney Dis (2016.0) 67 A4. 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--- title: '360° Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study' journal: JMIR Formative Research year: 2023 pmcid: PMC10031445 doi: 10.2196/37305 license: CC BY 4.0 --- # 360° Diagnostic Tool to Personalize Lifestyle Advice in Primary Care for People With Type 2 Diabetes: Development and Usability Study ## Abstract ### Background Various multifaceted factors need to be addressed to improve the health and quality of life of people with type 2 diabetes (T2D). Therefore, we developed a web-based decision support tool that comprises a more holistic diagnosis (including 4 domains: body, thinking and feeling, behavior, and environment) and personalized advice. This 360° diagnostic tool enables people with T2D and health care professionals at the general practice to obtain an overview of the most important T2D-related issues and, subsequently, determine the most suitable intervention for the person with T2D. ### Objective This study aimed to describe the systematic and iterative development and evaluation of the web-based 360° diagnostic tool. ### Methods We defined the requirements for the web-based 360° diagnostic tool based on previously developed tools, a literature review, and inputs from a multidisciplinary team of experts. As part of the conceptualization, we defined 3 requirements: diagnostics; feedback; and advice, consultation, and follow-up. Next, we developed and designed the content for each of these requirements. We evaluated the diagnostic part of the tool (ie, measurement instruments and visualization) with a qualitative design, in a usability study with a think-aloud strategy and interview questions, among 8 people with T2D at a Dutch general practice. ### Results For each of the 4 domains, specific parameters and underlying elements were selected, and measurement instruments (including clinical data and questionnaires) were chosen. Cutoff values were defined to identify high-, middle-, and low-ranking scores, and decision rules were developed and implemented using R scripts and algorithms. A traffic light color visual design was created (profile wheel) to provide an overview of the scores per domain. We mapped the interventions that could be added to the tool and developed a protocol designed as a card deck with motivational interview steps. Furthermore, the usability study showed that people with T2D perceived the tool as easy to use, useful, easy to understand, and insightful. ### Conclusions Preliminary evaluation of the 360° diagnostic tool by experts, health care professionals, and people with T2D showed that the tool was considered relevant, clear, and practical. The iterative process provided insights into the areas of improvement, which were implemented. The strengths, shortcomings, future use, and challenges are also discussed. ## Background Type 2 diabetes (T2D) is considered a public health problem worldwide. The prevalence rate of diabetes is high and continues to increase [1]. Approximately 537 million adults (aged 20-79 years) live with diabetes, of which the majority ($90\%$) have T2D [2]. T2D contributes to increased mortality and morbidity [1,3]. People with T2D have an increased risk of adverse health- (eg, cardiovascular diseases, neuropathy, and retinopathy), social-, and economic-related consequences [1,4,5]. As multiple factors are related to T2D, it is important to use a holistic approach in the management of T2D to improve the health and quality of life of people with T2D. Health care usually focuses on physical health factors, whereas a holistic approach also includes other factors such as lifestyle, mental health, and socioeconomic environment factors, and as such, demedicalizes societal problems [6,7]. Thus, it is important to develop a tool that enables a person with T2D, together with the health care professional at the general practice, to obtain an overview of the most important T2D-related issues to determine the most suitable management options for the individual. Such a tool may [1] increase and support self-management, empowerment, and informed decision-making for people with T2D and [2] improve communication between health care professionals and people with T2D (including shared decision-making and shared treatment strategy) [6]. Besides physical health factors (eg, familial predisposition, insulin resistance, and obesity), lifestyle-related factors (eg, eating pattern, physical activity, and sedentary behavior), mental health factors (eg, emotional stress, anxiety, and depression), and socioeconomic environment factors (eg, neighborhood deprivation) have also been identified as risk factors of T2D [8-11]. These risk factors not only influence T2D but also affect how individuals experience and manage their T2D [7,12]. Inversely, T2D may affect these factors and, thus, an individual’s quality of life. For example, T2D could induce diabetes-related depression or emotional stress and thus may affect mental health [7,13-16]. Moreover, these factors may also interact with each other. For example, the socioeconomic environment (eg, social networks) may influence physical and mental health [7]. Therefore, it is necessary to consider a holistic approach for T2D that involves all these factors during the diagnosis and treatment to obtain a broad perspective on the health status of individuals with T2D. In addition to the empirical evidence that a holistic approach is well suited for diagnosing and managing T2D, it is also in line with the more recent approach of positive health. This concept covers a more dynamic definition of health, focusing on an individual’s capacity to deal with new situations (resilience and coping) [6]. To develop a tool that encompasses a holistic approach, including comprehensive health status assessments that are subsequently discussed between patient and health care professionals and followed up by interventions, if necessary, we were inspired by the web-based tools of the Power2DM project [17] and the Primus project [18]. The goal of the Power2DM project [17] was to support patients with diabetes by developing a self-management support system and a shared decision-making app for health care professionals and patients. The goal of the Primus project [18] was to improve the health and quality of life of people aged 55-74 years. A web-based screening of behaviors that increase the risk of cardiovascular diseases, depression, or loneliness was developed. Using if-then decision rules, the screening results were used to tailor a written health risk assessment. For instance, individuals were advised to lose weight if their BMI was >25. Next, these results were discussed with a nurse practitioner in primary health care and the person with T2D on how to implement the advice to improve the person’s lifestyle. In the Netherlands, people with T2D are mainly under the care of nurse practitioners supervised by a general practitioner (GP) in primary health care. In addition to the Primus project example, we wanted to develop a tool that visualizes the results of the assessment to facilitate the discussion about it and to support informed and shared decision-making. Some examples are available, such as the “Web Diagram” by Huber et al [6], the Assessment of Burden of Chronic Conditions tool [19], and the “Self-Management Web” by Beck et al [20]. We wanted the visualization to be easily understood, encompassing a holistic approach and motivating discussion of actions. Moreover, to facilitate discussions between patients and health care professionals, it is necessary to offer professional guidance on achieving shared decision-making and to use a directive, client-centered counseling style to elicit behavioral change [21,22]. In summary, it is important that the tool includes the following 3 components: diagnosis (ie, performing the diagnosis: the GP’s assistant performs the measurement and the patient completes the questionnaires); feedback (ie, communicating the diagnosis: the patient receives the feedback and the explanation of the results); and advice, consultation, and follow-up (ie, acting on the diagnosis: discussion during the consultation on improving the results via personalized advice and the patient working on personal goals). ## Objectives For a more holistic diagnosis and personalized advice approach for people with T2D, we developed a web-based tool, that is, a 360° diagnostic tool. In line with empirical evidence, the positive health concept, and the dynamic definition of health, our holistic approach for T2D includes 4 domains: physical health, mental health, lifestyle behavior, and socioeconomic environment. Moreover, this holistic approach sheds light on the relationships between domains, for example, how lifestyle behavior influences physical health. Diagnosing people with T2D in all 4 domains will facilitate a more personalized approach in which they can successfully face their specific physical, emotional, and social challenges, which includes prioritizing and provisioning tailored interventions to improve their T2D and quality of life. The 360° diagnostic tool is intended as a decision support tool that enables people with T2D and health care professionals in the general practice setting to identify and address relevant factors affecting T2D and its impact on a person to determine the most suitable interventions. In this paper, we describe the systematic and iterative development and evaluation of this web-based 360° diagnostic tool. ## Procedure As part of the web-based 360° diagnostic tool, we defined the requirements for diagnosis; feedback; and advice, consultation, and follow-up. We based our approach regarding diagnosis, feedback, advice, consultation, and follow-up on the setup of the Primus project [18]. First, to enable a diagnosis, an overview of the possible relevant parameters for each of the 4 domains was made. A team of experts from the Netherlands Organization for Applied Scientific Research (TNO), representing multidisciplinary research fields, and health care professionals acted as an advisory committee in the iterative process of the development of the tool. For each domain, the potential list of parameters and elements was drafted by 3 experts in the field using previously developed tools in the Primus project [18] and Power2DM project [17], a literature review, and expert consultation. Thereafter, 3 other experts from the advisory committee reviewed and complemented the list. Parameters were defined as barriers or risk factors for people with T2D and that are changeable. Subsequently, for each parameter, the underlying elements were identified. Elements were defined as the measurable components of a parameter (for instance, the parameter “blood pressure” consists of the elements “systolic blood pressure” and “diastolic blood pressure”). The final step was to select a measurement instrument to assess each element. A measurement instrument had to fulfill certain criteria, such as (validated and reliable) questionnaires that were easy to complete and as short as possible. Then, the instruments regarding the body domain had to be components of standard clinical care (eg, blood pressure). Second, to provide feedback on each parameter or underlying element, communicating the diagnosis was an important part of the conceptualization. Therefore, for each measurement instrument (ie, element), we had to decide the cutoff values. These values were based on the literature to identify high-, middle-, and low-ranking scores. Decision rules were developed to combine the elements to rank the main parameters. To implement these decision rules for the elements and parameters in a web-based portal, R scripts and algorithms were developed. We aimed to develop a tool that visualizes the assessment results to facilitate discussion and support informed and shared decision-making between people with T2D and health care professionals in general practice. We wanted the visualization to be easily comprehensible, encompassing all 4 domains and motivating discussion of the actions. Third, to enable advice, consultation, and follow-up, a protocol was needed to discuss the 360° diagnosis in a structured manner during the consultation with the health care professional. The protocol was developed based on a 3-talk model of shared decision-making [21] and motivational interviewing (MI) [22]. The aim of the 360° diagnosis and profile wheel was to identify and address relevant factors that affect a person with T2D and to obtain personalized advice and the most suitable treatment option. Therefore, we first needed to compile a list of interventions in the Netherlands for all the parameters included in the 360° diagnosis. Subsequently, we needed to use the middle- and low-ranking scores in the profile wheel to decide the parameters for which an intervention could be offered to the individual. Interventions were selected if they could positively alter these parameters. We conducted desk research with inputs from [1] experts of the advisory committee and [2] internet searches. Interventions were included when they could be clearly identified as one distinct intervention and when they were available either nationwide or in 2 regional areas where we intended to evaluate and test the 360° diagnostic tool. Interventions could range from apps (eg, MySugr), websites, local walking initiatives, mindfulness courses, or referral to a dietician. Finally, we compiled a list of intervention options that could be added to the tool to provide personalized advice. Interventions were presented as specific pieces of advice for discussion during the consultation that could improve the middle and low scores of parameters or underlying elements. A follow-up was planned after the first consultation. To evaluate whether the diagnostic part of the tool (ie, instruments, visualization, and technology) was relevant, acceptable, and usable, we presented the tool to the advisory committee and conducted a qualitative study, that is, a usability study with a think-aloud strategy and evaluative interview questions, among people with T2D at the general practice “Mozaiek” in Rotterdam, the Netherlands (for more information about usability testing, refer to the study by Maramba et al [23]). The prototype of the 360° diagnostic tool presented to both the GP and nurse practitioner at the general practice “Mozaiek” in Rotterdam, the Netherlands, received a positive evaluation and was considered relevant and practical. For the usability study, 8 people with T2D were recruited from the general practice “Mozaiek” in Rotterdam, the Netherlands. Participants were recruited via a nurse practitioner of the general practice, specifically focusing on people with T2D with low socioeconomic status. The inclusion criteria for participation in the study were a diagnosis of T2D and being able to read and speak Dutch. The participants participated individually. The procedure was as follows: the interviewer filled in the biomarker data of the participants in the domain “body.” Participants then completed web-based questionnaires for the other 3 domains (duration of 10-20 minutes). On the basis of these responses, the diagnosis could be calculated and shown in the profile wheel. While filling in the questionnaires and inspecting the graphical feedback in the profile wheel, users were encouraged to think aloud. Subsequently, evaluative interview questions regarding certain topics were asked, for example, whether specific questions or questionnaires posed difficulties as well as regarding the profile wheel, whether they recognized themselves in the feedback, and if the icons were self-explanatory. Thus, the interviewer discussed the visualization (eg, understandable, recognizable, and informative) and the questionnaires (eg, wording, difficulty, and length) with the participants. The total session took 90 minutes. The results of the usability study concerned the relevance, acceptability, and usability of the tool, specifically regarding the evaluation of the measurement instruments and the visualization of the tool. ## Ethical Considerations The 8 participants in the usability study were informed by the nurse practitioner of the general practice about what was expected of them when participating in the study, and participants verbally provided informed consent. Subsequently, during participation, the interviewer informed each participant again at the beginning and asked if there were any questions. The privacy and anonymity of the participants were guaranteed. As a reward for participating in the study, participants received a voucher of €25 (US $26.08). This usability study was the first phase of a larger project approved by the Medical Ethics Committee Brabant (NL67846.028.18; January 8, 2019). ## Diagnosis The first list of parameters and instruments compiled by the first 3 experts included 138 different instruments to assess holistic health, including neurology, metabolic health, motor skills, physical fitness, personality, eating behavior, quality of life, and mental health. This list was further edited by another 3 experts focusing on lifestyle and T2D. The final diagnostic tool included the following 4 domains: body, thinking and feeling, behavior, and the environment. Specific parameters for each of the 4 domains were selected. It was decided that each domain should not have >6 parameters for visualization and comprehensibility reasons (Table 1). The domain “body” included the parameters glucose metabolism, blood pressure, cholesterol, weight, and kidney function. The domain “thinking and feeling” included the parameters perceived health, pain, mental health, perceived stress, and problems with T2D. The domain “behavior” included the parameters alcohol consumption, cigarette smoking, eating patterns, physical activity and exercise, sedentary behavior, and T2D management. The domain “environment” included the parameters family, loneliness, work, income, and housing. Most of the parameters consisted of the underlying elements, as listed in Table 1. For example, regarding the domain “behavior,” the following two underlying elements of the parameter “alcohol consumption” were included: [1] the average number of glasses per day and [2] binge drinking. To develop a T2D diagnostic and communication tool, we selected measurement instruments to assess each element of the 360° diagnostic tool. It was decided how to measure each of these elements and by whom (eg, which measurement instruments, including their feasibility, accessibility, and burden). The elements included for the domain “body” were measured through clinical data filled in by the health care professional, for example, hemoglobin A1c (HbA1c; also referred to as glycated hemoglobin), sober glucose, high-density lipoprotein, and low-density lipoprotein. The parameters or underlying elements of the other 3 domains were measured through questionnaires completed by the person with T2D, for example, the average number of glasses of alcohol per day, perceived stress, and loneliness. Furthermore, the number (of items) and duration of the questionnaires were considered to determine which instruments to use. Table 1 \| Domain and parameters \| Domain and parameters.1 \| Elements \| Instruments \| \| --- \| --- \| --- \| --- \| \| Bodya \| Bodya \| Bodya \| Bodya \| \| \| Glucose metabolism \| HbA1cb, fasting glucose, and 2-hour glucose \| Determined from blood samples \| \| \| Blood pressure \| Diastole and systole \| Blood pressure monitor \| \| \| Cholesterol \| HDLc, LDLd, total cholesterol, and ratio HDL and triglycerides \| Determined from blood samples in a laboratory \| \| \| Weight \| BMI, waist circumference, and waist-hip ratio \| Weighing scale and measuring tape \| \| \| Kidney functioning \| eGFRe and albuminuria stages \| Determined from morning urine in a laboratory \| \| Thinking and feelingf \| Thinking and feelingf \| Thinking and feelingf \| Thinking and feelingf \| \| \| Perceived health \| N/Ag \| A single general health item from the Medical Outcomes Survey Short-Form 36 (SF-36) [24] \| \| \| Pain \| \| One item from the Medical Outcomes Survey Short-Form 36 (SF-36) [24] \| \| \| Mental health \| \| WHO-5h Well-being Index [25] \| \| \| Perceived stress \| \| Perceived Stress Scale [26] \| \| \| Problems with diabetes \| \| PAID-5i [27] \| \| Behaviorf \| Behaviorf \| Behaviorf \| Behaviorf \| \| \| Alcohol consumption \| The average number of glasses per day and binge drinking \| Five questions regarding frequency and quantity measures of alcohol consumption [28] \| \| \| Cigarette smoking \| Number of cigarettes and craving \| A craving question based on the Fagerström Test for Nicotine Dependence [29] and a question on number of cigarettes [30] \| \| \| Eating pattern \| Fruit, vegetables, soda, fast-food, and snacks \| Questions based on Dutch dietary guidelines [31] and Dietary guidelines for people with Type 2 Diabetes [32] \| \| \| Physical activity \| \| SQUASH (Short Questionnaire to Assess Health-enhancing Physical Activity) [33] \| \| \| Sedentary behavior \| \| Questions based on the Marshall sitting questionnaire [34] \| \| \| Diabetes management \| Glucose monitoring and medication adherence \| Diabetes Self-Management Questionnaire [35] \| \| Environmentf \| Environmentf \| Environmentf \| Environmentf \| \| \| Family \| Worries about children and worries about relations \| Questions based on the DSMj IV [36] and the Dutch Self-Sufficiency Matrix [37] \| \| \| Loneliness \| \| Questions based on the DSM IV [36] and the Dutch Self-Sufficiency Matrix [37] \| \| \| Work \| \| Questions based on the DSM IV [36] and the Dutch Self-Sufficiency Matrix [37] \| \| \| Income \| \| Questions based on the DSM IV [36] and the Dutch Self-Sufficiency Matrix [37] \| \| \| Housing \| Neighborhood and house \| Questions based on the DSM IV [36] and the Dutch Self-Sufficiency Matrix [37] \| ## Cutoff Values and Decision Rules Decision rules had to be formulated based on cutoff points to identify high-, middle-, and low-ranking scores. We decided to visualize these scores based on a “traffic light model,” which communicated high (green)-, middle (orange)-, and low (red)-ranking scores, where middle- and low-ranking scores identified areas of improvement. For instance, psychological well-being was measured using the World Health Organization (WHO)-5 Well-being Index (WHO-5) [25]. The scores on the WHO-5 range from 0 (absence of well-being) to 100 (maximum well-being), and the cutoff score of ≤50 was used for the screening of depression. Thus, scores of ≤50 were “red,” and scores of >50 were “green.” In this case, no “orange” scores were obtained. Another example is blood pressure, which is shown in Table 2. The cutoff points for the separate elements of diastolic and systolic blood pressure were based on the WHO guidelines [38]. The decision rule for combining the separate elements in the aspect of “blood pressure” was developed by the researchers. Table 2 \| Separate elements \| Separate elements.1 \| Separate elements.2 \| Separate elements.3 \| Separate elements.4 \| Combined parameter \| Combined parameter.1 \| Combined parameter.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Systolic cutoff score \| Systolic decision rule \| Diastolic cutoff score \| Diastolic decision rule \| Systolic cutoff score \| Systolic cutoff score \| Diastolic cutoff score \| Blood pressure decision rule \| \| <140 \| Green \| <90 \| Green \| <140 \| <140 \| <90 \| Green \| \| 140 to <160 \| Orange \| 90 to 100 \| Orange \| <140 \| <140 \| 90 to 100 \| Orange \| \| ≥160 \| Red \| >100 \| Red \| <140 \| <140 \| >100 \| Red \| \| N/Aa \| \| \| \| 140 to <160 \| 140 to <160 \| <90 \| Orange \| \| \| \| \| \| 140 to <160 \| 140 to <160 \| 90 to <100 \| Orange \| \| \| \| \| \| 140 to <160 \| 140 to <160 \| ≥100 \| Red \| \| \| \| \| \| ≥160 \| ≥160 \| <90 \| Red \| \| \| \| \| \| ≥160 \| ≥160 \| 90 to <100 \| Red \| \| \| \| \| \| ≥160 \| ≥160 \| ≥100 \| Red \| ## Visualization of the Tool Next, a visual design was created for the profile wheel (Figure 1) to provide an overview of the scores per domain. We developed this visualization of the 360° diagnostic tool in cooperation with a data scientist expert on visualization. The interaction between the tool and the user involved clicking on the questionnaires, response categories, and icons in the profile wheel. Each domain (ie, body, thinking and feeling, behavior, and environment) was considered equally important. Therefore, the wheel was divided into 4 quadrants of equal sizes. In each quadrant, a maximum of 6 small icons were fitted, reflecting a particular parameter of each domain. For instance, a blood pressure monitor icon represented blood pressure, and the stress icon resembled the outline of a human head with lightning bolts. Depending on the diagnostic data, the icons in the profile wheel were red, orange, or green, reflecting a healthy status or room for improvement. If a parameter consisted of underlying elements, it was possible to click on that particular icon to reveal these elements. When clicking on a particular icon (Figure 2), more specific information was provided. For instance, for blood pressure, scores for systolic and diastolic blood pressure were also shown with traffic light colors indicating cutoff scores. The patient’s score was given as a black colored number, and a small black triangle indicated the corresponding position on the bar. Figure 1: The profile wheel version 1.0 resulting from the 360° diagnosis. Figure 2: A version 1.0 example of a click-through in the profile wheel for the parameter “blood pressure.” BP: blood pressure. ## Advice In the first exploration of the available interventions in the Netherlands, we listed 27 interventions. We classified the interventions into the following categories: We also classified the availability of the interventions into three categories: [1] proximity to the general practice, [2] general practice located within the municipality, and [3] nationwide and web based. The overview of interventions can be used to discuss options between the patient and health care provider. ## Consultation and Follow-up The protocol was drafted by 2 experts on MI based on a Dutch version that represented the MI steps in a set of playing cards [40]. The protocol was designed as a card deck (Figure 3). An expert on shared decision-making reviewed the protocol to check correspondence with shared decision-making principles. A health care professional also reviewed the protocol. The card deck consisted of the following 15 cards: title page, content, MI basic attitude, MI core skills 1, MI core skills 2, MI core skills 3, diabetes coaching steps overview, step 1 agenda, step 2 current situation I, step 2 current situation II, step 3 motivation I, step 3 motivation II, step 4 planning I, step 4 planning II, and step 5 rounding up. Each skill or step card described the goal, underlying parts, and examples (Figure 3). In step 5 “rounding up,” an intervention was chosen to improve one of the middle (orange)- or low (red)-ranking parameters. Furthermore, a summary was provided about the current situation, the goal (which parameter to improve), the intervention to reach that goal, and possible backup plans and resources to adhere to the intervention. Follow-up was designed in collaboration with behavioral change experts and health care professionals. Figure 4 shows the 360° diagnostic procedure advised to be followed in general practice. First, the assistant of the GP (eg, nurse practitioner) measures the patient’s clinical data in the body domain. Second, the patient completes the questionnaires for the other 3 domains. In the consultation, the diagnosis represented in the profile wheel is discussed using the protocol, and an intervention is chosen. After approximately 3 months, a second 360° diagnosis is performed by the GP’s assistant to check how the patient is doing and to discuss whether possible improvements have been made. The second 360° diagnosis is an important form of feedback for both the health care professional and the patient. Figure 3: Two example cards of the 360° diagnosis motivational interviewing card deck. Figure 4: The diagnostic procedure advised to be followed in the general practice for the 360° diagnostic tool. GP: general practitioner. ## Overview of the Participants In total, 5 males and 3 females participated in the usability study, and their ages ranged from 34 to 70 years (mean 54.25, SD 10.35 years). Five participants had lower education, 1 had middle education, 1 had higher education, and 1 participant’s educational background was unknown. Half of the participants were Dutch. The duration of T2D ranged from 2 weeks to 4 years, and for 1 participant, the duration was unknown. Five participants used medication for T2D, and for 2 participants, it was unknown. ## Evaluation of the Measurement Instruments of the Tool Half of the participants took approximately 12 minutes to answer all the questions included in the 360° diagnostic tool. One of the reasons that some participants took relatively more time to answer the questionnaires was that the system was too slow owing to bad internet connectivity. In general, the participants understood the questions in the questionnaire and were able to answer them easily. They did not perceive the questions to be difficult, strange, or unexpected. Of all the included questionnaires, the stress questionnaire took the most time to complete because stress was perceived as a more complex topic by participants and because it consisted of more questions. The first questionnaires in the 360° diagnostic tool were more generic, but the participants were originally expecting questions about diabetes. In addition, it is not always clear for participants whether questions (eg, regarding pain) were asked in general or in relation to T2D. In addition, explanations were missing regarding how certain parameters, such as loneliness, were related to diabetes. Therefore, we revised the structure of the questionnaires. We also added a brief introduction to the questionnaires so that participants would understand why we asked these questions about a certain parameter that was not obviously related to diabetes. In terms of response categories, participants did not perceive the organization of the response categories of some of the questions assessing the parameter “problems with diabetes” as logical. Therefore, they were adjusted such that these response categories ranged from “not a problem” to “a serious problem.” Moreover, some questions required additional response categories. For example, “no relationship,” “having a relationship, but not living together,” and “living together” were missing as response categories to the question about marital status. In addition, some questions needed to be adjusted to be applicable to people with T2D. For example, questions about diet were adjusted to fit the recommended dietary pattern. Furthermore, routing was lacking for some questions. For example, participants who indicated they did not drink alcohol still received a follow-up question on how many glasses of alcohol they consumed during the week or weekend, and they needed to fill in 0 glasses to be able to continue with the questionnaire. The number of questions was deemed to be adequate. Although some participants indicated that for some domains and parameters, more questions could have provided a more detailed view of their context or situation, for instance, parameters measured with only 1 question, such as loneliness or income. However, we decided to keep the questionnaires short and did not include additional questions. In addition, more information and nuances can be discussed in further detail during the consultation with the health care professional. In conclusion, positive feedback on the questionnaires affirmed our choice and the length of the questionnaires for the 360° diagnostic tool. The evaluations suggested improvements to the structure and introduction of the questionnaires and response categories for some questions (including routing). These points for improvement were easily adjusted and integrated into the 360° diagnostic tool. ## Evaluation of the Visualization and Feedback of the Tool In general, the participants perceived the profile wheel visualization, including the icons presented, as pleasant. The profile wheel did not contain much information and was perceived as useful. Participants indicated that it helped them keep track of their health and provided personalized results that could be discussed and analyzed with their health care provider. However, complex language and the use of technical terminology caused some problems in understanding the profile wheel. As a result, we changed the text accordingly. For example, “glucose” was replaced by “blood sugar.” The visualizations and colors in the profile wheel were perceived as clear and corresponded with the participant’s health status. Participants understood the traffic light colors (green, orange, and red) directly and had an overall impression of each domain. The participants reported some icons that were perceived as unclear and confusing. For instance, they did not recognize that the blood pressure icon represented a blood pressure monitor or that the glucose icon represented a finger prick. As a result, we redesigned these icons into a clearer icon of a blood pressure monitor and a glucose icon representing sugar (Figure 5). Clicking through icons was considered nice and informative. However, the participants expected that every icon would have a click-through, and this was not the case. Adjustments were made to the profile wheel so that it was possible to click on each icon. Furthermore, when clicking on the icon, it was not always clear what the depicted values meant and how to interpret the results. However, this needed to be optimized. Furthermore, for 1 participant, the profile wheel could not be shown because the questionnaires were not fully answered; however, it was resolved in a newer version. The profile wheel in the newer version shows gray icons if certain questions remain unanswered. The usability study also showed that programming (underlying scripts and algorithms) required further work. For example, the icon for cholesterol was red, whereas the underlying data did not imply red classification. In addition, the technical system (ie, the portal of the 360° diagnostic tool) did not always work as intended and was sometimes slow on the time scale of minutes. In conclusion, the visualization of the profile wheel (including click-through icons) was evaluated positively regarding relevance, ease of use, and usability for the 360° diagnostic tool. The evaluations showed some suggestions for improvements regarding simpler language (including avoiding technical terminologies) in the feedback, clearer and click-through icons, and debugging the underlying scripts and algorithms in the profile wheel. These points of improvement were incorporated into the updated version of the profile wheel. Figure 5: The adjusted profile wheel after evaluation of the usability study. ## Principal Findings The treatment of T2D could benefit from a more holistic diagnosis, which provides insights into the main underlying T2D-related issues for an individual, shared decision-making, and puts the patient first. Therefore, a 360° diagnostic tool has been developed for this purpose. A preliminary evaluation of our tool by a small number of experts and health care professionals showed that the tool was relevant, clear, and practical. These experts and health care professionals only commented on the tool and not on its implementation process. People with T2D perceived the tool, including the measurement instruments and visualization, as easy to use, useful, easy to understand, and insightful. In developing the 360° diagnostic tool, a systematic and iterative approach was followed, which has several advantages. First, a broad range of stakeholders was involved, including experts from multiple domains and health care providers. This broad consultation was combined with extensive desk research. This contributed to a comprehensive overview of the factors related to T2D and enabled their prioritization. Second, by using a systematic approach, the development process of the tool is transparent, and all content and design choices are well documented. Third, by adopting an iterative approach, areas of improvement could be identified early in the process. This iterative approach involved multiple consultations with experts in the field as well as presenting the tool to health care providers. The patients were included in the process during the usability study. The main points of improvement in the usability study included the logic of some of the answer options, amending explanation texts for both the questionnaires and the profile wheel, and adjusting some of the icons used in the profile wheel. In addition, certain technical parameters needed to be resolved. The iterative nature of the development process helped identify gaps and issues and provided an opportunity to resolve them before the tool was implemented in primary practice. For instance, as some icons were not clear to the patients, a newer version of the profile wheel was developed (Figure 5). Finally, involving health care providers and patients early in the process facilitated the future implementation of the tool. In the development process of the 360° diagnostic tool, choices were made regarding the selection of relevant domains and their underlying parameters and elements. For example, the Web Diagram by Huber et al [6], which can be considered a similar approach, also includes the “spiritual domain.” We decided not to include this domain, as we feel it is partly covered by the “mental health” domain, and is more fitting as part of MI in finding potential solutions during the consultation, instead of as a diagnostic criterion per se. In the selection of domains, the parameter and element focus was on the known changeable factors related to T2D. As such, the 360° diagnostic tool not only visualizes the burden of disease but also provides the opportunity for tailored and actionable recommendations via shared decision-making. In the usability test, no specific domains, parameters, or elements were deemed missing by people with T2D or health care professionals. In addition, the domains, parameters, and elements included in our tool largely overlap with the standard set of outcomes that reflect what matters most to patients as proposed by the International Consortium for Health Outcomes Measurement [41], except for specific diabetes-related complications and medication for comorbidities. Diabetes-related complications, however, were generically included in our tool by assessing compounding problems. Our tool is also unique in combining subjective (questionnaires) with objective (biomarkers, body composition, etc) measurements, whereas other tools such as the Self-Management Web by Beck et al [20], the Assessment of Burden of Chronic Conditions tool [19], and the Web Diagram by Huber et al [6] only include subjective measurements and are therefore more vulnerable to bias. In addition, the questionnaires included in the 360° diagnostic tool consisted mostly of well-established and validated questionnaires, which allowed for the benchmarking of individual scores to subgroup or population averages. What all tools have in common is the holistic view of health, their supporting function in the conversation between health care providers and patients, and their focus on shared decision-making. The importance of a comprehensive approach in treating chronic diseases such as T2D has also been recognized by the WHO [42], which stipulates that several factors influence treatment adherence and that these factors may interact with each other and, as such, affect both adherence and metabolic control. Another strength of our tool is that it is visual, which contributes to a better understanding, is more persuasive than text, and helps to reveal underlying patterns [43]. It is also more appropriate for people with low socioeconomic status (which is related to health literacy). The usability study included people with low socioeconomic status who reported that the tool was clear and easy to use. Fit for use by people with low socioeconomic status or health literacy is very relevant because chronic conditions such as T2D are more prevalent among them, and therefore, treatment adherence is a greater challenge [42]. ## Limitations First, a shortcoming of this study is that only 1 primary care practice was involved. The development process and usability study could have been improved by including health care professionals and patients from multiple health care practices to generate a more representative sample and increase the generalizability of the results. Second, the usability study only involved an evaluation of the questionnaires and the visualization via an interview. The protocol for health care providers to discuss the tool and the intervention toolbox needs to be evaluated in future research. In addition, further research is required to assess the feasibility and cost-effectiveness of implementing the tool in a real-world primary care setting. ## Implications for Future Use The 360° tool and intervention toolbox should be continuously updated with changing guidelines. We also aim to further develop the tool to increase its usability and value in primary care. First, the profile wheel currently shows values for a single assessment but monitoring changes over time for individual patients needs to be incorporated as a part of the 360° diagnostic procedure in practice (Figure 4). Monitoring the use of the profile wheel can be valuable for tracking progress and assessing treatment effectiveness in a more holistic way. Therefore, we aim to visualize its changes over time for individual patients. Second, the intervention overview has not yet been integrated into the digital 360° tool. We aim to include an intervention toolbox and algorithms linking the diagnosis to appropriate interventions within the tool used in this study to facilitate and support people with T2D to undertake action based on the diagnosis. However, offline interventions are often location specific, which demands situation-specific identification of fitting interventions. Future development of the intervention toolbox will include situation-specific decisions for the appropriate interventions to be added to the toolbox along with the domains and parameters they are expected to be effective for. These decision algorithms should be continuously updated based on the data collected after their implementation in primary care. Finally, the 360° tool has been developed specifically for people with T2D, but as also shown by Boudewijns et al [19], there is a large overlap in the factors influencing chronic diseases. Thus, after adaptation, our tool may be translated to other chronic diseases or potentially for preventive purposes. Therefore, we aim to adjust the backbone of the 360° tool in such a way that it becomes modular, such that new domains, parameters, or elements can be added or removed more easily to adapt the tool for other chronic diseases, such as cardiovascular risk management, or even completely new diagnostic purposes, such as implementation in pregnancy monitoring. If the tool is used for other purposes, it is still necessary to consult the relevant experts and perform a literature review to identify the missing parameters or elements. ## Conclusions The web-based 360° diagnostic tool is a decision support tool to identify the main underlying T2D-related issues for an individual, to determine the most suitable interventions by including a holistic diagnosis, and to facilitate shared decision-making between a person with T2D and the health care professionals in primary care. Preliminary evaluation of the 360° diagnostic tool by a small number of experts, health care professionals, and people with T2D showed that the tool is considered relevant, clear, and practical. The iterative development process provided insights into the areas of improvement, which were implemented later. In addition, the usability and value of the tool will benefit from further research (eg, feasibility, impact, and cost-effectiveness) and development (eg, monitoring and visualizing changes over time, integrating interventions as part of the tool, and adapting it for other chronic diseases and diagnostic or preventive purposes). Therefore, this tool may lead to a more personalized treatment strategy that may result in better health outcomes and quality of life. ## References 1. Diabetes. World Health Organization (2022.0) 2. Diabetes Atlas. International Diabetes federation (IDF) 3. Overbeek JA, van der Heijden AW, Herings RM, Nijpels G. 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--- title: 'Participatory System Dynamics Approach Targeting Childhood Health in a Small Danish Community (Children’s Cooperation Denmark): Protocol for a Feasibility Study Design' journal: JMIR Research Protocols year: 2023 pmcid: PMC10031447 doi: 10.2196/43949 license: CC BY 4.0 --- # Participatory System Dynamics Approach Targeting Childhood Health in a Small Danish Community (Children’s Cooperation Denmark): Protocol for a Feasibility Study Design ## Abstract ### Background Improving childhood health is complex due to the multifactorial nature and interaction of determinants. Complex problems call for complex intervention thinking, and simple one-size-fits-all solutions do not work to improve childhood health. Early awareness is important, as behavior in childhood often is manifested across adolescence and into adulthood. To facilitate shared understanding of the complex structures and relationships that determine children’s health behavior, participatory system approaches in, for example, local communities have shown promising potential. However, such approaches are not used systematically within public health in Denmark, and before being rolled out, they should be tested for their feasibility within this context. ### Objective This paper describes the study design for Children’s Cooperation Denmark (Child-COOP) feasibility study that is aiming to examine the feasibility and acceptability of the participatory system approach and the study procedures for a future scale-up controlled trial. ### Methods The feasibility study is designed as a process evaluation of the intervention with the use of both qualitative and quantitative methods. A local childhood health profile will provide data for childhood health issues, for example, daily physical activity behavior, sleep patterns, anthropometry, mental health, screen use, parental support, and leisure-time activities. Data at system level are collected to assess development in the community, for example, readiness to change, analysis of social networks with stakeholders, rippled effects mapping, and changes in system map. The setting is a small rural town in Denmark, Havndal, with children as the primary target group. Group model building, a participatory system dynamics method, will be used to engage the community, create consensus on the drivers of childhood health, identify local opportunities, and develop context-specific actions. ### Results The Child-COOP feasibility study will test the participatory system dynamics approach for intervention and evaluation design and survey objective measures of childhood health behavior and well-being among the ~100 children (6-13 years) attending the local primary school. Community-level data will also be collected. We will assess the contextual factors, implementation of interventions, and mechanisms of impact as part of a process evaluation. Data will be collected at baseline, at 2 years, and 4 years of follow-up. Ethical approval for this study was sought and granted from the Danish Scientific Ethical Committee [1-10-72-283-21]. ### Conclusions s: The potential of this participatory system dynamics approach includes opportunities for community engagement and local capacity building to improve children’s health and health behavior, and this feasibility study holds the potential to prepare an upscaling of the intervention for effectiveness testing. ### International Registered Report Identifier (IRRID) DERR1-$\frac{10.2196}{43949}$ ## Introduction Children’s health and well-being are of great concern worldwide [1], with decreasing levels of physical activity, more sedentary time, emerging rates of childhood obesity, and more children living with mental health problems [1-5]. Childhood health is closely related to the socioeconomic status of the family, for example, children raised in families with low socioeconomic status more often have mental health problems, obesity, and a sedentary behavior [2,3,6]. Several studies suggest that it is possible to improve childhood health despite inequalities in health [2,3,7-10], and early interventions have shown to be important in childhood health promotion [11]. Childhood health is a result of a complex interplay between many factors at individual, family, and society levels. Hence, declining childhood health must be viewed as a complex problem and calls for multilevel interventions and broader societal awareness [12]. Within childhood obesity, interventions building on community-based participatory research, and system dynamics have shown promising results in addressing complex health problems of children [13] by involving whole of communities in complex problem-solving at multiple levels of actions. Researchers have highlighted such participatory and system dynamic approaches as a potential way forward [14,15], addressing complex health problems in communities [13,16]. However, before applying a full-scale participatory system dynamics intervention in Denmark, feasibility testing is needed to underpin decisions about whether or not and how to progress [16]. The 2021 Medical Research Council (MRC) framework for complex interventions stresses the importance of considering the phases of intervention development in an iterative manner, from development, testing, and evaluating to implementation of interventions. Moreover, it identifies important core elements such as context, engage stakeholders, and development of a program theory [16]. Building on Australian experience with the participatory system dynamics approach [13] and the MRC framework as guidance [14], this paper describes the Children’s Cooperation Denmark (Child-COOP) feasibility study design that is aiming to examine the feasibility and acceptability of the participatory system dynamics approach [17] and the study procedures intended for a future larger scale-up controlled trial. ## Design The Child-COOP feasibility study will apply a participatory system dynamics approach, and the MRC framework [16] informs the design of the feasibility study and guidelines for applying feasibility studies [18]. As suggested by the MRC, feasibility studies should be designed to assess the intervention and evaluation design. ## The Community The Child-COOP feasibility study will be tested and evaluated in a small community in a rural town area (Havndal) with approximately 900 citizens, located in the northeastern part of a large Danish Municipality, Randers. Including the surrounding area, the local community consists of approximately 2000 citizens. The community holds an integrated school and kindergarten “The Child Village” with approximately 100 children in grades 0 to 6 and approximately 40 children in the kindergarten. A few shops such as a local fitness provider, leisure-time sport provider, and a relatively small business environment exist within the area. ## Populations The target population is primary school–aged children (grades 0 to 6, 6-13 years of age; $$n = 110$$), and all children attending the local primary school will be invited to participate in the health monitoring (described later in “Childhood Health: Outcome and Data Collection” section). Another target population is the key stakeholders of the local community, the local stakeholders, and municipality leaders and staff working within the community. These could include local politicians, grocery owner, chairperson of sports clubs, school board leader and members, local parents (with children at the school), leader of the municipality health department, and other municipality leaders, among others. The town of Havndal is one of the areas in Randers Municipality with the lowest social class and highest unemployment rate. ## Intervention The Child-COOP feasibility study will apply a participatory system dynamics approach and consists of several elements illustrated in Figure 1 and described in the following sections. Figure 1: Illustration of the participatory system dynamics approach including the group model building processes. CLD: Causal loop diagram. ## Monitoring A local childhood health profile will be generated at baseline, for example, daily physical activity behavior, sleep patterns, anthropometry, mental health, screen use, parental support, and leisure-time activities (Table 1). Table 1 \| Concept \| Outcomes of interest \| Instrument or measure \| Method \| Data collection \| \| --- \| --- \| --- \| --- \| --- \| \| Anthropometry \| HeightBMIFat massPrevalence of overweight \| Tanita Leicester transportable height measureInBody 230 multifrequency body composition (bioelectrical impedance analysis) \| Individual measurement \| Examination at baseline and after 2 and 4 years \| \| Physical activity and sedentary behavior \| Minutes per day spent on moderate and vigorous physical activity and sedentary activityMinutes per day spent onStep counts \| Accelerometer (Axivity AX3) \| Individual measurement \| 7 days at baseline and after 2 and 4 years \| \| Physical literacy \| Motivation and confidencePhysical competenceDaily behaviorKnowledge and understanding \| A Danish version of the Canadian Assessment of Physical literacy \| Individual measurement of physical competence (including motor skill test, aerobic capacity run, and torso strength test) and daily behavior (self-report weekly participation in moderate- to vigorous-intensity physical activity and objectively measured average step count for 1 week). Survey of motivation or confidence and knowledge or understanding. \| Baseline and after 2 and 4 years \| \| Mental health \| General well-beingSocial relations—peersLonelinessBullyingStress \| Børnungeliv.dk \| Survey \| Baseline and after 2 and 4 years \| \| Sleep \| Hours of sleepTime of sleep week days or weekendsQuality of sleepUse of electronic devices \| Børnugeliv.dk and accelerometer (Axivity AX3) \| Survey and individual measurement \| Baseline and after 2 and 4 years \| \| Leisure time \| Participation in sport and other activitiesUse of computer, tablet, and other electronic devices \| Børnungeliv.dk \| Survey \| Baseline and after 2 and 4 years \| \| Body and movement \| Active transport to schoolTime spent on physical activity in schoolFrequencies of high-intensity activityPhysical fitness levelFacilities for sports and play in the neighborhoodBody satisfaction \| Børnungeliv.dk \| Survey \| Baseline and after 2 and 4 years \| \| Food and meals \| Breakfast, lunch, and dinnerCooking at homeIntake of fruit and vegetable, sugar-sweetened beverage, candy, and cake \| Børnungeliv.dk \| Survey \| Baseline and after 2 and 4 years \| \| Family relations \| SiblingsCountry of origin (child and parents)Relation to parentsPlace of residence \| Børnungeliv.dk \| Survey \| Baseline and after 2 and 4 years \| ## Recruitment of Key Leaders and Local Stakeholders A coordinator from Randers Municipality together with the research team identify and recruit key leaders (eg, local politicians, department heads, and municipality leaders) and local stakeholders (eg, school board members, school principal, school nurse, and sport club representatives). Key leaders and local stakeholders will be selected based on the authority and capacity to initiate actions that are likely to influence the children’s health behavior across sectors and organizations. During this stage, key leaders must commit to allocating resources to ensure subsequent implementation. The aim is to recruit 12-15 key leaders and local stakeholders from the community. ## Group Model Building Process and System Mapping A total of 3 group model building (GMB) workshops (WS1, WS2, and WS3) will be held during the 3 stages of the process (see Figure 1). The GMB method is an evidence-based method for solving complex challenges [13]. During WS1, the local childhood health profile will be presented, and based on this information, key stakeholders will discuss what health-related topic they find most important to their community (eg, obesity, physical activity, and mental health). In WS1 and WS2, the key leaders and stakeholders will map the system by creating a causal loop diagram (CLD) to understand how the perceived local system affects the prioritized childhood health topic in their community. For developing the CLD, the web-based software tool Systems Thinking in Community Knowledge Exchange (STICKE; version 3.0.14; Deakin University) was used [13]. STICKE stands for Systems Thinking in Community Knowledge Exchange and is developed to facilitate community knowledge exchange to foster shared understanding of complex problems. In WS3, all community members willing to engage in changing the local system will be invited to identify priority areas for action based on the developed CLD from WS1 and WS2. ## Actions and Support The output of WS3 is the formation of local working groups that will focus on implementing the chosen actions using cocreation ideals [13]. The working groups will be supported and supervised by a backbone office formed by the Child-COOP municipality coordinator and the research team. A follow-up workshop will be held with the key stakeholders 6 months after the completion of WS3 to review the consolidated priority actions. To increase and maintain motivation and actions in the local community, subsequent follow-up meetings will be held with the working groups when needed after WS3. ## Assessment of the Evaluation Design The feasibility study of the evaluation design for Child-COOP will involve multiple levels and perspectives with a focus on key aspects such as recruitment, data collection tools and processes, analysis, and unintended outcomes [16]. In Child-COOP, the evaluation design will include both quantitative and qualitative methods. The evaluation design includes [1] evaluation of childhood health and [2] evaluation of community readiness to change, social network, actions implemented, and system changes. Child-COOP will be considered feasible if it meets the following criteria: [1] The GMB sessions were conducted as planned. [ 2] Working groups are formed and active in implementing interventions arising from GMB. [ 3] Outcome measures were collected and considered complete (>$75\%$ considered acceptable at baseline). [ 4] Response rate considered acceptable (>$75\%$ considered acceptable at baseline). ## Overview All measurement instruments are listed in Table 1 and briefly explained. The measures will be conducted over 2 test days at the local school to assess the health of the children and test the data collection procedure. On day 1, children in grades 4, 5, and 6 are invited to participate, and on day 2, those in grades 0, 1, 2, and 3 are invited to participate. All children available on the day of data collection, whose parents have given written consent, will be included. A team of trained data collectors will collect the data on the test days with assistance from the class teachers. The longitudinal design makes it possible to study the changes over time within the local community, with limited effectiveness testing due to the limited power, as this is a feasibility study. ## Anthropometry All included children will have height, weight, and fat mass measured, wearing light clothes and barefoot, by the school nurse (Table 1). ## Physical Activity and Sedentary Behavior Physical activity and sedentary behavior will be measured continuously over a 7-day and -night period using Axivity AX3 accelerometers (Table 1). The accelerometer will be attached to the skin on the medial front of the right thigh using skin tape. The procedure is described in detail elsewhere [19]. The accelerometer is water-resistant, and the skin tape is intended to be water-resistant; thus, the accelerometer can be worn when swimming and bathing. The OMGui software (version 1.0.0.43; GitHub Inc), which is available on the internet, will be used for instrument initialization and data download [20]. ## Physical Literacy The Danish version (DAPL) of the Canadian Assessment of Physical Literacy [21] will be used to measure physical literacy for children in grades 1 to 6 [22]. Grade 0 children will not be included in the assessment tool and have neither been used nor validated, as we consider these children too young for the assessment [22]. ## Self-reported Health and Well-being The children will fill a questionnaire at school with assistance from their teacher and trained data collectors, more questions for children in grades 4 to 6 and less for children in grades 0 to 3. The parents will also fill a questionnaire about their child’s health and well-being with more questions for children in grades 0-3 and less for children in grades 4-6. We will use a Danish-validated questionnaire instrument (Danish: BørnUngeLiv, English: ChildYouthLife) developed by researchers and practitioners from the municipalities to use in the municipalities of Denmark to assess the health and well-being of children [23]. Topics that will be covered in the questionnaires are mental health, sleep, leisure-time activities, body and movement, food and meals, and family relations (see Table 1). ## System Level: Outcomes and Data Collection Data at system level will be collected continuously from baseline and with 2- and 4-year follow-up. All outcomes and measurement instruments are listed in Table 2 and briefly explained. Table 2 \| Item \| Outcomes of interest \| Instrument or measure \| Method \| Data collection \| \| --- \| --- \| --- \| --- \| --- \| \| Readiness to change \| Baseline and change in:Community knowledge about child obesityExisting community effortsCommunity knowledge of the effortsLeadershipCommunity attitudesResources related to child obesity \| The community readiness model \| One-to-one interviews \| 12 interviews of 45-75 minutesCollection before WS1a1-year follow-up \| \| Social network \| Strength and importance of relationshipsDensityCentrality“Opinion leader”And change over time \| Social network dynamics \| SurveySemistructured interviews \| Survey delivered to stakeholdersCollection before WS11-year follow-up2-year follow-up \| \| Actions implemented \| Number of actions implementedPlace of influence within the CLDbStakeholders \| REMcAdd action variables within system dynamics software (STICKEd) \| Ongoing communication with working groups after WS3eSemistructured interviews with working group leaders \| Using REM as a method to track actions developed1-year follow-up2-year follow-up4-year follow-up \| \| Change in system maps \| CLD \| CLD using STICKE \| Monitoring CLDFocus groupsAdjustment of CLD \| CLD at WS3Collection of focus groups at2-year follow-up4-year follow-upBased on focus groups \| ## Readiness to Change To assess whether changes occur in the community’s readiness and capacity for making system changes, we use the community readiness model [24] forward-and-back translated to Danish [25]. Community capacity and readiness here refer to a “community’s ability to identify, mobilize and address public health problems” [26]. Responses will be used to measure the changes in community capacity by scoring the descriptive responses on anchored scales [24,25]. Data will be collected from key leaders in the community including school principal and staff, local government leaders, parent representatives, among others. It is hypothesized that Child-COOP will change the readiness to change in the local community. ## Social Network Analysis Social network analysis will be used to assess the development of relationships between people and organizations [27]. Social network analysis can calculate network statistics including density and centrality and “opinion leader” positions [13]. Furthermore, social network dynamics will be applied with longitudinal network models in mind to determine how the networks change over time and the role of the network in the diffusion information, knowledge, and practice [28]. Data will be collected using the COMPACT Stakeholder-driven Community Diffusion Survey [29] adapted to a Danish context. It is hypothesized that Child-COOP will change and connect more people within the community’s social networks. ## Actions Implemented Proxy indicators of system change will be actions initiated by the community, and these will be monitored from baseline and followed up annually using the STICKE software [30]. The number of community actions will be tracked as proxies of community-level engagement [13]. Semistructured interviews with working group leaders will also be used to understand and provide deeper information on the actions implemented (eg, goal, content, and setting) [31]. To track the actions developed and implemented in the local community, we will use “ripple effect mapping” (REM) [32]. REM is a method used to better understand the complex, dynamic nature, and wider impacts of system dynamics intervention [33]. Unlike traditional evaluation designs, REM is aimed at understanding contribution; how may an intervention, action, or policy contribute toward changing a larger system? [ 32-34] During the workshops, participants (stakeholders, citizens, municipality employees, etc) visualize the impacts of the actions and how these impacts may go beyond those, which Child-COOP was designed to achieve [32]. ## Change in System Map Tracking of change in the CLD and the communities’ responses to the participatory system dynamics approach will be monitored through a revised system map at 2 and 4 years post the initial CLD [13]. The STICKE software [30] will be used to track changes in the system and the interaction among key actors, actions, and the system. ## Assessment of the Intervention: Process Evaluation The process evaluation aims to understand the functioning of an intervention by examining mechanisms of impact, implementation, acceptability, and context. Through analyses, crosscutting qualitative and quantitative results on the implementation (adherence, dose, quality of delivery, participant responsiveness, and reach), mechanism of impact, acceptability, and context will be synthesized to gain knowledge on how the participatory system dynamics approach was delivered and implemented and inform a final program theory. ## Mechanism of Impact What are the change mechanisms for Child-COOP and how do they align with program theory? These questions will be addressed through 12 semistructured interviews with key stakeholders and the municipal coordinator, addressing the research questions: What are the mechanisms of change associated with the participatory system dynamics approach? What hinders or enables implementation of the participatory system dynamics approach? Our program theory is a tentative developed program theory for the overall study (see Figure 2), while the developed CLD will be considered a form of logic model [16]. Figure 2: Child-COOP tentative program theory for feasibility study in Havndal, Denmark. Child-COOP: Children’s Cooperation Denmark. ## Implementation What is delivered in Child-COOP? Here, we aim to understand how the “implementation” is done locally in the community assessing [1] fidelity (is the participatory system dynamics approach delivered as intended?); [ 2] dose (who is reached by the participatory system dynamics approach and to what strength?); [ 3] adaptions (have any significant changes to the participatory system dynamics approach been made?); [ 4] reach (how many are part or affected by the participatory system dynamics approach?); and [5] process (understanding the implementation process?). These elements are sought to understand through structured observations by registration and participation (number of participating stakeholders or established working groups or initiated actions) and a questionnaire for stakeholders. ## Acceptability and Context How is “acceptability” of the Child-COOP approach among the participants? Through 12 semistructured interviews with key stakeholders and participants, we will collect data aiming to understand the following: How do participants involved in workshops and implementation of actions react to and engage in the Child-COOP approach? Furthermore, we aim to conduct follow-up phone interviews with dropout key stakeholders to provide a realistic perspective. ## Ethics Approval This study will be carried out in accordance with the Declaration of Helsinki. Ethical approval for the study has been granted by The Regional Scientific Ethics Committee of the Central Denmark Region, Danish National Committee on Health Research Ethics [1-10-72-283-21]. Any protocol amendments will be reported and submitted to the Ethics Committee. Anonymity and confidentiality of participants will be ensured by assigning a study ID number to all participants. Informed consent will be obtained from parents in order to include the children in the health assessments. No results of the anthropometric measurements will be visible to, or available for, the children to reduce risk of the bullying and stigmatizing. ## Consent for Publication Informed consent will be collected from parents on behalf of the children taking part in the study. Informed consent will be collected from all participants in the workshops. ## Results The Child-COOP feasibility study has officially started with baseline measures in September 2021, recruitment of participants and delivery of the GMB phase started in 2021-2022. The 2-year follow-up will be the next step and will reveal the first results of the study—this is expected in 2023. Furthermore, process evaluation is underway and will be concluded at the 2-year follow-up in 2023. The project will conclude with the 4-year follow-up, which will provide the final results—these are expected in 2025. In September 2022, a large grant was given for the Child-COOP Denmark project, focusing on children’s physical activity behavior. This project will include 5 municipalities and 10 communities, building on the experiences of the Child-COOP feasibility study. It will begin immediately after the conclusion of the process evaluation of the Child-COOP feasibility study in late 2023 or early 2024. ## Expected Findings The Child-COOP feasibility study will provide new knowledge on the potential to implement a participatory system dynamics approach targeting childhood health in a Danish context. Childhood health is a complex health problem and evidence points to system science as one of the best means of identifying and addressing such complex and dynamic problems [13]. The approach will be tested out in a small disadvantaged community in Denmark, and therefore, it may be applicable in similar settings upon a positive feasibility assessment. ## Strengths of the Study Design Interventions building on a participatory system dynamics approach as Child-COOP have already been suggested as a feasible way to address complex problems, as they combine the current evidence base on prevention, best practice, and local wisdom to achieve new knowledge and create solutions [16,34]. The participatory system dynamics approach in Child-COOP based on the co-creation and close collaboration between the municipality, community, and facilitating research team will most certain be a strength of the Child-COOP feasibility study. In addition, this study builds upon existing evidence and shows a positive effect of a participatory system dynamics approach in improving the childhood health and obesity rates [13]. Furthermore, to investigate the complex relationship of childhood health drivers in the community, the active involvement of the local community in mapping, developing action plans, and maintaining the efforts may provide sustainability and empower the local community [13]. The extensive evaluation design using multiple methods for data collection at the individual level as well as the system level also serve as a strength of this study. ## Potential Challenges Potential challenges of the study include the long-term engagement of the local community, which may decline over time; however, this feasibility study will provide insight into the magnitude of the support from the research team, local leaders, or coordinators to develop, facilitate, and support the working groups to continue to implement and adjust actions in their local community. Moreover, a potential challenge is that the community will not take the chance to change and influence the local system affecting the health and well-being of the children. In addition, external factors may challenge the implementation of the intervention, for instance, the COVID-19 pandemic or local and municipality economic challenges. However, the participatory system dynamics approach is flexible and can be adapted to be conducted largely on the internet while still representing the local context. Finally, we acknowledge that a whole of system approach for evaluation may never be fully achievable, as changes in a complex system will always give rise to more uncertainties than a single evaluation can satisfactorily capture [34,35]. However, with our feasibility assessment, we focus on the most important areas of uncertainty and to justify decisions of which assessment of impact can be expected to be meaningful at a system level. ## Conclusions In conclusion, the Child-COOP feasibility study is crucial in evaluating the potential for a full scale-up study in a Danish context. If feasible, this participatory system dynamics approach in Child-COOP provides opportunities for the application of local capacity building by applying a practical approach to complex health problems in a local community [34]. This study will be able to inform both the content of the participatory system dynamics approach and the future larger-scale evaluation design. Furthermore, the community outcomes included may help to better understand the changes in the community and the mechanisms leading to changes in childhood health within communities [13]. 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--- title: 'A Gamified Real-time Video Observed Therapies (GRVOTS) Mobile App via the Modified Nominal Group Technique: Development and Validation Study' journal: JMIR Serious Games year: 2023 pmcid: PMC10031451 doi: 10.2196/43047 license: CC BY 4.0 --- # A Gamified Real-time Video Observed Therapies (GRVOTS) Mobile App via the Modified Nominal Group Technique: Development and Validation Study ## Abstract ### Background The success rate of tuberculosis (TB) treatment in Malaysia remains below the recommended World Health Organization target of $90\%$ despite the implementation of directly observed therapy, short-course, a physical drug monitoring system, since 1994. With increasing numbers of patients with TB in Malaysia defaulting on treatment, exploring another method to improve TB treatment adherence is vital. The use of gamification and real-time elements via video-observed therapies in mobile apps is one such method expected to induce motivation toward TB treatment adherence. ### Objective This study aimed to document the process of designing, developing, and validating the gamification, motivation, and real-time elements in the Gamified Real-time Video Observed Therapies (GRVOTS) mobile app. ### Methods The modified nominal group technique via a panel of 11 experts was used to validate the presence of the gamification and motivation elements inside the app, which were assessed based on the percentage of agreement among the experts. ### Results The GRVOTS mobile app, which can be used by patients, supervisors, and administrators, was successfully developed. For validation purposes, the gamification and motivation features of the app were validated as they achieved a total mean percentage of agreement of $97.95\%$ (SD $2.51\%$), which was significantly higher than the minimum agreement score of $70\%$ ($P \leq .001$). Further, each component of gamification, motivation, and technology was also rated at $70\%$ or more. Among the gamification elements, fun received the lowest scores, possibly because the nature of serious games does not prioritize the fun element and because the perception of fun varies by personality. The least popular element in motivation was relatedness, as stigma and discrimination hinder interaction features, such as leaderboards and chats, in the mobile app. ### Conclusions It has been validated that the GRVOTS mobile app contains gamification and motivation elements, which are intended to encourage medication adherence to TB treatment. ## Introduction More than 10 million cases of tuberculosis (TB) are reported each year globally [1]. In Malaysia, from 2010 to 2015, TB cases increased from 68.4 to 79.6 cases per 100,000 people [2]. In addition, according to a model projection, the observed and projected TB incidence in Malaysia will reach 300,000 cases in 2030 [3]. In Malaysia, directly observed therapy, short-course (DOTS) is a method to ensure medication compliance by having a trained health care worker or other designated individual provide the prescribed TB drugs and watch the patient take every dose. DOTS, which was implemented in 1994, has resulted in a treatment success rate from $76\%$ in 2013 to $81\%$ in 2017 [2]. However, this rate has remained below the recommended World Health Organization target of $90\%$. In addition, there is an increase in the prevalence of TB treatment default in Malaysia, which has ranged from $4\%$ in 2010 to $4.8\%$ in 2015 and to $5.6\%$ in the latest study [4]. The increasing number of TB cases indicates that there are still issues and challenges that need to be addressed at all levels. Specifically, the problems currently facing DOTS can be categorized according to the 3 main stakeholders. For patients with TB, the compulsory need for daily DOTS monitoring results in stigma by the public and absence from home or work responsibilities [5-7]. Health workers and policy makers are hesitant to fully implement DOTS, as there are inadequate human resources, increased TB management costs, less participation from lower management, and a lack of public awareness [8,9]. To address these challenges, the World Health Organization recommends the use of digital technologies to promote TB medication adherence [10]. Video directly observed therapy (VDOT) was introduced to replace physical DOTS and has proven to significantly reduce the cost of managing TB, improve patients’ access to doctors, and be less disruptive to patients’ work and family life [8,11-13]. According to many studies, VDOT is a more cost-effective method that significantly increases patient treatment adherence compared to conventional DOTS [14-18]. Although data on VDOT are becoming increasingly robust, the system has yet to be rigorously evaluated within low- and middle-income countries, especially regarding its feasibility [19]. This is quite worrying, as the implementation of VDOT requires complete access to hardware and internet connectivity, which some countries cannot afford [10]. However, in Malaysia, the number of smartphone users is growing and expected to reach over 33 million by 2024, with an $87.36\%$ smartphone penetration, which suggests that VDOT can be implemented in Malaysia [20]. Making VDOT available via mobile apps could make drug monitoring more convenient and effective [21]. However, with the large numbers of mobile health apps in existence, the problem is often about the sustainability of their use [22]. As a solution, integrating gamification elements inside mobile apps can positively impact health and well-being, improve health behaviors and patient engagement, decrease health care use, and empower patients to self-manage their disease [23,24]. In addition, the use of real-time elements such as virtual reality and augmented reality has proven to increase learning effectiveness and behavior modification, correct medication identification, correct self-administration of medication, and support patient counseling practices [25-27]. Thus, in our Gamified Real-time Video Observed Therapy (GRVOTS) mobile app, the integration of gamification and real-time elements is expected to increase patient motivation. The purpose of this study was to validate the gamification, motivation, and real-time element in GRVOTS, a mobile app for VDOT, from the perspective of the service provider (expert review). ## Methods The mobile app prototype was developed from February 2021 to May 2022. The developmental process as a whole, including content and prototype development as well as content validation (nominal group technique [NGT]), was performed using the design science research process model [28]. ## Development Content development of the prototype involved a few stages of literature review, mapping, and justification of the new framework. From the literature review, we identified 3 frameworks that could be used as features of app: gamification framework, video reality and motivation framework, and technology feature framework. The gamification component, as the foundation of the proposed gamified mobile app GRVOTS, will be based on the validated framework for the gamification of diabetes self-management called The Wheel of Sukr [29]. The framework consists of 8 components: fun, esteem, growth, motivation, sustainability, socializing, self-representation, and self-management. For the video reality and motivation framework, we used the attention, relevance, confidence, and satisfaction (ARCS) model of motivational design [30]. In terms of the mapping procedure, the ARCS model of motivational design can be combined with the gamification elements to foster motivation [31]. The dynamic nature of gamification, such as self-management, self-representation, and fun, can be equal to satisfaction in the ARCS model. Further, elements of gamification, such as esteem, reward, growth, and socializing, can be equal to the components of confidence in the same model. Subsequently, these 2 frameworks can be integrated where gamification elements are added to the categories of confidence and satisfaction that are based on the ARCS model. Table 1 shows the categories and subcategories of the proposed model in matrix form. Game dynamics can improve user desire and motivation by establishing rules that encourage users to explore and learn about the apps [32]. Figure 1 shows a screenshot of the main function of the GRVOTS mobile app and its relation to our intended gamification, motivation, and real-time elements from the framework. The GRVOTS mobile app is designed for 3 users—patients, supervisors, and administrators—where they interact with one another via the internet. All data inputted by the patients will be automatically collected by the server and viewed by the specific supervisor (health care worker) and TB management team to help them with clinical interventions. The proposed model presented in Figure 2 is based on the development of mobile apps for smartphones only. ## Content Validation via the NGT (Expert Review) The NGT is a structured variation of a small-group discussion to reach consensus. Through the agreement of the description of the elements, the NGT was used in this research as a validation tool to evaluate the presence of the gamification and motivation components intended to be used in the app. ## Sample Size The NGT is a small-group technique suited to panel sizes of more than 10 people [33,34]. Therefore, there were 11 experts involved in this NGT session. A panel of experts was involved to validate the gamification elements in the GRVOTS app using physical meetings in 3 different settings. ## Study Population Flow This study was conducted iteratively in 3 meetings for a more comprehensive evaluation, as illustrated in Figure 3. The criteria of the experts involved in the group were different according to each group. For the first group, IT experts were experienced and involved in mobile app development for at least 2 years and well versed in the gamification features of mobile apps. The second and third groups were composed of administrative and health care workers who were directly involved in managing patients with TB in the outpatient environment, respectively. Figure 3: Flow of the modified nominal group technique (NGT) meetings. ## Study Settings The 3 NGT meetings were performed in 3 settings: at the university for the meeting with the IT expert panel, at the district health office for the meeting session with the administrative team, and at the TB clinic for the meeting with clinical experts. ## Instrument for NGT The instrument used in the NGT was a questionnaire, and the items were generated from a literature review based on 3 existing models as per the previous mapping. These models were developed into a panel expert checklist, and the questionnaire had 2 parts: Part A asked about the gamification-motivation–rea-time theory, The Wheel of Sukr [35], and the ARCS model, whereas Part B concerned the technology features by Anderson et al [36]. ## Implementation of NGT The implementation of NGT involved experts who were selected according to the scope of the study. The workshop was conducted in a face-to-face meeting by a moderator [34]. The NGT workshop lasted approximately 2 hours. Before the workshop, the experts were given a week to use the app. Some of them experienced the task as a supervisor and some as a patient. They were required to send 3-4 VDOT videos. Table 2 shows the basic steps to carry out the NGT process, and Table 3 shows the 5 steps of data analysis for the NGT. The data analysis process for the NGT was based on the percentage of agreement where an element is accepted when the percentage of agreement is $70\%$ or more [37]. The 1-sample, 2-tailed t test was used to determine whether the mean (SD) percentage of agreement result on the gamification and motivation elements in this app was significantly higher than the percentage of agreement of $70\%$, with a level of significance (α error) less than.05. The software used at this stage was SPSS (version 28.0; IBM Corp) and Microsoft Excel. ## Ethics Approval Ethical approval was obtained from the Universiti Teknologi MARA Ethical Board and Medical Research and Ethics Committee, Malaysia: NMRR-21-1016-58994 (IIR). No informed consent was taken in this study as the data were only retrieved retrospectively from the database and no identifiers were collected for this study. ## Results Mobile app development with the integration of gamification and real-time elements was performed via a literature review, and the content validation of the component was performed via the modified NGT. ## Prototype The final beta version of the mobile app used Android as the platform. The prototype was designed to be used by 3 users—patients, supervisors, and TB managers—and each user had a different role in the program. This mobile app’s main function is to provide patients with a way to record daily DOTS intake via video (VDOT) as well as medication side effect reporting. After patients log in, they are directed to set up their personal profiles and learn how to use the VDOT medication reminder, which is the main activity. After each VDOT session is uploaded, it is followed by pop-up motivational quotes as well as the movement of the progress meter indicator. The accumulated points collected from the progress meter will be translated into badges in the progress report theme. Subsequently, a daily pop-up message will also be the main reminder for the next medication, and patient will be asked if they noticed any adverse reactions to previous medication, with a selection of options concerning their symptoms. The ability to report adverse effects gives patients access to their own medication diaries, which can be reviewed during medical visits. Throughout the TB treatment journey, the progress report theme will help patients track their journey and redeem the internal and external rewards offered. Every VDOT report and any side effects noted by patients will be verified by the supervisor as a feedback interaction. This feature enables supervisors to regularly check and understand the patients’ progress as well. In addition, patients can always go to other main theme of “knowledge” to continuously learn more about TB treatment and nutrition. The app provides different users with access to different functions and main menus. In the patients’ main menu, there are 4 main themes for VDOT. Information regarding the app, progress report, and side effect reporting is shown in Figure 4. For the supervisors’ main menu, there are 2 menus, one to validate the VDOT report and another to validate the side effects, as shown in Figure 5. Figure 4: Screenshots of the GRVOTS app for patients. GRVOTS: Gamified Real-time Video-Observed Therapies. Figure 5: Screenshots of the GRVOTS app for supervisors to monitor patients. GRVOTS: Gamified Real-time Video-Observed Therapies. ## NGT Output for Content Validation A panel of 11 experts were involved in the modified NGT, including 2 gamification experts, 2 public health experts, 1 chest physician, 2 medical officers, 3 medical assistants, and 1 staff nurse. They were asked to rank the app’s elements based on the expert panel checklist. Tables 4-6 show the results and rankings of gamification, motivation, and technology feature elements via the NGT. The total percentage of agreement among experts was $97.95\%$ (SD $2.51\%$), which was significantly higher than the minimum of $70\%$, with a difference of $27.947\%$ ($95\%$ CI $26.74\%$-$29.15\%$; $P \leq .001$). The t test confirmed the validity of all gamification and motivation components in the app. ## Discussion This comprehensive GRVOTS mobile app was developed based on the integration of gamification and real-time motivational elements of autonomy, competence, relatedness, attention, relevance, self-control, confidence, and satisfaction. These components were successfully validated and significant. ## Mobile App Developed Our GRVOTS mobile app was able to motivate patients toward medication adherence using the ARCS model of motivation, which was translated via its gamification and motivation features. First, the interactive VDOT feature can stimulate the patient’s feelings of attention and arousal. Second, by linking the previous experience of medication intake physically with the experience of using GRVOTS, the element of “relevance” can also be instilled in the patient. When patients perceive a high sense of relatedness, they are more likely to exhibit higher engagement with a program [38]. By providing goals along the journey toward medication adherence, the app can instill confidence and self-belief, especially when the patients can digitally see their previous VDOT report and a progress meter of treatment success. As they use GRVOTS to monitor their medication intake, patients experience more freedom of choice and self-control, and the results of treatment adherence will subsequently reinforce the app’s value. The last element of “satisfaction” can also be realized when patients comply with the use of GRVOTS and receive rewards in the form of badges and progress meters. ## Content Validation via the Modified NGT The results of the modified NGT showed that all the components obtained from the literature and related model were validated by the experts during the NGT session, as shown in Tables 4-6. Based on the results, the elements were prioritized based on the percentage of acceptance. Considering these rankings, some of the GRVOTS functions were improved to provide a better mobile app for the next pilot study. According to the results for the gamification elements, the growth element received the highest scores, whereas fun received the lowest scores. By definition, “fun” aspects within the gamification elements in this GRVOTS app are exemplified by a progress bar, a progress meter, inspiration quotes, and badge rewards. The fun element was voted as a less visible element in this app, perhaps due to many factors, such as the perception of fun, which varies for different people, as well as the nature of serious games, which does not prioritize the fun element. A number of studies have described that the perception of gamification features differs according to the gamer type, gender, and personality of the player. For instance, extroverts like rewards and leaderboards, which appear to be more entertaining, but introverts prefer badges and feedback [39-41]. Health apps are also considered serious games that are played for purposes other than pure entertainment [42]. In this case, having a fun component is not a priority. According to a study regarding serious games, other elements, such as explicit learning tasks, instruction, and support built into the game or added by teachers, may be more important than having fun while playing [43]. For the motivation elements of the ARCS model, the highest ranked element was confidence and the lowest was relevance. It is suggested that when patients perceive a high sense of relatedness, they are more likely to exhibit higher engagement with the program [38]. In mobile apps, relatedness is seen as a feature of leaderboards or chats that encourages engagement and collaboration to achieve a particular objective [41]. However, in this GRVOTS mobile app, the relevance element is evidenced by allowing daily communication between patients and supervisors via the VDOT and adverse event reporting components without the leaderboard or chat features. This is because most of our patients with TB refused to disclose their condition and communicate with other patients due to the stigma associated with the disease. For the technology feature elements, the highest ranked was ease of use and the lowest was real-time features. There are 2 type of VDOT: live VDOT, also known as synchronous VDOT, in which patients and providers interact in real time [12,44]; as well as asynchronous technologies that record, upload, and digitally store videos for future review [11,19,45]. Synchronous VDOT has the advantage of human interaction but is not a feasible option, as patients and workers need to find time to meet. Asynchronous VDOT is more flexible but can be manipulated easily by sending the same recorded video. Thus, apps with real-time features ensure the originality of the video and simultaneously generate a greater degree of user engagement [25-27]. Although the real-time feature in our app provides details concerning the time and date of the video, feedback from the experts indicated that we should time stamp the videos so that they can be identified more easily later. Since users found it challenging to determine if their VDOT session was successfully uploaded or not, the addition of an upload bar was also recommended. ## Strengths and Limitations The GRVOTS mobile app can benefit users in many ways. For patients with TB, the app can help patients gain self-control, boost their self-esteem, and motivate them to take their medications. For health care workers, this app made it easier for TB system management to detect DOTS defaulters and manage them accordingly. As a portable device, mobile apps enable monitoring that can be done anywhere, saving money and time and boosting patient engagement with the DOTS program. In summary, this app can also initiate a patient self-care system and reduce dependency on health care providers such as doctors and nurses. The limitation of this study is that the mobile app prototype is only being developed for the Android platform because of time and logistics. The app does not have a virtual reality feature, and improvements are needed in the future. This study conducted the validation of the gamification and motivation elements only from the perspective of the experts and not from the perspective of patients, which may limit the review’s validity; an analysis regarding its usability among patients will be conducted in the future. ## Recommendation In the future, GRVOTS should also be available on other platforms, especially the iPhone Operating System. The language options should also include English, Chinese, and Tamil, as these languages are frequently used in multiethnic communities in Malaysia. The use of various languages will expand the benefits to more users. This will enhance knowledge transfer and improve users’ understanding. Next, a usability study to access the user experience will be conducted, followed by an effectiveness study via a single-arm intervention study, in which patients will use the app for DOTS during intensive phase up to 2 months monitoring, followed by an assessment at 3 time intervals to evaluate their medication adherence, motivation, and the usability of the app. ## Conclusion More comprehensive and efficient TB system management via VDOT mobile app monitoring is a way to improve patient treatment adherence. According to the literature review, gamification elements can motivate patients; thus, by integrating the uniqueness of gamification and motivation elements in an app, gamification will increase patient motivation, ensure the sustainability of use, and ultimately increase patient adherence. In addition, our GRVOTS mobile app connects up to 3 users (eg, patients, supervisors, and administrators) remotely and enable DOTS monitoring to be performed from anywhere. Based on the study findings, the GRVOTS mobile app has been validated by the expert panel as having the intended elements of gamification, real time, and motivation. Next, a usability study of the GRVOTS mobile app will be conducted to measure the user experience among patients, followed by a single-arm intervention study to assess the app’s effectiveness in increasing patient motivation and medication adherence in TB treatment. ## Data Availability All data supporting the study findings are within the manuscript. 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--- title: Autoimmune disease and risk of postpartum venous thromboembolism authors: - Rob F. Walker - Neil A. Zakai - Susan M. Mason - Richard F. MacLehose - Faye L. Norby - Line H. Evensen - Alvaro Alonso - Pamela L. Lutsey journal: Research and Practice in Thrombosis and Haemostasis year: 2023 pmcid: PMC10031534 doi: 10.1016/j.rpth.2023.100091 license: CC BY 4.0 --- # Autoimmune disease and risk of postpartum venous thromboembolism ## Abstract ### Background The risk of pregnancy-related mortality in the United States has nearly doubled since 1990, with venous thromboembolism (VTE) accounting for approximately $10\%$ of these deaths. ### Objectives The objective of this study was to assess whether preexisting autoimmune disease is a risk factor for postpartum VTE. ### Methods Using the MarketScan Commercial and Medicare Supplemental administrative databases, a retrospective cohort study analyzed whether postpartum persons with autoimmune disease had a higher risk of postpartum VTE incidence than postpartum persons without autoimmune disease. Using International Classification of Diseases codes, we identified 757,303 individuals of childbearing age who had a valid delivery date with at least 12 weeks of follow-up. ### Results Individuals were, on average, 30.7 years old (SD, 5.4), and $3.7\%$ ($$n = 27$$,997 of 757,303) of them had evidence of preexisting autoimmune disease. In covariate-adjusted models, postpartum persons with preexisting autoimmune disease had higher rates of postpartum VTE than postpartum persons without autoimmune disease (hazard ratio [HR], 1.33; $95\%$ CI, 1.07-1.64). When analyzed by individual autoimmune disease, those with systemic lupus erythematosus (HR, 2.49; $95\%$ CI, 1.47-4.21) and Crohn’s disease (HR, 2.49; $95\%$ CI, 1.34-4.64) were at an elevated risk of postpartum VTE compared with those without autoimmune disease. ### Conclusion Autoimmune disease was associated with a higher rate of postpartum VTE, with evidence that the association was most pronounced among individuals with systemic lupus erythematosus and Crohn’s disease. These findings suggest that postpartum persons of childbearing age with autoimmune disease may require more monitoring and prophylactic care after delivery to prevent potentially fatal VTE events. ## Essentials •Pregnancy and autoimmune diseases have inflammatory factors that increase the risk of blood clots.•We tested whether autoimmune disease was associated with postpartum venous thromboembolism (VTE).•Postpartum persons with autoimmune disease had a $33\%$ increased risk of postpartum VTE.•Postpartum persons with Crohn’s disease and systemic lupus erythematosus had the highest VTE risk. ## Introduction The risk of pregnancy-related mortality has nearly doubled in the United States in the past 30 years, with 20.1 deaths per 100,000 live births reported in 2019 [1,2]. Venous thromboembolism (VTE), which consists of both pulmonary embolism (PE) and deep vein thrombosis (DVT), occurs in approximately 120 of every 100,000 pregnancies [3,4]. PE is responsible for about $9\%$ of pregnancy-associated deaths [1,5]. VTE risk is elevated in pregnancy because of an acquired procoagulant state, mechanical obstruction, and many other factors [6]. These factors increase the risk of VTE antepartum and during the puerperium [7,8]. In the puerperium, VTE risk is highest during the first week after delivery (∼90 per 100,000), declines to ∼25 per 100,000 in the second week, and is near the baseline levels by around the 12th week [9]. Factors such as smoking and obesity, as well as certain obstetric procedures and complications (eg, cesarean delivery, obstetrical hemorrhage, or preeclampsia), confer an increased risk for VTE [[9], [10], [11]]. For pregnant persons perceived to be at particularly high VTE risk, clinical practice guidelines from the American Hematologic Society and several obstetric societies recommend prophylactic measures such as administration of low–molecular-weight heparin during and after pregnancy [[12], [13], [14], [15]]. Pregnancy-related VTE is a significant source of morbidity and mortality, and identification of novel risk factors for pregnancy-associated VTE remains a priority to appropriately target prophylactic measures and thus reduce complications due to pregnancy-associated VTE. Several autoimmune diseases, such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and inflammatory bowel diseases (IBDs), increase the risk of VTE in the nonpregnant population [[16], [17], [18], [19]]. For example, patients with SLE have up to a $26\%$ risk of experiencing a thrombosis event throughout their disease course [19,20]. These autoimmune diseases are believed to increase the VTE risk by upregulating the body’s procoagulants while simultaneously decreasing the physiological anticoagulants by systemic inflammation. A high prevalence of antiphospholipid antibodies is common in patients with autoimmune disease, leading to an increased risk of antiphospholipid antibody syndrome, which targets phospholipid-protein complexes that are known to increase the risk of VTE [20]. Given that pregnancy itself is a procoagulant state [8], autoimmune diseases may exacerbate the already increased risk during and immediately after pregnancy. Few studies have estimated the association of autoimmune disease with the risk of pregnancy-associated VTE. A barrier to evaluating this association is the relative rarity of both conditions. To minimize this limitation, we constructed a retrospective cohort using data from a large administrative database composed of medical claims from the employer-provided health insurance. We tested whether the hypothesis that autoimmune disease is associated with an increased risk of VTE in the 12 weeks after delivery. We also evaluated the association of several specific autoimmune conditions with VTE risk, namely thyrotoxicosis, Hashimoto’s thyroiditis, ankylosing spondylitis, psoriasis, RA, SLE, and Crohn’s disease. Among these, we hypothesized that those that were systemic (eg, SLE) would be more strongly associated with postpartum VTE than those that are organ-specific autoimmune diseases (eg, thyrotoxicosis or Hashimoto’s thyroiditis). ## Data source A retrospective cohort study was constructed using data obtained from the US IBM MarketScan Commercial Claims and Encounter Database (IBM Watson Health) from 2011 to 2018. The database contains individual-level enrolment, inpatient, outpatient, ancillary, and prescription data from healthcare claims provided by US employers, health plans, and hospitals. Data are compliant with the Health Insurance Portability and Accountability Act and are deidentified. This study was deemed exempt by the University of Minnesota Institutional Review Board. ## Study design and study population From MarketScan, we obtained records for a random sample of 1 million individuals with evidence of pregnancy. Using these enrollees, we identified 814,647 persons with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), or Tenth Revision (ICD-10-CM) diagnosis inpatient code for delivery. To be included as a delivery in this study, pregnancies had to last at least 20 weeks, indicated by International Classification of Diseases (ICD) codes covering natural delivery, Caesarian sections, or extraction by other methodologies (most codes in V27.xx and Z37.xx). Although the face validity of these codes is high, formal validation studies of ICD codes for all deliveries are lacking. However, validation exists for some specific delivery types, and the accuracy of the codes appears reasonable (eg, caesarian section delivery has $98\%$ specificity and $73\%$ sensitivity) [21,22]. The ICD-$\frac{9}{10}$ diagnosis and procedure codes used to classify delivery are compiled in Supplementary Table 1. After restricting to individuals of common childbearing age (15-45 years) with 3 months of continuous enrolment before their delivery date, we identified 757,303 persons who delivered for the analytic dataset. Only the first birth in the 7-year follow-up period was considered for this analysis. The start of study follow-up was defined as the date of a pregnant person’s first delivery. ## Exposure assessment Twenty-four unique autoimmune diseases were considered for this analysis and are listed with their corresponding ICD-$\frac{9}{10}$-CM codes in Supplementary Table 2. The validity of ICD codes for identifying autoimmune disease varies by disease but is generally high [23,24]. For example, a systematic review reported ICD-9-CM code 710.0 to have a PPV of $70\%$ to $90\%$ for identifying SLE [23]. Preexisting autoimmune diseases were identified by the presence of 2 or more inpatient and/or 2 or more outpatient ICD codes occurring more than 7 days apart before the first delivery date. Owing to the low prevalence of several autoimmune diseases among persons of childbearing age, only 7 autoimmune diseases were considered for stratified analyses: thyrotoxicosis, Hashimoto’s thyroiditis, ankylosing spondylitis, psoriasis, RA, SLE, and Crohn’s disease. ## Outcome assessment Postpartum VTE events were confined to cases occurring on the date of delivery and 12 weeks thereafter. Incident VTE events were identified by the presence of at least one inpatient ICD-$\frac{9}{10}$-CM diagnosis code or 2 outpatient diagnosis codes 7 to 185 days apart for VTE. We used an algorithm of ICD-$\frac{9}{10}$-CM codes and proof of anticoagulation treatment, which had been validated against medical record review, to detect VTE (PPV = $91\%$) [25]. Owing to the nature of this manuscript, we also added pregnancy-related ICD-$\frac{9}{10}$-CM codes to our algorithm (all codes are found in Supplementary Table 3). Inpatient and outpatient diagnoses were confirmed with at least one anticoagulant prescription occurring within 31 days of the VTE date. Patients without an anticoagulant prescription were excluded from the analyses. ## Covariates MarketScan demographic, inpatient, outpatient, and pharmacy claim data before the delivery date were used to assess the presence of relevant covariates. Comorbidities and prescription fills were assessed for any relevant ICD-9 or ICD-10 codes occurring at any time before the delivery date. We used ICD-$\frac{9}{10}$-CM coding algorithms developed by Quan to identify comorbidities [26]. These algorithms were found to outperform the existing comorbidity scores in administrative data [26]. ## Statistical analysis Cox regression was used to estimate hazard ratios (HRs) and $95\%$ CIs for the risk of postpartum VTE within 12 weeks of delivery, comparing postpartum persons with preexisting autoimmune disease with those without autoimmune diseases. Two adjusted models were used. Model 1 adjusted for age, hypertension, diabetes (both mellitus and gestational), preeclampsia, multiple gestation, and antepartum hemorrhage. Model 2 included all comorbidities in Model 1 plus prescription variables (immunosuppressant drugs, dexamethasone, oral prednisone, or hydroxychloroquine). Identical analyses also investigated the risk of postpartum VTE within 0 to 6 weeks after delivery and 7 to 12 weeks after delivery to assess whether risk differed by time postpartum. Associations were also evaluated separately for specific autoimmune diseases where enough VTE cases were available to derive meaningful estimates of association. All data management and analyses were performed using SAS version 9.4 (SAS Inc). ## Results The average age of postpartum persons in the analytic sample was 30.7 years (SD, 5.4 years), and 27,997 of the 757,303 deliveries ($3.7\%$) occurred among those with preexisting autoimmune diseases. The most prevalent 10 autoimmune diseases (Figure A), together with the number of postpartum VTE events per autoimmune disease (Figure B), are shown in the Figure. The most common autoimmune diseases were thyrotoxicosis ($24.5\%$), Hashimoto’s disease ($15.8\%$), ankylosing spondylitis ($11.3\%$), and psoriasis ($9.6\%$). Postpartum persons with preexisting autoimmune disease had higher proportions of comorbidities and pregnancy complications, and they were more likely to be prescribed immunosuppressant drugs, oral prednisone, hydroxychloroquine, and dexamethasone (Table 1).FigureThe distribution of (A) preexisting autoimmune disease and (B) number of postpartum venous thromboembolism (VTE) events among diseases in a sample of 27,997 postpartum persons with autoimmune disease who delivered, MarketScan 2011 to 2018. ITP, idiopathic thrombocytopenic purpura; SLE, systemic lupus erythematosus. Table 1Characteristics of postpartum persons by autoimmune status, MarketScan 2011 to 2018.Population characteristicsNo autoimmune diseaseAutoimmune disease($$n = 729$$,306)($$n = 27$$,997)Age (y), mean (SD)32.3 (5.8)33.1 (5.5)Person-years follow-up (mean)1,401,944 (1.9)51,877 (1.9)Postpartum VTE156288Postpartum VTE per 1000 person-y ($95\%$ CI)1.11 (1.06-1.17)1.70 (1.37-2.08)Comorbidities, n (%) Hypertension29,111 (4.0)2437 (8.7) Cancer4842 (0.7)655 (2.3) Diabetes (mellitus and gestational)108,569 (14.9)5463 (19.5) Preeclampsia50,308 (6.9)2580 (9.2) Multiple gestation23,160 (3.2)1346 (4.8) Antepartum hemorrhage51,951 (7.1)2800 (10.0) Chronic pulmonary disease55,062 (7.6)3834 (13.7) Depression56,210 (7.7)4237 (15.1)Medications, n (%) Immunosuppressants318 (0.04)1552 (5.5) Hydroxychloroquine253 (0.03)1403 (5.0) Oral prednisone41,679 (5.7)5912 (21.1) Dexamethasone5446 (0.8)565 (2.0)VTE, venous thromboembolism. There were 1650 postpartum VTE events in the study population; among them, 1470 ($89.1\%$) occurred within 0 to 6 weeks after delivery, and 88 ($5.3\%$) occurred in persons with preexisting autoimmune diseases. The Figure shows the number of VTE events stratified by specific autoimmune disease conditions that were the most common. In both the crude and adjusted models, postpartum persons with any autoimmune disease had a higher rate of postpartum rate of VTE (crude HR, 1.47; $95\%$ CI, 1.19, 1.82; Model 1 HR, 1.33; $95\%$ CI, 1.07, 1.64; Model 2 HR, 1.22; $95\%$ CI, 0.97, 1.85) relative to those without (Table 2). When stratifying by time since delivery, although not statistically significant, there was a signal that postpartum persons with autoimmune disease may have slightly higher rates of postpartum VTE 0 to 6 weeks after delivery (fully adjusted HR, 1.16; $95\%$ CI, 0.90, 1.49). Postpartum persons with autoimmune disease did have higher rates of postpartum VTE 7 to 12 weeks after delivery (fully adjusted HR, 1.91; $95\%$ CI, 1.03, 3.54) compared with those without autoimmune diseases. However, the effect estimates contained wide CIs for the latter comparison, with only 11 postpartum VTE events among autoimmune disease occurring. When evaluating the individual autoimmune diseases separately, the rate of postpartum VTE was almost 2 times higher among postpartum persons with SLE (HR, 1.90; $95\%$ CI, 0.98, 3.68) and 2.5 times higher among postpartum persons with Crohn’s disease (HR, 2.45; $95\%$ CI, 1.27, 4.72) relative to postpartum persons without these conditions; however, both of these results are imprecise. Effect estimates for postpartum VTE were close to the null hypothesis among those with Hashimoto’s thyroiditis, thyrotoxicosis, ankylosing spondylitis, psoriasis, and RA, often with very wide CIs owing to the small sample sizes. Table 2Association of autoimmune disease and risk of postpartum venous thromboembolism by autoimmune disease category, MarketScan 2011 to 2018.ExposureTotal NPostpartum VTEsCrude model HR ($95\%$ CI)Adjusted Model 1 HR ($95\%$ CI)aAdjusted Model 2 HR ($95\%$ CI)bAll autoimmune disease27,997881.47 (1.19-1.82)1.33 (1.07-1.64)1.22 (0.97-1.55)Specific autoimmune disease diagnosis Systemic lupus erythematosus2151143.03 (1.79-5.13)2.49 (1.47-4.21)1.90 (0.98-3.68) Crohn’s disease1730102.67 (1.43-4.97)2.49 (1.34-4.64)2.45 (1.27-4.72) Hashimoto’s thyroiditis4415141.46 (0.87-2.48)1.30 (0.77-2.20)1.29 (0.76-2.18) Thyrotoxicosis6870161.07 (0.65-1.75)0.97 (0.59-1.58)0.96 (0.59-1.57) Rheumatoid arthritis247871.30 (0.62-2.73)1.09 (0.52-2.29)0.73 (0.32-1.63) Psoriasis268671.20 (0.57-2.51)1.08 (0.51-2.26)1.03 (0.48-2.17) Ankylosing spondylitis315381.17 (0.58-2.34)1.05 (0.53-2.11)1.04 (0.52-2.08)HR, hazard ratio; VTE, venous thromboembolism.aAnalyses adjusted for age, hypertension, diabetes, preeclampsia, multiple gestation, and antepartum hemorrhage.bAnalyses adjusted for variables in Model 1 + immunosuppressant drugs, dexamethasone, oral prednisone, and hydroxychloroquine. ## Discussion In this commercially insured population of approximately 750,000 postpartum persons who had a delivery after at least 20 weeks of gestation, individuals with autoimmune disease had 1.33 times the rate of VTE in the 12 weeks after delivery compared with individuals without autoimmune disease, after controlling for comorbidities. The association was most pronounced among postpartum persons with SLE and those with Crohn’s disease, where the rate was nearly 2.5 times higher compared with postpartum persons without autoimmune diseases. These findings help clarify the diseases that may increase the risk of postpartum VTE and reveal a high-risk group of pregnant and postpartum persons who may require measures to reduce the morbidity and mortality from VTE. ## SLE and postpartum VTE Systemic autoimmune diseases, such as SLE, are associated with increased inflammation, potentially increasing molecular procoagulant factors that can eventually contribute to VTE [27]. Coupled with pregnancy-associated factors (eg, procoagulant state and stasis), systemic autoimmune diseases may increase the risk of VTE. In the present analysis of MarketScan data, postpartum persons with SLE were at a 2.5-fold greater risk of developing VTE. Patients with SLE have been shown to have an increased risk of VTE, in both general and peripartum populations [19]. A review by Zöller et al. [ 20] estimated that $10\%$ to $26\%$ of all SLE patients will experience thrombosis during their life. A study of pregnancy-associated VTE, conducted using the Health Care Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS) in the US, found an elevated risk of pregnancy-associated VTE with SLE (odds ratio [OR], 8.20; $95\%$ CI, 6.20, 10.84) and Crohn’s disease (OR, 2.71; $95\%$ CI, 1.50, 4.90) [28]. An important aspect of interpreting HCUP-NIS data, however, is that the records are based on hospitalization (not participant) and so were someone with SLE and VTE hospitalized multiple times that person would appear multiple times in the database. In a general population study by Yusuf et al. [ 16], those hospitalized with autoimmune diseases associated with antiphospholipid antibodies (including SLE, RA, autoimmune hemolytic anemia, and immune thrombocytopenic purpura) had an elevated risk of VTE during hospitalization (OR ranging from 1.17 to 1.25), controlling for any surgeries, pregnancy, or delivery. This study did not look at stratified results for pregnancy-related VTE. The results from our analysis of MarketScan data complement the existing literature on SLE and VTE and provide additional evidence of elevated risk for persons during the postpartum period. ## Crohn’s disease, IBD, and postpartum VTE Crohn’s disease generally affects the gastrointestinal system; however, inflammation from Crohn’s disease can cause complications outside of the bowel system that increase the risk of VTE [29]. Patients with IBD, including Crohn’s disease and ulcerative colitis, generally have a well-documented risk profile for VTE in the general (nonperipartum) population. A 2001 study by Bernstein et al. [ 30] showed that IBD patients have a 3-fold to 4-fold increased risk of developing a DVT (incidence rate ratio, 4.7; $95\%$ CI, 3.5, 6.3) or PE (incidence rate ratio, 2.9; $95\%$ CI, 1.8, 4.7). These findings are corroborated by several other studies, and effect estimates were often similar for the other IBDs, such as ulcerative colitis. For instance, a Danish study by Kappelman et al. [ 31] found that even young patients with IBD (ie, those aged 20 years and younger) were at an increased risk of VTE, despite their youth and VTE generally occurring at older ages [32]. Evidence of IBD and VTE risk in the peripartum period is somewhat limited. The HCUP-NIS study, which showed a higher risk of pregnancy-associated VTE with SLE, also reported an elevated risk among pregnant persons with Crohn’s disease (OR, 2.71; $95\%$ CI, 1.50, 4.90) [28]. To our knowledge, the only other study investigating IBD and peripartum VTE was a Danish study by Hansen et al. [ 33]. This study found that individuals with IBD, compared with those without, were at an elevated risk for postpartum VTE both during pregnancy (relative risk, 1.67; $95\%$ CI, 1.15, 2.41) and in the postpartum period (relative risk, 2.10; $95\%$ CI, 1.33, 3.30). The results were similar when the authors evaluated Crohn’s disease, specifically. In the present MarketScan analysis, Chron’s disease was associated with a 2.5-fold increased risk of VTE. We were not able to evaluate ulcerative colitis specifically, owing to its rarity in our study population. Our findings enhance the existing literature suggesting that IBD, specifically Crohn’s disease, is associated with an increased risk of VTE in the postpartum period. ## Additional autoimmune disease and postpartum VTE Several other autoimmune diseases considered in this analysis did not show a significant association with postpartum VTE events (ie, Hashimoto’s thyroiditis, RA, etc.). In many instances, CIs were wide, although point estimates were close to the null, suggesting that it may have been unlikely to see an effect even in a larger sample. Our study included postpartum persons with any evidence of preexisting autoimmune disease, not only recent hospitalization owing to autoimmune disease, and thus, it is possible that the lack of associations for other autoimmune diseases in our study is due to capturing a less severe distribution of these diseases. In addition, many of these diseases are organ-specific, and hence, they were hypothesized to have a lesser effect on VTE risk. Postpartum persons with Hashimoto’s thyroiditis, the most common cause of hypothyroidism, had a HR of 1.30 ($95\%$ CI, 0.77, 2.20) in the present analysis. Previous research has reported hypothyroidism to be modestly associated with both DVT and PE, agreeing with our effect estimate for this particular autoimmune disease [34]. Thyrotoxicosis can sometimes be difficult to diagnose during pregnancy because of similar physiological and hormonal effects, potentially explaining the high number of postpartum persons diagnosed in the present study (nearly 7000) and our null findings [35]. In the present analysis, approximately 2500 postpartum persons had a diagnosis of RA; however, only 7 postpartum VTE events were measured among those with this condition. In the HCUP-NIS study, RA was associated with pregnancy-related PE (OR, 3.62; $95\%$ CI, 1.50, 8.70) [28]. It is common for pregnant persons with RA to be in a “remission” state of disease during pregnancy, potentially explaining our null association with VTE. However, RA remains a well-known risk factor for VTE, and pregnant persons with this condition should be monitored for risk during pregnancy and the postpartum period [20,36]. Both psoriasis and ankylosing spondylitis, systemic autoimmune diseases, are shown to be associated with VTE in nonpregnant populations [[37], [38], [39]]; however, studies that have investigated these diseases and VTE during pregnancy and the postpartum period are scarce. One Scandinavian study looking at pregnant persons with psoriasis and singleton births found no outstanding relationship between psoriasis and pregnancy-related VTE [40]. We also found a null association among those with psoriasis; however, individuals with psoriasis may have been misclassified because of rashes such as eczema or contact dermatitis being coded as psoriasis. All autoimmune disease subclassifications included in this study population had less than 15 postpartum VTE events during the study period. As such, our findings for these autoimmune diseases must be viewed with caution owing to poor precision. ## VTE prophylaxis among peripartum persons with autoimmune disease There is an overall low absolute risk for a pregnancy-related VTE; however, reducing pregnancy-associated VTE is a public health priority, given the potential for devastating consequences, and the observation that underserved and diverse populations are at an increased risk of VTE [41] and have higher a burden of autoimmune diseases [[42], [43], [44]]. Our findings, from a large real-world sample, provide additional support for the existing clinical practice guidelines from the American College of Obstetricians and Gynecologists, the Society of Obstetricians and Gynecologists of Canada, and the Royal College of Obstetricians and Gynecologists (summarized in Table 3) [[12], [13], [14]]. The Society of Obstetricians and Gynecologists of Canada and Royal College of Obstetricians and Gynecologists recommendations include certain autoimmune diseases as “high-risk” factors to be considered when deciding whether to engage with thromboprophylaxis, whereas the American College of Obstetricians and Gynecologists recommendations have no mention of autoimmune diseases other than genetic thrombophilia factors. Table 3Summary of thromboprophylaxis recommendations for postpartum venous thromboembolism for postpartum persons with/without autoimmune disease. SocietyProphylaxis recommended in the postpartum period?Mention of autoimmune disease in determining the need for prophylaxis?Agent and dosage of thromboprophylaxisSOGC [13]Universal postpartum thromboprophylaxis not recommended, assessments required, and prophylaxis based on the presence of preexisting risk factors deemed high-risk by SOGCYes, postpartum thromboprophylaxis should be administered if SLE and/or IBD is coupled with another risk factor contributing to a >$1\%$ change in VTELMWH is preferredRCOG [14]Immediate risk assessment and prophylaxis recommended if a woman has 2 noted risk factors deemed high-risk by the RCOG other than a history of VTE or thrombophiliaYes, prophylactic thromboprophylaxis recommended if SLE and/or antiphospholipid syndrome is coupled with another preexisting, obstetric, or transient risk factor deemed “high-risk” by RCOGProphylactic LMWH for at least 10 d postpartum. For those with a history of VTE and antithrombin deficiency, a higher dose of LMWH for 6 wk or oral anticoagulant is restarted, as soon as possible after deliveryACOG [15]Dependent on several clinical scenarios. Pregnant persons with no VTE history simply require surveillance. Those with a history of VTE and/or a high-risk thrombophilia are recommended prophylaxis 4-12 h after delivery for up to 6 wkNone, although inherited genetic thrombophilia is highlighted as a risk factor and may be linked with antiphospholipid syndromeLMWH (prophylactic, intermediate-dose, or adjusted-dose) or unfractionated heparin (prophylactic or adjusted-dose) depending on the clinical scenario and prevalent risk factorsACOG, American College of Obstetricians and Gynecologists; IBD, inflammatory bowel diseases; LMWH, low–molecular-weight heparin; RCOG, Royal College of Obstetricians and Gynaecologists; SLE, systemic lupus erythematosus; SOGC, Society of Obstetricians and Gynaecologists of Canada; VTE, venous thromboembolism. ## Strengths and limitations Using a large administrative dataset is a source of both strengths and weaknesses of this manuscript. This study evaluated a broad array of autoimmune diseases and pregnancy-related VTE risks in the United States, enabled by the real-world population of over 750,000 pregnant persons that allowed for the prospective assessment of associations between these relatively rare exposures and VTE. However, precision was at times poor for the analyses of specific autoimmune conditions, resulting in wide CIs. Studies regarding autoimmune disease and VTE during pregnancy are relatively uncommon. The most prominent research regarding autoimmune disease and pregnancy-related thrombosis comes from the HCUP-NIS data, which do not allow for the detailed longitudinal analyses and come with inherent limitations because of the following: a) it includes hospitalization discharge information only and b) the same individual may be included in the analysis more than once, if they have multiple discharges [28]. MarketScan’s inclusion of outpatient data allowed for a more comprehensive evaluation of patient history and establishing a clinically meaningful denominator, and the approach is that of a traditional cohort with each individual included in the analysis only once. As another limitation, variables in this study were defined using ICD codes from the billing data and are subject to misclassification. However, whenever possible, we used ICD-based definitions that were validated against a medical record review to minimize misclassifications. For example, our VTE definition had a positive predictive value (PPV) of $91\%$ [25]. Nonetheless, misclassification remains a threat to validity. Only the first delivery in the study period was used for the analysis, potentially omitting later pregnancies, deliveries, and potential pregnancy-related VTE events. Related to the VTE outcome, an inherent limitation of MarketScan is the lack of information on out-of-hospital death. Therefore, some fatal postpartum VTE events were almost certainly misclassified. In addition, MarketScan includes only individuals with private insurance. Thus, our results may not be generalizable to the enrollees who have severe autoimmune diseases and are uninsured because they are unable to work. Finally, MarketScan did not include information on some key potential covariates, such as race/ethnicity or socioeconomic status (income, education, etc.). Given the health disparities in both maternal mortality [2] and VTE [41], identifying VTE risk factors among racial/ethnic groups at the highest risk is paramount and a limitation in the manuscript. ## Conclusions Reducing VTE-related maternal mortality and morbidity in the United States, where VTE complicates 1 of every 1000 pregnancies, is vitally important. The findings in the current study suggest values for increasing VTE awareness among pregnant and postpartum persons with autoimmune conditions particularly—SLE, Crohn’s disease—and their healthcare providers. Prophylactic measures are sometimes recommended for high-risk individuals with SLE during pregnancy and may additionally be beneficial for those with IBD, particularly Crohn’s disease, during pregnancy. Research regarding VTE among pregnant persons with autoimmune disease remains sparse, and targeted studies investigating SLE, Crohn’s disease, and less common autoimmune diseases may highlight areas of improvement for pregnancy-related and postpartum thrombosis outcomes among persons of childbearing age. ## Supplementary material Supplemental Tables 1-3 ## Funding The manuscript was funded and supported by the $\frac{10.13039}{100000002}$National Institutes of Health National Heart Lung and Blood Institute grants R01 HL131579, K24 HL159246, and K24 HL148521. The supporters had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. ## Ethics Data were obtained from MarketScan Commercial Claims and Encounters Databases. Data were de-identified and HIPAA compliant. The study was deemed exempt by the University of Minnesota Institutional Review Board. ## Author contributions R.F.W. and P.L.L. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and Design: R.F.W., P.L.L., and N.A.Z conceptualized and designed the study. R.F.W., P.L.L., N.A.Z., S.M.M., and R.F.M acquired, analyzed, or interpreted the data. R.F.W., P.L.L., and N.A.Z drafted the manuscript. All authors critically revised the manuscript. R.F.W. performed the statistical analysis. 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--- title: Evaluation of Usage of a Fracture Risk Assessment by FRAX Tool in Adults With Type 2 Diabetes Mellitus journal: Cureus year: 2023 pmcid: PMC10031547 doi: 10.7759/cureus.35205 license: CC BY 3.0 --- # Evaluation of Usage of a Fracture Risk Assessment by FRAX Tool in Adults With Type 2 Diabetes Mellitus ## Abstract Background: Fragility fractures are increasingly recognized as a complication of type 2 diabetes mellitus (T2DM). The FRAX-Port® is a calculation tool that assesses the 10-year risk of either major and hip fracture, integrating several clinical risk factors, including T2DM. We aimed to evaluate the fracture risk in adults with T2DM and determine the rate of patients at high risk for fracture under anti-osteoporotic therapy. Methods: We developed a cross-sectional study, including a convenience sample of adults with T2DM, followed in our tertiary center between 2019 and 2022. Fracture risk was evaluated according to FRAX-Port®. Results: One hundred adults were included, $54\%$ male, with a mean age of 68.4±9.2 years. Respecting fracture risk factors, $17\%$ had a previous fragility fracture, $12\%$ had a history of hip fracture in their parents, $9\%$ had active alcohol consumption, and $4\%$ had active smoking. Additionally, $17\%$ presented secondary osteoporosis, being the most frequent cause of systemic corticosteroid exposure ($10\%$). Regarding diabetes-specific risk factors, $94\%$ had a diabetes duration longer than five years; HbA1c greater than $7\%$ in $70\%$; $42\%$ had diabetic retinopathy, $33\%$ had diabetic chronic kidney disease, $18\%$ had peripheral neuropathy, and $7\%$ had autonomic neuropathy; $83\%$ were on insulin, $2\%$ on canagliflozin and $1\%$ on pioglitazone. According to the FRAX-Port®, the median probability of major fracture was $6.8\%$ (IQR 6.9), and hip fracture was $2.4\%$ (IQR 3.9). Fracture risk was high, intermediate, and low at $41\%$, $15\%$, and $44\%$, respectively. Lastly, $56\%$ of participants should undergo bone densitometry and $45\%$ had a formal recommendation to begin an anti-osteoporotic treatment. However, only $6\%$ were under anti-osteoporotic therapy: bisphosphonates ($5\%$) and denosumab ($1\%$). Conclusions: More than a third of T2DM patients evaluated had a high fracture risk. We found that FRAX-Port® is an easy-to-apply tool, which helps in the decision to perform densitometry or to institute anti-osteoporotic therapy. Given the increasing prevalence of T2DM and the associated risk of falls, this study highlights the need to recognize the fracture risk in these patients, usually a forgotten complication during the screening of risk factors for adverse events in adults with T2DM. ## Introduction Osteoporosis is the most prevalent metabolic bone disease worldwide. Its prevalence reached 32 million European individuals in 2019, $5.6\%$ of the total European population, representing a major public health concern. Fragility fractures constitute the principal adverse outcome of osteoporosis and often led to impaired function, chronic pain, and either a major decline in the quality of life or even death [1]. Type 1 diabetes mellitus (T1DM) has been associated with reduced bone mass and increased fracture risk. Additionally, bone fragility is increasingly recognized as a complication of type 2 diabetes mellitus (T2DM) [2]. However, the identification and management of fracture risk in the T2DM population remain challenging since bone mineral density (BMD) in these patients tends to be relatively preserved or even higher than in healthy subjects [2,3]. In T2DM, bone microarchitectural changes decrease bone strength and quality, suppress bone turnover and increase cortical porosity. Moreover, individuals with long-standing diabetes have an increased risk of falls and consequent susceptibility to fractures due to hypoglycemic episodes, decreased visual acuity, and impaired mobility and balance [2]. Considering the progressive aging of the population and the increasing prevalence of diabetes, evidence suggests fragility fractures to represent a major, although underdiagnosed, manifestation of this disease. Strategies to effectively identify and promptly intervene in the risk of an osteoporotic fracture within these individuals are mandatory [1]. These concerns about the clinical utility of BMD in the T2DM population justify the need for alternative tools to accurately predict fracture risk in these patients. The Fracture Risk Assessment Tool (FRAX) is a calculation score that assesses the 10-year probability of hip and major - at the spine, hip, forearm, and humeral - osteoporotic fractures, integrating several clinical risk factors [2]. The Portuguese version of the FRAX tool (FRAX-Port®) was created and fully validated for the Portuguese population and can help in the decision to perform densitometry or to institute anti-osteoporotic therapy [4]. This algorithm applies to individuals aged between 40 and 90 years [4]. Only T1DM is included among the secondary causes of osteoporosis in the FRAX score, whereas T2DM is not. Some strategies have been used to optimize the use of FRAX within individuals with T2DM, such as selecting rheumatoid arthritis as an equivalent variable for T2DM in the FRAX [2]. Evidence addressing the risk of osteoporotic fracture in T2DM individuals is scarce. Therefore, we aimed to evaluate fracture risk in adults with T2DM and determine the rate of patients at high risk of fracture under anti-osteoporotic therapy. ## Materials and methods We performed a cross-sectional study, including a convenience sample of adults with T2DM, followed in our outpatient Endocrinology and Diabetes Clinic between 2019 and 2022. Only individuals aged between 40 and 90 years were included since FRAX is only applicable within this age range. This work refers to an observational study using data from an anonymized database. All data were anonymously collected and analyzed. The fracture risk factors that are involved in the FRAX model were documented, including age, gender, height, weight, previous fragility fracture (those occurring at skeletal sites usually associated with osteoporosis such as hip, vertebrae, forearm, and humerus, and not caused by external trauma), parental history of hip fracture, glucocorticoid use, current smoking status, alcohol intake, rheumatoid arthritis and causes of secondary osteoporosis (hyperparathyroidism, long-standing untreated hyperthyroidism, hypogonadism or premature menopause, chronic malnutrition or malabsorption and chronic liver disease). Weight (kg) was measured and height (cm) was self-reported. The 10-year probability of major osteoporotic fracture and hip fracture was calculated by the Portuguese version of the Fracture Risk Assessment Tool, FRAX-Port® (https://www.shef.ac.uk/FRAX/tool.jsp?lang=pt) [4]. Data about BMD were not available and, therefore, this information was not included in the analysis. The FRAX risk was adjusted to T2DM by selecting rheumatoid arthritis as the equivalent variable for T2DM [2]. Fracture risk was considered high if the probability of a major or hip fracture was above $11\%$ or $3\%$, respectively; intermediate if the probability of a major or hip fracture was between $7\%$-$11\%$ or $2\%$-$3\%$, respectively; and low if the probability of a major or hip fracture was below $7\%$ or $2\%$, respectively [4]. BMD evaluation was warranted when pre-BMD evaluation FRAX-Port® estimates were between $7\%$ and $11\%$ for major osteoporotic fracture and between $2\%$ and $3\%$ for hip fracture [4]. Lastly, it was recommended to start anti-osteoporotic therapy in all subjects that satisfied one or more of the following criteria: a) ≥1 fragility fracture of the hip or ≥1 symptomatic vertebral fragility fracture; b) ≥2 fragility fractures, independently of the site of fracture or the absence of symptoms; and c) estimates of FRAX-Port®, without DXA, ≥$11\%$ for major osteoporotic fracture or ≥$3\%$ for hip fracture [4]. Categorical and continuous variables were presented as percentages with ratios and mean values ± standard deviation or median values (interquartile range [IQR]), respectively. Distribution normality was tested for continuous quantitative variables using both histogram observation and the Kolmogorov-Smirnov test analysis. All statistical tests were performed using statistical software (SPSS version 25.0 for Windows; IBM Co., Armonk, NY). ## Results One-hundred patients were included, $54\%$ ($$n = 54$$) male, with a mean age of 68.4±9.2 years. The median time since diabetes diagnosis was 20 years (IQR 11) and the mean HbA1c was 7.9±$1.6\%$. The mean BMI was 28.4±4.6kg/m2. Respecting fracture risk factors, $17\%$ ($$n = 17$$) had a previous fragility fracture, $12\%$ ($$n = 12$$) had a history of hip fracture in their parents, $9\%$ ($$n = 9$$) had active alcohol consumption, and $4\%$ ($$n = 4$$) active smoking. Additionally, $17\%$ ($$n = 17$$) presented secondary osteoporosis, especially due to the use of systemic glucocorticosteroids ($10\%$), followed by male hypogonadism ($2\%$) and primary biliary cirrhosis ($2\%$) (Table 1). Table 1 \| Fracture risk factors \| Fracture risk factors.1 \| N=100 \| \| --- \| --- \| --- \| \| Previous fragility fracture \| Previous fragility fracture \| 17% (n=17) \| \| History of hip fracture in parents \| History of hip fracture in parents \| 12% (n=12) \| \| Active alcohol consumption \| Active alcohol consumption \| 9% (n=9) \| \| Active smoking \| Active smoking \| 4% (n=4) \| \| Causes of secondary osteoporosis \| Systemic corticosteroids \| 10% (n=10) \| \| Causes of secondary osteoporosis \| Male hypogonadism \| 2% (n=2) \| \| Causes of secondary osteoporosis \| Primary biliary cirrhosis \| 2% (n=2) \| \| Causes of secondary osteoporosis \| Primary hyperparathyroidism \| 1% (n=1) \| \| Causes of secondary osteoporosis \| Adrenal adenoma with ACS \| 1% (n=1) \| \| Causes of secondary osteoporosis \| Menopause <45 years \| 1% (n=1) \| \| Causes of secondary osteoporosis \| Aromatase inhibitor \| 1% (n=1) \| \| Diabetes specific risk factors \| Diabetes duration >5 years \| 94% (n=94) \| \| Diabetes specific risk factors \| HbA1c >7% \| 70% (n=70) \| \| Diabetes specific risk factors \| Microvascular complications \| 62% (n=62) \| \| Diabetes specific risk factors \| Diabetic retinopathy \| 42% (n=42) \| \| Diabetes specific risk factors \| Diabetic kidney disease \| 33% (n=33) \| \| Diabetes specific risk factors \| Peripheral neuropathy \| 18% (n=18) \| \| Diabetes specific risk factors \| Autonomic neuropathy \| 7% (n=7) \| \| Diabetes specific risk factors \| Anti-diabetic therapy \| 83% (n=83) \| \| Diabetes specific risk factors \| Insulin \| 83% (n=83) \| \| Diabetes specific risk factors \| Canagliflozin \| 2% (n=2) \| \| Diabetes specific risk factors \| Pioglitazone \| 1% (n=1) \| Regarding diabetes-specific risk factors, $94\%$ ($$n = 94$$) had a diabetes duration longer than five years; HbA1c greater than $7\%$ in $70\%$ ($$n = 70$$); $62\%$ ($$n = 62$$) had microvascular complications; $83\%$ ($$n = 83$$) were on insulin, $2\%$ ($$n = 2$$) on canagliflozin and $1\%$ ($$n = 1$$) on pioglitazone (Table 1). According to the FRAX-Port®, the median probability of major fracture was $6.8\%$ (IQR 6.9) and of hip fracture was $2.4\%$ (IQR 3.9). Fracture risk was considered high, intermediate, and low at $41\%$ ($$n = 41$$), $15\%$ ($$n = 15$$), and $44\%$ ($$n = 44$$), respectively. Lastly, $56\%$ ($$n = 56$$) of participants should undergo bone densitometry and $45\%$ ($$n = 45$$) had a formal recommendation to begin an anti-osteoporotic treatment. However, only $6\%$ ($$n = 6$$) were under anti-osteoporotic therapy: bisphosphonates ($5\%$, $$n = 5$$), namely alendronate ($4\%$, $$n = 4$$) and ibandronate ($1\%$, $$n = 1$$), and denosumab ($1\%$, $$n = 1$$) (Table 2). Table 2 \| FRAX-Port® calculation tool \| FRAX-Port® calculation tool.1 \| N=100 \| N=100.1 \| \| --- \| --- \| --- \| --- \| \| Probability of major fracture* \| Probability of major fracture* \| 6.8% (6.9) \| 6.8% (6.9) \| \| Probability of hip fracture* \| Probability of hip fracture* \| 2.4% (3.9) \| 2.4% (3.9) \| \| Fracture risk \| High \| 41% (n=41) \| 41% (n=41) \| \| Fracture risk \| Intermediate \| 15% (n=15) \| 15% (n=15) \| \| Fracture risk \| Low \| 44% (n=44) \| 44% (n=44) \| \| Indication for bone densitometry \| Indication for bone densitometry \| 56% (n=56) \| 56% (n=56) \| \| Indication for anti-osteoporotic treatment \| Indication for anti-osteoporotic treatment \| 45% (n=45) \| 45% (n=45) \| \| Treatment \| Anti-osteoporotic therapy \| Anti-osteoporotic therapy \| 6% (n=6) \| \| Treatment \| Bisphosphonates \| Bisphosphonates \| 5% (n=5) \| \| Treatment \| Denosumab \| Denosumab \| 1% (n=1) \| \| Treatment \| Calcium supplementation \| Calcium supplementation \| 1% (n=1) \| \| Treatment \| Vitamin D supplementation \| Vitamin D supplementation \| 10% (n=10) \| ## Discussion Bone fragility is increasingly recognized as a complication of T2DM [2]. According to our study, more than a third of T2DM adults evaluated had a high osteoporotic fracture risk, reinforcing this association. As previously stated, the management of fracture risk in the T2DM population remains challenging since BMD tends to be relatively preserved or even higher than in healthy subjects [2,3]. We found out that FRAX-Port® constitutes an easy and effective alternative tool to predict fracture risk within this population and can guide the decision on performing bone densitometry and starting anti-osteoporotic therapy, which is consistent with previous studies [5-7]. Given that T2DM is associated with higher fracture risk, which is independent of the ordinary clinical risk factors, some have already suggested including T2DM in FRAX. To enhance the ability of FRAX in the prediction of fracture risk in patients with T2DM, it has been suggested the equivalent replacement of rheumatoid arthritis with T2DM in the algorithm, decreasing the T-value of the femoral neck by 0.5 SD or adding 10 years of age [2]. In our study, the FRAX risk was adjusted to T2DM by selecting rheumatoid arthritis as the equivalent variable for T2DM. However, it should be noted that some patients can have both diseases, rheumatoid arthritis and T2DM, which limits the use of this strategy within this subpopulation. We also found that the FRAX tool has other important limitations, namely in those patients with another additional cause of secondary osteoporosis, whose fracture risk calculated by FRAX remains the same. According to the FRAX-Port®, the median probability of major fracture and hip fracture was similar to other previous studies [5,8]. The mean age of our sample was high, certainly contributing to the higher risk of osteoporotic fracture. However, Valentini et al. found approximately a double FRAX major osteoporotic fracture probability ($12.1\%$) and hip fracture probability ($4.7\%$) in a sample with similar mean age (73 years) [9]. Surprisingly, we found that more than a third ($41\%$) of T2DM adults evaluated had a high osteoporotic fracture risk, despite a reduced number of them under pharmacological therapy with either antiosteoporotic agents or calcium and vitamin D supplementation. A previous study by Hu et al. reported a lower rate of high fracture risk ($19.4\%$) in a cohort of 1047 patients with T2DM [10]. We must not forget that the risk-based thresholds for an intervention that we present are based on cost-effectiveness and are appropriate for cheaper treatment schemes, such as alendronate; expensive treatment strategies may need higher thresholds to justify intervention [2,11]. Additionally, we found a high rate of previous fragility fractures ($17\%$), which is consistent with previous studies that reported a rate of previous fragility fractures between $13.8\%$ and $29.9\%$ [5,12,13]. The mechanisms associated with an increased fracture risk within individuals with T2DM are numerous. Firstly, it is known that estrogen levels are reduced in elderly individuals, which significantly decreases the absorption of calcium within the intestine and compromises 1,25-(OH)2 vitamin D3 production in the kidney, leading to secondary hyperparathyroidism and promoting bone reabsorption. Secondly, the adipokines and inflammatory factors produced in the visceral adipose tissue may also increase bone reabsorption. Lastly, non-enzyme-promoting glycosylation usually results in the deposit of advanced glycation end-products within the organic bone matrix, increasing its fragility, and the accumulation of bone-marrow fat may also increase the risk of fracture [10]. Surprisingly, in our study diabetic specific risk factors were significantly prevalent, although none of them are considered in the FRAX tool. Previous studies have already reported the relationship between poor glycemic control and the risk of fracture, showing that HbA1C levels equal to or above $7\%$ were associated with a higher incidence rate of hip fractures [9,14,15]. Some have stated that a biphasic response may occur within individuals with T2DM during their disease, presenting a decreased risk of fracture close to their diagnosis which only significantly increases after five years. This may be due to a protective effect from both an increase in total fat mass and insulin levels, which leads to an anabolic status that assures normal bone formation in these newly diagnosed T2DM individuals [16]. However, in T2DM, other factors than glycemic control and longer duration of diabetes may affect fracture risk and should be considered, such as diabetic neuropathy, diabetic retinopathy [17], and glucose-lowering agents, such as insulin and thiazolidinediones, which are associated with either a higher risk of hypoglycemia-induced falls and bone loss, respectively, and therefore contribute to an increased risk of fracture [18,19]. Thiazolidinediones interact with peroxisome proliferator-activated receptor (PPAR)γ, favoring adipocyte differentiation over osteoblasts and regulating the expression of genes involved in adipogenesis, glucose homeostasis, and inflammation [2]. Evidence from the CANVAS study on sodium-glucose cotransporter 2 (SGLT2) inhibitors showed a decreased bone density and a higher risk of fractures in patients treated with canagliflozin [20]. Robust evidence of the safety of SGLT2 inhibitors within bone health is warranted, although both empagliflozin and dapagliflozin are currently being favored in T2DM patients with known bone fragility, as the available data on both of them has not raised similar concerns [2]. Furthermore, hypoglycemic episodes increase the risk of falls and consequent fractures [21]. In our study, data about the characteristics of diabetes were available; however, we could not evaluate their effect on fracture risk calculated by the FRAX tool. Ordinary clinical risk factors can be applied to efficiently identify patients with diabetes at increased fracture risk, although risk assessment tools such as FRAX do not fully capture these increased risks, even when BMD is included in the risk score, and thus systematically underestimate the risk of osteoporosis-related fractures in patients with T2DM [2,22,23]. Therefore, the FRAX model should be interpreted with caution and further improved to evaluate the risk of major osteoporotic fractures and hip fractures in theT2DM population. Our study has several strengths. Given that published studies on this subject are scarce, we consider that our study adds valuable data. Moreover, we had detailed information about diabetes-specific risk factors, which enriched our study. We investigated the absolute risk of fracture in patients with T2DM through the FRAX algorithm, without the evaluation of femoral BMD; this strategy allowed us to decrease the cost associated with auxiliary diagnostic exams, and facilitated our workflow, possibly reflecting a way to the widespread use of this easy and cheap tool in every outpatient setting. On the other hand, this study has also some limitations. Firstly, its cross-sectional design raises the issue of selection and information bias. The patients’ sample was drawn in an outpatient clinic setting, so it tightly mirrors real-world clinical practice. Secondly, we only included self-reported fractures, which increases the possibility of some recall inaccuracy, especially within the older subjects. In addition, asymptomatic vertebral fractures were not investigated. Moreover, as a Portuguese study, most of the participants were Caucasian from the Mediterranean area; therefore, our results are not generalizable to other populations. Lastly, we did not include a control group, so we were not able to demonstrate that the FRAX score for both major and hip osteoporotic fractures is higher in the T2DM population than in individuals without diabetes. ## Conclusions More than one-third of the T2DM patients evaluated had a high fracture risk. Given the increasing prevalence of T2DM and the associated risk of falls, this study highlights the need to recognize the fracture risk in these patients, an often-overlooked complication of diabetes. FRAX-Port® constitutes an easy-to-apply tool, which helps in the decision to perform densitometry or to institute anti-osteoporotic therapy in clinical practice. We suggest that a FRAX adjustment for T2DM may be useful within clinical practice, and here recommend applying this fracture risk assessment tool in individuals with T2DM, selecting rheumatoid arthritis as the equivalent variable for T2DM. Further studies are needed on the evaluation of the structural determinants of bone fragility, to increase the accuracy of these algorithms, either by including disease-specific determinants of fracture, or even BMD parameters like trabecular bone score (TBS) that can somehow quantify the quality of the evaluated bone. ## References 1. Kanis JA, Norton N, Harvey NC. SCOPE 2021: a new scorecard for osteoporosis in Europe. Arch Osteoporos (2021) 16 82. PMID: 34080059 2. Ferrari SL, Abrahamsen B, Napoli N. Diagnosis and management of bone fragility in diabetes: an emerging challenge. Osteoporos Int (2018) 29 2585-2596. PMID: 30066131 3. Miyake H, Kanazawa I, Sugimoto T. Association of bone mineral density, bone turnover markers, and vertebral fractures with all-cause mortality in type 2 diabetes mellitus. Calcif Tissue Int (2018) 102 1-13. PMID: 28965188 4. Rodrigues AM, Canhão H, Marques A. Portuguese recommendations for the prevention, diagnosis and management of primary osteoporosis - 2018 update. Acta Reumatol Port (2018) 43 10-31. PMID: 29602163 5. Carnevale V, Morano S, Fontana A. Assessment of fracture risk by the FRAX algorithm in men and women with and without type 2 diabetes mellitus: a cross-sectional study. Diabetes Metab Res Rev (2014) 30 313-322. PMID: 24420974 6. Bhattoa HP, Onyeka U, Kalina E, Balogh A, Paragh G, Antal-Szalmas P, Kaplar M. Bone metabolism and the 10-year probability of hip fracture and a major osteoporotic fracture using the country-specific FRAX algorithm in men over 50 years of age with type 2 diabetes mellitus: a case-control study. Clin Rheumatol (2013) 32 1161-1167. PMID: 23588883 7. Wang C, Liu J, Xiao L. Comparison of FRAX in postmenopausal Asian women with and without type 2 diabetes mellitus: a retrospective observational study. J Int Med Res (2020) 48 300060519879591. PMID: 31617425 8. Alfadhli EM, Alsharif AS, Alharbi RA, Alalawi SS, Darandari SE, Alsaedi SA, Alharbi SO. Comparison of bone mineral density and Fracture Risk Assessment Tool in Saudi women with and without type 2 diabetes mellitus: a cross-sectional study. Saudi Med J (2022) 43 700-707. PMID: 35830997 9. Valentini A, Cianfarani MA, De Meo L. FRAX tool in type 2 diabetic subjects: the use of HbA(1c) in estimating fracture risk. Acta Diabetol (2018) 55 1043-1050. PMID: 29980834 10. Hu L, Li T, Zou Y, Yin XL, Gan H. The clinical value of the RA-adjusted fracture risk assessment tool in the fracture risk prediction of patients with type 2 diabetes mellitus in China. Int J Gen Med (2021) 14 327-333. PMID: 33542649 11. Marques A, Lourenço Ó, Ortsäter G, Borgström F, Kanis JA, da Silva JA. Cost-effectiveness of intervention thresholds for the treatment of osteoporosis based on FRAX(®) in Portugal. Calcif Tissue Int (2016) 99 131-141. PMID: 27016370 12. Bonaccorsi G, Fila E, Messina C. Comparison of trabecular bone score and hip structural analysis with FRAX(®) in postmenopausal women with type 2 diabetes mellitus. Aging Clin Exp Res (2017) 29 951-957. PMID: 27722900 13. Majumdar SR, Leslie WD, Lix LM. Longer duration of diabetes strongly impacts fracture risk assessment: the Manitoba BMD cohort. J Clin Endocrinol Metab (2016) 101 4489-4496. PMID: 27603908 14. Conway BN, Long DM, Figaro MK, May ME. Glycemic control and fracture risk in elderly patients with diabetes. Diabetes Res Clin Pract (2016) 115 47-53. PMID: 27242122 15. Li CI, Liu CS, Lin WY. Glycated hemoglobin level and risk of hip fracture in older people with type 2 diabetes: a competing risk analysis of taiwan diabetes cohort study. J Bone Miner Res (2015) 30 1338-1346. PMID: 25598134 16. Leslie WD, Lix LM, Prior HJ, Derksen S, Metge C, O'Neil J. Biphasic fracture risk in diabetes: a population-based study. Bone (2007) 40 1595-1601. PMID: 17392047 17. Schwartz AV, Vittinghoff E, Sellmeyer DE. Diabetes-related complications, glycemic control, and falls in older adults. Diabetes Care (2008) 31 391-396. PMID: 18056893 18. Gilbert MP, Pratley RE. The impact of diabetes and diabetes medications on bone health. Endocr Rev (2015) 36 194-213. PMID: 25738213 19. Schwartz AV. Diabetes, bone and glucose-lowering agents: clinical outcomes. Diabetologia (2017) 60 1170-1179. PMID: 28451714 20. Watts NB, Bilezikian JP, Usiskin K, Edwards R, Desai M, Law G, Meininger G. Effects of canagliflozin on fracture risk in patients with type 2 diabetes mellitus. J Clin Endocrinol Metab (2016) 101 157-166. PMID: 26580237 21. Zhao Y, Kachroo S, Kawabata H. Association between hypoglycemia and fall-related fractures and health care utilization in older veterans with type 2 diabetes. Endocr Pract (2016) 22 196-204. PMID: 26492544 22. Schwartz AV, Vittinghoff E, Bauer DC. Association of BMD and FRAX score with risk of fracture in older adults with type 2 diabetes. JAMA (2011) 305 2184-2192. PMID: 21632482 23. Giangregorio LM, Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, Kanis JA. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res (2012) 27 301-308. PMID: 22052532
--- title: 'In Vivo Toxicological Analysis of MnFe2O4@poly(tBGE-alt-PA) Composite as a Hybrid Nanomaterial for Possible Biomedical Use' authors: - Rohit Kumar - Samir Bauri - Soumyamitra Sahu - Shaily Chauhan - Sunny Dholpuria - Janne Ruokolainen - Kavindra Kumar Kesari - Monalisa Mishra - Piyush Kumar Gupta journal: ACS Applied Bio Materials year: 2023 pmcid: PMC10031559 doi: 10.1021/acsabm.2c00983 license: CC BY 4.0 --- # In Vivo Toxicological Analysis of MnFe2O4@poly(tBGE-alt-PA) Composite as a Hybrid Nanomaterial for Possible Biomedical Use ## Abstract Nanocomposites have significantly contributed to biomedical science due to less aggregation behavior and enhanced physicochemical properties. This study synthesized a MnFe2O4@poly(tBGE-alt-PA) nanocomposite for the first time and physicochemically characterized it. The obtained hybrid nanomaterial was tested in vivo for its toxicological properties before use in drug delivery, tissue engineering fields, and environmental applications. The composite was biocompatible with mouse fibroblast cells and hemocompatible with $2\%$ RBC suspension. This nanocomposite was tested on Drosophila melanogaster due to its small size, well-sequenced genome, and low cost of testing. The larvae’s crawling speed and direction were measured after feeding. No abnormal path and altered crawling pattern indicated the nonappearance of abnormal neurological disorder in the larva. The gut organ toxicity was further analyzed using DAPI and DCFH-DA dye to examine the structural anomalies. No apoptosis and necrosis were observed in the gut of the fruit fly. Next, adult flies were examined for phenotypic anomalies after their pupal phases emerged. No defects in the phenotypes, including the eye, wings, abdomen, and bristles, were found in our study. Based on these observations, the MnFe2O4@poly(tBGE-alt-PA) composite may be used for various biomedical and environmental applications. ## Introduction Metallopolymer nanocomposites are hybrid nanomaterials that combine metal component’s, electrical, optical, catalytic, and thermal properties with the polymer component’s flexibility, solubility, and manufacturability.1 These nanomaterials have been broadly utilized in biomedical applications due to their superior properties. Among the polymer components, polyesters have been used extensively in metallopolymer nanocomposite fabrication due to their better amphiphilicity, good biocompatibility, improved biodegradability, protection from UV radiation, and cellular enzymes.2 Furthermore, the metal component comprises metal/metal oxide nanoparticles like ZnO, Fe3O4, Fe2O4, CuO, MgO, ZnFe2O4, MnFe2O4, NiFe2O4, CoFe2O4, AgO2, and so on. These nanoparticles have been developed via different chemical and biological procedures. They exhibited significant role in the degradation of various inorganic/organic pollutants for environmental remediation and have been further utilized for therapeutic effects in various disease treatments.3−5 Further, in vitro cytotoxicity assays showed anti-cancer activity of biogenic MnFe2O4 nanoparticles against lung, breast, and skin cancer cells.6 Next, the polymer component of the metallopolymer nanocomposite also requires a cost-effective synthesis approach to reduce the usage of excess organic solvents and eliminate the postpurification step. Based on this, in a report,7,8 a low-molecular-weight metal-free semiaromatic alternating polyester [poly(tBGE-alt-PA) copolymer] was synthesized in one-pot step via anionic ring-opening copolymerization reaction.7,8 This copolymer was used to fabricate nanodrug carriers for combinatorial drug delivery of both doxorubicin and curcumin. Then, the rationale for developing such metallopolymer nanocomposites stands due to the slower degradation profile, lesser bioavailability, and agglomeration behavior of metal/metal oxide nanoparticles.9 By overcoming these physicochemical properties, metallopolymer nanocomposites have been used in drug delivery,10 biosensing, bioimaging, bioelectronics, and environmental remediation applications.11 Concerns regarding toxicological impacts on the human health and the environment have arisen in response to the expanding usage of nanoparticles worldwide in recent years. That is why it is always important to consider a nanomaterial’s potential risks and benefits before using it.12 Generally, the toxicity of newly developed nanoparticles/nanomaterials is majorly influenced by their physicochemical properties such as [1] the plasmonic properties, [2] coating on their surface, [3] particle size, [4] net surface charge, [5] shape/morphology, and finally [7] phase stability. Therefore, in the past few years, the nanotoxicology field has gained a lot of interest among material scientists, nanotechnologists, biomedical scientists, innovators, and entrepreneurs.12 However, two major significant factors have led to rapid progress in this area. The first factor is the large-scale production of nanomaterials with disturbing physical and chemical properties, and the second is the constant development of nanomaterials has stirred interdisciplinary research.13,14 For instance, nanomaterials have made massive progress in biomedicine. Nanomaterials have a large surface area-to-volume ratio, so they may display unpredictable interactions with cells and tissues. Several research studies have shown that nanomaterials exhibit highly complex interactions with cells and the environment.12 Many strategies to study the toxicity of nanomaterials are in progress. In the 20th century, it was shown that materials on a micrometer scale did not show any toxicity,15 but nanoscale materials might exhibit some toxic effects.16,17 The toxicity of nanomaterials can be studied in cell culture (in vitro) and in living organisms (in vivo) such as fish, flies, Drosophila melanogaster, mice, or rats. In our previous experiments, we synthesized a bimetallic-semiaromatic polyester hybrid nanocomposite.18 The physicochemical properties of the synthesized MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite were studied and reported previously. We later found that the nanocomposite was both biocompatible and hemocompatible in nature.18 In this work, we have evaluated the genotoxic and cytotoxic analysis of the newly developed metallo-polyester nanocomposites made up of MnFe2O4 nanoparticles and poly(tBGE-alt-PA) copolymers on the model organism D. melanogaster. For more than a century, scientists have used the fruit fly D. melanogaster as a laboratory organism to investigate a wide range of aspects of biology, such as heredity effects, aging, learning, behavior, and embryonic development.19,20 Developmental time points are mainly influenced by the environmental cycles, and reproduction ability may be changed. Disease-causing genes of D. melanogaster have almost $75\%$ of homology with humans; that is why it is used as a genetic model to research various types of human diseases such as cancer, cardiovascular disease, and sleeping disease.20,21 ## Synthesis and Characterization of MnFe2O4@poly(tBGE-alt-PA) Nanocomposite MnFe2O4@poly(tBGE-alt-PA) nanocomposite was synthesized and reported in our previous study.18 In addition to the synthesis, we also characterized the physicochemical properties of the MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite in our earlier paper.18 FTIR spectroscopy demonstrated successful hybrid nanocomposite synthesis. X-ray diffraction technology characterized the crystal structure of the hybrid nanocomposite. A thermogravimetric analysis instrument was used to investigate the thermal stability of the hybrid nanocomposite in a nitrogen environment.18 The surface topology of the hybrid nanocomposite was studied through field emission scanning electron microscopy.18 Using an MTT assay with mouse fibroblast cells (NIH3T3), the biocompatibility of the MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite was evaluated.18 The hemocompatiblity of the MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite was studied by the in vitro hemolysis test.18 ## Fly Strain and Culture Condition All experiments were accomplished with the Oregon R strain of D. melanogaster. The flies were reared on standard fly food made of type I agar, sucrose (150 μM), corn meal, and yeast.22,23 Propionic acid and methyl-paraben were added to the food to protect them from fungal and microbial contamination.22 The flies were released to fresh food vials in a ratio of 5:3 (females and males, respectively). They were nurtured under optimum conditions of $60\%$ relative humidity, 25 °C constant temperature, and 12 h day–night condition.22,23 At first, a stock solution of 2.5 mM concentration of MnFe2O4@poly(tBGE-alt-PA) was made by mixing the nanocomposite in powdered form in Mili Q water (7732185, SRL Chemicals, India), and the solution was stored at 4 °C. Fly food was prepared and divided into control and treatment. In the control food, no nanocomposite solution was added, whereas in the treatment food, different volumes of stock solutions were added to achieve various MnFe2O4@poly(tBGE-alt-PA) concentrations (50, 100, and 200 μM). Once the food was solidified, adult Oregon-R flies were freed to each vial. Larvae in their third instar of development and adult flies were employed extensively in the research.22,23 Adult flies fertilized with both control and treatment foods and laid eggs. The larvae were utilized in the experiment just 4 to 5 days after hatching.24,25 ## Evaluation of Cytotoxicity and Genotoxicity of MnFe2O4@poly(tBGE-alt-PA) on Larval Gut Larvae that had been fed MnFe2O4@poly(tBGE-alt-PA) were analyzed. Following the protocol of Priyadarshini et al. 2020, the larval gut was extracted and costained with dichloro-dihydro-fluorescein diacetate (DCFH-DA) (D6883, Sigma-Aldrich, Merck, Germany) and 4′,6-diamidino-2-phenylindole (DAPI) (D9542, Sigma-Aldrich, Merck, Germany).22 Cell nuclei are stained with DAPI (D9542, Sigma-Aldrich, Merck, Germany), whereas ROS (reactive oxygen species) produced by mitochondria was stained with DCFH-DA. Disintegrated nuclei were counted and represented against different concentrations of the nanocomposite fed to Drosophila. To determine the level of cellular stress caused by the nanocomposite treatment, a graph was also constructed showing the concentration against the intensity of DCFH-DA (D6883, Sigma-Aldrich, Merck, Germany). Following the protocol of Bag et al., we stained the intestines with trypan blue dye (93595, Sigma-Aldrich, Merck, Germany) to look for signs of membrane disruption caused by MnFe2O4@poly(tBGE-alt-PA) treatment.26 ## Measurements of Oxidative Stress after MnFe2O4@poly(tBGE-alt-PA) Treatment on Larvae Haemolymph collected from larvae in their third instar was utilized to measure oxidative stress. Briefly, 25 numbers of third instar larvae were collected. The larvae were cooled in a box and pricked near the thorax to stop melanization. Centrifugation of larvae was performed at 4 °C for 10 min at 4500 rpm (Eppendorf-centrifugation $\frac{5430}{5430}$R, Germany). 5 μL of hemolymph was taken in an Eppendorf tube of 1.5 mL, and 10 μL of 1X phosphate-buffered saline (PBS) was added to the tube. An equal volume of 1.6 mM nitroblue tetrazolium (NBT) solution (11383213001, Sigma-Aldrich, Merck, Germany) was added to the mixture and left for 1 h in the dark. NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay was performed on the hemolymph according to the protocols of Nayak et al. 2020 and Bag et al. 2020.26,27 NBT (11383213001, Sigma-Aldrich, Merck, Germany) (1.6 M) solution was given to the hemolymph and left for 1 h in the dark. The reaction was stopped after 1 h by adding an equivalent amount of $100\%$ glacial acetic acid (GAA) (A6283, Sigma-Aldrich, Merck, Germany) and incubating for 5 min. Then, 150 μL of $50\%$ GAA (A6283, Sigma-Aldrich, Merck, Germany) was mixed, and 200 μL of the solution was poured in the well of a 96-well plate, and the absorbance was taken at 595 nm with the help of a microplate reader (Elisa Biobase, EL10A). ## Larvae Crawling Behavior Assay Larval movement patterns show how neuronal damage is caused by hazardous chemicals and various materials used for toxicology analysis on D. melanogaster. Larvae have a characteristic rhythmic crawling movement; they travel in a straight line at a constant rate most of the time. Changes in how one crawls are a significant sign of neural malformation. The crawling assay was done with five third instar larvae from each treatment concentration (50, 100, and 200 μM) of MnFe2O4@poly(tBGE-alt-PA) nanocomposite and control.28 Larvae were isolated from the food and washed in 1X PBS to clear the food particles. $2\%$ agar-containing Petri plates were made as the crawling surface.29 Initially, the larvae were put on an agar plate to adapt to that environment. One by one, the larvae were picked to the center of a different agar plate and placed on a graph paper to observe their crawling path. In the meantime, the video was recorded (Canon EOS 3000D, Japan). The time taken by each larva to reach the periphery of the Petri plate was measured, and that time was divided by 1 min to calculate the crawling speed. On the agar gel, the larvae left a trailing impression of their crawling path. Markers were used to sketch the larvae’s crawling routes, and their average speed per second was then plotted. ## Trypan Blue Staining Third instar larvae were stained with Trypan blue (93595, Sigma-Aldrich, Merck, Germany) following a reported protocol.22 We placed 10 larvae from each group (control and each treatment) into a 0.5 mL centrifuge tube. Before the experiment, the larvae were collected and washed thoroughly in PBS (1 X) to eliminate any leftover feeding particles. All larvae were submerged into the trypan blue (93595, Sigma-Aldrich, Merck, Germany) and placed in a dark place for 45 min at room temperature (RT). After 45 min, the larvae were washed in PBS solution to remove any trace of color consumed or left on their surface. After imaging the larvae using a stereomicroscope (ACCU-SCOPE Inc., Commack, New York), we looked for signs of cell damage.22,30 ## Touch Sensitivity Study Various organs, including the nervous system, different body parts, and neuromuscular junctions, work together to produce touch. Central pattern generators (CPGs), located in the brain, are the source of stimuli. Even without outside sensory input, the oscillatory network continues to move. However, without a peripheral nervous system (PNS) stimulus loop, the body’s segmentation expands and contracts uncoordinatedly. Signaling from the CPG that initiates peristaltic movement begins in the late embryonic stage and persists throughout the larval stage. The chordotonal organ of the PNS receives a signal for sensing and movement from the CPG.31,32 Any sensory impairment thus impairs the larvae’s ability to respond to stimuli. Larval behavior is examined, and the neural defect can be scored. The exact path has been followed for isolating the larva, washing, and acclimatization in the agar plate environment. The thoracic region of the larva was gently pricked with an eyelash glued to a toothpick which acts as mechanical stimuli. The responses of the larvae were noted and scored according to Dhar et al. 2020.29 ## Climbing Assay Climbing is an innate behavior of Drosophila. Drosophila always tries to climb vertically against gravity, so they showed negative geotactic behavior. Climbing reflects the neurodegeneration in the Drosophila model. Adult fruit flies’ locomotory behavior was evaluated using this same technique as in a reported protocol.22 3-day old flies (30 adult flies) were moved to the climbing apparatus from three distinct concentrations.33,34 Flies were taped gently to the bottom of the vial, and the duration of 10 s to climb 16 cm of the tube was recorded. All concentrations of the nanocomposite and controls were tested five times using this methodology. Percentages of total flies were used to determine the number of flies in each group that successfully climbed the mark of 16 cm in the time of 10 s.35 The climbing experiment describes the behavioral changes that occur in flies in response to gravity. The number of flies that could ascend to 16 cm in the 10 s is used to analyze this test. In due order, the number of flies that could climb up to 16 cm was normalized to $100\%$. The assay was performed six times ($$n = 6$$) for each concentration, including control. In the control flies vial, 62.78 ± $3.03\%$ were able to climb, whereas 56.11 ± $1.59\%$ in 50 μM, 58.33 ± $2.55\%$ in 100 μM, and 68.33 ± $3.31\%$ in 200 μM were able to climb up to the 16 cm mark. The result of the climbing assay is plotted in a graph shown in Figure 5. The climbing ability was not significantly changed in the treated flies compared to the control flies of the setup.22,49 Figure 5: Climbing assay in each nanocomposite treated setup comparable with control flies; behavioral abnormality was not significantly found with increases up to 200 μM concentration. ## Survivability Study Toxicology was evaluated using the same method described by Ales Panacek et al. 2011. Flies were fed with various concentrations of (50, 100, and 200 μM) MnFe2O4@poly(tBGE-alt-PA), including control, and they laid their eggs on the food. However, the results vary depending on the concentration of the nanocomposite. Each vial was labeled with a symbol for a developing fly egg, and daily counts of the hatched flies were recorded. The proportion of surviving flies in each concentration was used to create the graph.36,37 This assay determines the number of survival days of flies that successfully undergo eclosion from the pupal stages. Fifty freshly emerging flies (25 males and 25 females/vial and five vials/group) were put to standard food with or without nanocomposite treatment (50, 100, and 200 μM) and control. On alternating days, the surviving flies were switched to fresh food containing MnFe2O4@poly(tBGE-alt-PA) and control (without nanocomposite treatment). Dead flies were counted every day until the final fly perished. There were no significant differences found in control, 50, and 200 μM concentrations [56.60 ± 1.44, 58.80 ± 1.28, and 58.20 ± 1.02 days ($$n = 5$$)], whereas the 100 μM concentration increased the survivability of hatched flies to 61.60 ± 1.44 days ($$n = 5$$), as shown in Figure 6A,B).36,37,50 Figure 6: Survivability assay. (A) Percentage survival. (B) Mean survival for flies fed on standard and nanocomposite mixed food. No significant survivability differences found in control, 50, and 200 μM concentrations of the nanocomposite, whereas the 100 μM concentration of nanocomposite increases the survivability of flies. ## Average Body Weight Analysis Thirty adult flies (15 males + 15 females) were sampled from each concentration shortly after hatching, and their weight was compared with that of the control group.22 ## Larval Light Preference Assay This experiment detected an early photoreceptor deficiency using the approach described by Sabat et al. 2016.38 A Petri dish was divided into four quadrants, with the opposite quadrant being colored black (two quadrants are black). Then, $1\%$ agarose was added and let to set. Fifteen third instar larvae from both the control and treatment vials were kept in the dark for 6 h before the experiment began. The larvae were placed on the agar plate, and the lid with the same marking as the Petri plate was closed. The Petri dish was illuminated uniformly, and the larvae were given 5 min to move freely between the dark and light sections. After 5 min, we removed the lid and tallied the larvae in each section. Each batch of larvae performed the test three times, and the experiment was conducted in three sets.22,38,39 ## SEM and EDS Scanning Three guts were separated from the third instar larva of each concentration and stored at 4 °C in $4\%$ paraformaldehyde (PFA) (158127, Sigma-Aldrich, Merck, Germany). To eliminate the extra PFA (158127, Sigma-Aldrich, Merck, Germany), the guts were rinsed with PBS. The guts were dehydrated using a graded serial dehydration method that involved increasing the percentage of ethyl alcohol (1.00983, Sigma-Aldrich, Merck, Germany). The concentrations of the ethyl alcohol used were 30, 50, 70, 90, and $100\%$. The desiccated guts were mounted on a slide containing carbon tape and then punctured in the midgut region to expose the gut lumen. Scanning electron microscopy (SEM) (JEOL JSM-6480LV) analysis followed by coating the samples with platinum. The quantity of manganese and iron was determined by energy-dispersive spectroscopy (EDS) analysis.40,41 ## Phenotype Observation The nanomaterial’s character was examined by checking phenotypes to determine whether it benefits or harms the model organisms. Fifty adult flies were screened for phenotypes in their eyes, wings, bristles, and abdomens. The images were taken with a stereo microscope (Motic SMZ-171).42,43 The adult phenotypic analysis showed no defects in the eyes, wings, bristles, or abdomens. Wing venation pattern, eye coloring, thorax bristle count, and abdomen structure were all monitored to see if there were any phenotypic alterations due to the treatment with the nanocomposite. No remarkable difference or defect in the first-generation D. melanogaster treated and control groups is shown in Figure 10. Figure 10: Eyes, wings, abdomen, bristles phenotype. In control (A,E,I,M) and treated concentrations of 50 μM (B,F,J,N), 100 μM (C,G,K,O), and 200 μM (D,H,L,P), no remarkable difference or defect has been found in first-generation D. melanogaster. ## Statistical Analysis With the help of a software GraphPad Prism 9.0, we analyzed all experimental data. Using the significance $P \leq 0.05$, $P \leq 0.01$, and $P \leq 0.001$ from unpaired two-tailed student t-test, the data were interpreted with the mean ± SEM values. ## Synthesis and Physicochemical Characterization of MnFe2O4@poly(tBGE-alt-PA) Nanocomposite The MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite was fabricated and studied for the first time in our prior work.18 No chemical interactions were seen between the copolymer and the produced hybrid nanocomposite; it was found to be crystalline and thermostable.18 MnFe2O4@poly(tBGE-alt-PA) hybrid nanocomposite has a net negative charge on their outermost layer. Further, the nanocomposite was hemocompatible and biocompatible.18 ## Cytotoxicity and Genotoxicity Analyses of MnFe2O4@poly(tBGE-alt-PA) on Larval Gut The flies were fed with the nanocomposite, which passed through the intestine and interacted directly with the epithelial cells of the intestine. The toxicity of the cells was examined to scrutinize whether the nanocomposite induced any damage to the cells. DAPI (D9542, Sigma-Aldrich, Merck, Germany) was used to identify deoxyribonucleic acid (DNA) damage after exposure of the cells to any toxic compound. The DNA damage observed was insignificant in the nanocomposite treatment shown in Figure 1. The graph shows the number of micronuclei formation due to DNA breakdowns in the treated groups (Figure 1). After internal staining of the larval gut by DCFH-DA (D6883, Sigma-Aldrich, Merck, Germany), it was observed that the MnFe2O4@poly(tBGE-alt-PA) nanocomposite could reduce the internal ROS activities shown in Figure 1. This finding was supported by the NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay, suggesting that the nanocomposite helps reduce ROS and protects the cell from oxidative stress.34,44 Figure 1:** DAPI and DCFH-DA staining of the larval gut. (a-l) Representation of the number of micronuclei formation due to DNA breakdown of control and treatment groups with the help of micronuclei counting. (m) No significant DNA damage was observed in 50 and 100 μM of concentrations, although it was measured significant damage at higher concentration (200 μM) of nanocomposites treatment. ## ROS Analysis An NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay was performed in the third instar larval hemolymph to measure the amount of intracellular ROS from the NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay. ROS formation reduced significantly in the nanocomposite-treated group compared to the control. Thus, the NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay suggests that the MnFe2O4@poly(tBGE-alt-PA) nanocomposite plays a vital role in ROS scavenging41,45 as shown in Figure 2. In control, the absorbance value at 595 nm was found to be 0.4731 ± 0.38. In 50 μM concentration, the value decreased to 0.3733 ± 0.007. In 100 μM concentration, the absorbance was further decreased to 0.2611 ± 0.032, and for 200 μM concentration, the absorbance was 0.1631 ± 0.011. The absorbance of the NBT (11383213001, Sigma-Aldrich, Merck, Germany) assay is directly proportional to the quantity of ROS generated, which ultimately correlates with the level of oxidative damage to the cells.46 The amount of ROS reduction that occurs compared to the control is represented in the graph (Figure 2). Figure 2: The mean interaction plot of the NBT assay of larvae shows that increases in the concentration of the MnFe2O4@poly(tBGE-alt-PA) nanocomposite play a vital role in reducing the ROS and protect the cell from oxidative stress. ## Crawling Assay The crawling behavioral test is a more practical assay to explore the neuronal abnormalities in an early stage of larva for the neuronal mechanosensory investigation. The crawling behavior of third instar larvae was studied in the Drosophila model. The neuronal toxicity caused by the NP exposure can disrupt the coordinated crawling of larvae. The healthy larvae move in a straight line, whereas the abnormal ones zigzag and sometimes slow down. Thus, the crawling assay in larvae is preferable for identifying abnormalities in gene expression that might result in fatalities during the pupal and adult stages. In the crawling assay, no distinct curve or turn has been recorded for the control larvae. There was no significant crawling path change for the treatment concentrations of 50, 100, and 200 μM. In the control vial of larvae, 1.101 ± $0.082\%$ were able to cover the distance in mm/s, whereas 1.219 ± $0.133\%$ were able to cover the distance in 50 μM, 1.119 ± $0.078\%$ in 100 μM, and 1.199 ± $0.151\%$ in 200 μM. The crawling speed of third instar larvae shows that all treated larva significantly covers the same distance in mm/s comparable to control larvae. The larvae tracking paths and the crawling speed plot is demonstrated in Figure 3. Figure 3: Genotoxicity studies in the larvae of Drosophila melanogaster. The mean interaction plot of the larval crawling speed of (A) Control and (B,C, and D) 50, 100, and 200 μM, respectively, in all treated groups. Larval crawling path and speed did not show significant changes in various treated concentrations comparable to control larval groups. ## Living and Nonliving Cell Analysis The use of trypan blue (93595, Sigma-Aldrich, Merck, Germany) staining allows for the differentiation of living cells from nonliving ones. Positive trypan blue (93595, Sigma-Aldrich, Merck, Germany) staining is not observed in any concentration of the nanocomposite that was given to the food vial, indicating nondamage to the gut’s inner layer.47 Even at higher concentrations of MnFe2O4@poly(tBGE-alt-PA), 200 μM, no significant toxicity was seen in the Drosophila’s larval stages, which are in a voracious feeding stage as shown in Figure 4.48 Figure 4: Trypan blue exclusion assay executed in third instar larvae and the effect of the nanocomposite in the third instar larval stage (trypan blue, used as a marker for dead cells, also detects the presence of any tissue damage). (A) Larvae from the control show no sign of cell or tissue damage. Also, (B) 50, (C)100, and (D)200 μM nanocomposite-treated larvae did not show any internal gut damage. ## Adult’s Average Body Weight Analysis The weight of adult flies was determined from several treatment and control vials to assess body growth and size. Thirty treated and control flies were weighed (15 males and 15 females). Then, the average weight of a single fly in each group was calculated and found to be 1.076 ± 0.052 mg in the control group, whereas in 50 μM treated concentration, it was 1.117 ± 0.036 mg. Likewise, the weight of a single fly in 100 μM treated concentration was 1.083 ± 0.022 mg. For 200 μM, the value was 1.068 ± 0.032 mg. In the body weight of the adult fly, no remarkable difference or defect has been found in the treated and the control groups. Treatment concentrations of MnFe2O4@poly(tBGE-alt-PA) (50, 100, and 200 μM) did not show a decrease in the average weight of the fly at various concentrations. A graph was plotted to represent the body weight, as shown in Figure 7A.22,51 Figure 7: (A) Weight of adult flies: 30 treated and control flies were weighed (15 males and 15 females). The average weight of a single fly in each group is plotted to its corresponding concentration and compared with the control. (B) Graph showing touch sensitivity score. Increasing the nanocomposite concentration up to 200 μM affect larval reflexes or neuronal development. A significant () differences were observed in touch sensitive score of treated flies at 100 μM and 200 μM as compared to control. ## Touch Sensitivity Test A sensation of touch is another fundamental activity of animals, with implications for anything from learning about their surroundings to interacting with others. No mechanoreceptor potential C (NOMPC) is a subset of the MYOC gene family that mediates mechanical stimuli into electrical signals,46 making it a key player in the process of feeling touch.28,52 In our experiment, we found that at 50 μM concentration of nanocomposite treatment, the larvae’s touch sensitivity score was 2.93 ± 0.18, which was practically identical to the control group’s score that was 2.40 ± 0.12 (the scores for both groups were between 2 and 3, indicating that the larvae hold their movement before moving forward). Similarly, at 100 and 200 μM concentrations, the touch sensitivity score was 3.20 ± 0.12 for both the cases (the scores for both groups were more than 3 but below 4, indicating that the larvae turned 90° and then moved), as shown in Figure 7B.29 Increasing the nanocomposite concentration up to 200 μM did affect larval reflexes or neuronal development. ## Larval Light/Dark Preference Assay The larva’s light preference test was done to look for any early defects in the light-sensing neurons. In this experiment, the percentage of larvae attracted to light increased as the concentration of nanocomposite treatment increased. The control group’s percentage of larvae attracted to light was 42.22 ± $1.29\%$. There were 44.56 ± $1.39\%$ of light-sensitive larvae in 50 μM, 51.62 ± $1.38\%$ in 100 μM, 54.60 ± $1.60\%$ in 200 μM, as shown in Figure 8.25,39 However, light was avoided or dark was preferred by 57 ± $1.29\%$ larvae from the control group, 55.44 ± $1.39\%$ in 50 μM, 48.38 ± 1.38, and 45.40 ± $1.60\%$ in the case of 200 μM hybrid nanocomposite-treated larval groups.53 Figure 8: Light/dark preference assay of larva, Petri dishes. (A). Control and (B–D). 50, 100, and 200 μM doses of the hybrid nanocomposite-treated larval groups, respectively, (E). Graph of the light/dark preference test (N = 12 (180 larvae) per time point). ## Analysis of the Presence of Elements in the Larval Midgut by SEM-EDS To verify the larva’s consumption of MnFe2O4@poly(tBGE-alt-PA), the gut of the third instar larva was examined using SEM/EDS. Each experimental setup’s larval midgut was taken out and analyzed for elementary deposition using SEM (JEOL JSM-6480LV), as shown in Figure 9A–D. The EDS findings verified that the MnFe2O4@poly(tBGE-alt-PA)-treated larval gut had a larger proportion of manganese and iron deposition than the untreated control gut. There was very less amount of iron and no traces of manganese found in the gut of untreated larvae. However, the percentage of manganese and iron increased according to the increasing concentration of MnFe2O4@poly(tBGE-alt-PA) treatment. This finding supports that no toxicity is induced by the nanocomposite and confirms the deposition of the hybrid nanocomposite in the gut.54 Figure 9: Elemental analysis of the larval gut indicates nanocomposite deposition within the gut of treated larvae (B, C, and D). In contrast, no nanocomposite traces were found in the gut of the control (A). ## Discussion A major cause for concern is the increasing number of biological uses for nanomaterials, which might present significant risks to human health.55 Several studies have demonstrated that nanomaterials’ size, shape, and structure significantly alter the actions and responses of living organisms.56 Due to the sequence completion of the human and Drosophila genomes, many loss-of-function experiments have been streamlined, providing us with a fundamental understanding of the genes involved in many diseases. Drug development may be advantageous due to the in vivo model organism, D. melanogaster, and a smaller gene family since fewer genes need to be controlled to produce acute circumstances for drug screening.57 Moreover, other NPs, such as ferrous, manganese, zinc oxide, and silica, influence the neurons by inhibiting neuronal function via dopamine depletion, increasing heat stress, and causing edema development. Increases in dosage, concentration, and particle size affect somatosensory neurons in the dorsal root ganglia.58 In the current study, the synthesized and physicochemically characterized nanocomposite was administered through the food immediately after hatching from the eggs, and the larva started feeding treated food. After being ingested, this nanocomposite did not alter larval behavior or produce any abnormal phenotypes. When fed with the nanocomposite, the larvae exhibited no signs of neural dysfunction. In this investigation, no larval fatality or aberrant behaviors were identified; nevertheless, if larvae struggle to survive the stress during larval stages, the deficiency appears to be seen in the adult stage within the disrupted developmental process during metamorphosis. The larval crawling experiment is used as a method for evaluating neuronal activity. Experimental studies on crawling larvae show that increasing MnFe2O4@poly(tBGE-alt-PA) concentrations did not affect their crawling speed. The crawling assay shows that increasing MnFe2O4@poly(tBGE-alt-PA) concentrations did not affect their crawling.28 The larvae treated with MnFe2O4@poly(tBGE-alt-PA) showed no signs of confusion, as shown by the lack of abrupt turns or decreased crawling speed in their tracks. Trypan blue staining was carried out to analyze the extent of the cellular damage. Trypan blue staining indicates the number of dead cells in the digestive tract after treatment with several concentrations of MnFe2O4@poly(tBGE-alt-PA).22,30 The Drosophila’s larval stages, which are at a voracious feeding stage, showed no signs of toxicity even when exposed to MnFe2O4@poly(tBGE-alt-PA) at concentrations as high as 200 μM. The larval intestine was examined using a scanning electron microscope to see if MnFe2O4@poly(tBGE-alt-PA) had any impact on cellular pathways or to check the nanocomposite deposition within the gut of treated larva. The EDS findings substantiated that the MnFe2O4@poly(tBGE-alt-PA)-treated larval gut had a larger proportion of manganese and iron deposition than the untreated control gut. The EDS results confirmed an increase in manganese and iron deposition in the MnFe2O4@poly(tBGE-alt-PA)-treated larval gut compared to the control gut. However, as the MnFe2O4@poly(tBGE-alt-PA) concentration was increased, the amount of manganese and iron also increased in the gut. These results further support that the nanocomposite does not cause any toxicity, and they verify the deposit of the hybrid nanocomposite in the gut.54 Oxidative stress is generated by the increased production of ROS carried by silver, gold, and titanium nanoparticles. One well-established technique for measuring ROS concentration is the NBT test.47 NBT was done to quantify the ROS in the treated larva compared with the control one. The absorbance was taken using a microplate reader, and the graph also measured the difference.46 When comparing the highest and lowest concentrations of MnFe2O4@poly(tBGE-alt-PA), the amount of oxidative stress produced at the lowest concentration, 50 μM, and the highest concentration, 200 μM, was considered negligible. Different phases of Drosophila development, characterized by crawling assay, climbing assay in the larvae, and phenotypic analysis, including the eye, wings, abdomen, and bristles, suggest that the MnFe2O4@poly(tBGE-alt-PA) composite has no such cytotoxicity as well as genotoxicity. The findings of this study show that our experimental design may be employed as a secure injestion system for toxicological research on various in vivo model organisms. ## Conclusion and Future Prospective The use of polymeric nanoparticles in the biomedical and environmental fields is increasing daily. Once within the body, some metallic and polymeric NPs produce ROS causing damage to Drosophila in various ways. Multiple mechanisms are engaged in response to ROS. However, if the ROS levels become too high, they can cause damage to cells, tissues, organs, and the entire body. Several fly behavioral assays and phenotypic studies showed that the nanocomposite did not affect neural activity and phenotypes at highest and lowest concentrations. MnFe2O4@poly(tBGE-alt-PA) is appropriate for many biological applications since it does not cause any genotoxic and cytotoxic effects in D. melanogaster. The current study focuses on the in vivo toxicological analysis of green synthesized nanocomposite as a possible drug delivery mechanism and various environmental applications such as wastewater treatment and nanofertilizers. The toxicity assessment of the nanocomposite in Drosophila is found to be safe and nontoxic. It needs to be deliberated on other model organisms before the extensive use in drug-delivery systems in biomedical fields. ## Compliance with Ethics Requirements This article does not contain any studies with human or animal subjects. ## References 1. Reig D. S., Hummel P., Wang Z., Rosenfeldt S., Graczykowski B., Retsch M., Fytas G.. 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--- title: Glucagon and exenatide improve contractile recovery following ischaemia/reperfusion in the isolated perfused rat heart authors: - Ross T. Lindsay - Philip Ambery - Lutz Jermutus - Andrew J. Murray journal: Physiological Reports year: 2023 pmcid: PMC10031586 doi: 10.14814/phy2.15597 license: CC BY 4.0 --- # Glucagon and exenatide improve contractile recovery following ischaemia/reperfusion in the isolated perfused rat heart ## Abstract The inotropic effects of glucagon have been recognized for many years, but it has remained unclear whether glucagon signaling is beneficial to cardiac function. We evaluated the effects of glucagon alone and in combination with the glucagon‐like peptide 1 (GLP‐1) receptor agonist exenatide in the isolated perfused rat heart. The isolated perfused rat heart was used to investigate the initial inotropic and chronotropic effects of glucagon and exenatide during aerobic perfusion, and recovery of contractile function following ischaemia/reperfusion. Glucagon, but not exenatide, elicited an acute chronotropic and inotropic response during aerobic perfusion of the rat heart. Compared with control, glucagon improved recovery of left ventricular developed pressure (LVDP) by $33\%$ ($p \leq 0.05$) and rate‐pressure product (RPP) by $66\%$ ($p \leq 0.001$) following ischaemia/reperfusion and amplified the mild recovery enhancement elicited by exenatide in a dose‐dependent manner. Glucagon shows inotropic properties in the isolated perfused rat heart and improves contractile recovery following ischaemia/reperfusion, both alone and when co‐administered with a GLP‐1 receptor agonist. Glucagon and exenatide, a GLP‐1 receptor agonist, combine to stimulate greater recovery of postischaemic contractile function in the Langendorff heart. Glucagon was inotropic and chronotropic, yet this initial effect decreased over time and did not account for the increased contractility observed postischaemia/reperfusion. Glucagon and exenatide, a glucagon‐like peptide 1 (GLP‐1) receptor agonist, combine to stimulate greater recovery of post‐ischaemic contractile function in the Langendorff heart. Glucagon was inotropic and chronotropic, yet this initial effect decreased over time and did not account for the increased contractility observed post ischaemia/reperfusion. ## INTRODUCTION In addition to its metabolic effects, glucagon has been shown to be a cardio‐stimulant, which increases heart rate and myocardial contractility (chronotropic and inotropic effects) (Ceriello et al., 2016). Successful administration of glucagon has been reported in a range of cardiovascular disorders, including heart failure and cardiogenic shock (Lvoff & Wilcken, 1972; Parmley et al., 1968; White, 1999). Lvoff and Wilcken noted the beneficial effects of administering glucagon to patients with severe ischaemic heart disease (Lvoff & Wilcken, 1972). Glucagon has also been a first‐choice treatment for β‐blocker intoxication (Rotella et al., 2020; White, 1999), although this clinical efficacy has not been assessed in a controlled clinical trial. Glucagon receptor agonism, as a key component of unimolecular incretin poly‐agonists such as glucagon‐like peptide 1 (GLP‐1)/glucagon “dual” or GLP‐1/glucose‐dependent insulinotropic polypeptide (GIP)/glucagon “triple” receptor agonists, is now being investigated in the treatment of type 2 diabetes mellitus, obesity, diabetic kidney disease, and nonalcoholic steatohepatitis (Ambery et al., 2018; Bossart et al., 2022; Jia et al., 2021; Parker et al., 2020, 2022). Glucagon receptor agonism leads to an improvement in hepatic mitochondrial function, and long‐term dual glucagon and GLP‐1 receptor agonism have positive effects on glucose control, weight management, lipid profile, and liver function (Ambery et al., 2018; Boland et al., 2020; Nahra et al., 2021). The heart has been shown to be one of the multiple tissues in the body, which express the glucagon receptor (Charron & Vuguin, 2015), albeit at a lower level than the liver or kidney. This, coupled with the inotropic and metabolic effects described, suggests that glucagon could protect the heart against ischaemia/reperfusion injury, yet studies addressing this to date have painted a mixed picture. Some studies have shown improved vasodilation postischaemia in the rat heart, and cardiac benefits in humans (Lvoff & Wilcken, 1972; Rosic et al., 2010; White, 1999). Others, using the mouse heart, suggested instead that glucagon antagonism postreperfusion may improve remodeling and ejection fraction, and that glucagon agonism may impair contractile recovery postreperfusion (Ali et al., 2015; Karwi et al., 2019). Preischaemic inotropism, seen in rats, dogs, cats, guinea pigs, and humans (Farah & Tuttle, 1960; Rodgers et al., 1981; Rotella et al., 2020; White, 1999), was not observed in these mouse studies (Ali et al., 2015), suggesting that inter‐species variation may explain the discrepancies between studies. GLP‐1 agonism has been shown to protect the heart against ischaemia/reperfusion (Aravindhan et al., 2015; Nikolaidis et al., 2004), and the GLP‐1 receptor is present in all four chambers of the heart (Baggio et al., 2018). However, GLP‐1 agonism has the effect of enhancing glycolysis while decreasing fat oxidation (Aravindhan et al., 2015), which conflicts with the healthy heart's predominant reliance on fatty acid oxidation (Neely et al., 1967). GLP‐1 agonism also lowered cAMP and increased lactate production in the left ventricle, which may potentially temper any beneficial effects (Aravindhan et al., 2015). In the liver, the combination of GLP‐1 agonism with glucagon agonism led to enhanced mitochondrial metabolism and fatty acid oxidation and the myocardial effects of combined agonism would therefore be of interest. The effect of glucagon on the ischaemic heart when administered in combination with GLP‐1 agonism has remained uninvestigated to date. Therefore, we set out to measure the effect of glucagon on postischaemic recovery of contractile function, with and without concomitant GLP‐1 receptor agonism, in the rat heart, which has been demonstrated to respond inotropically to glucagon, and which has a more positive force–frequency correlation with the human heart than the mouse heart (Milani‐Nejad & Janssen, 2014; Rodgers et al., 1981). We hypothesized (a) that glucagon administration would enhance the functional recovery of the Langendorff‐perfused rat heart following ischaemia/reperfusion, and (b) that coadministration with glucagon would augment any enhancement of functional recovery elicited by GLP‐1 receptor agonism. ## Animal studies ethical approval All experiments conformed with the UK Home Office guidelines under the Animals in Scientific Procedures Act and were approved by the University of Cambridge Animal Welfare and Ethical Review Committee. ## Materials and reagents All reagents were obtained from Sigma Aldrich unless otherwise stated. ## Heart perfusion Male Wistar rats (range, 300–350 g) were obtained from a commercial breeder (Charles River, Margate, UK), and housed in conventional cages with a normal 12‐h/12‐h light/dark photoperiod and access to normal rodent chow and water ad libitum. Rats were euthanized by rising CO2 levels, with death confirmed by cervical dislocation. Hearts were excised and perfused in the Langendorff mode with Krebs–Henseleit buffer (118 mM NaCl, 4.7 mM KCl, 1.2 mM MgSO4, 11 mM glucose, 1.3 mM CaCl2, 0.5 mM EDTA, 25 mM NaHCO3, 1.2 mM KH2PO4; pH 7.4) as previously described (Lindsay et al., 2021), and continually gassed with $95\%$ O2/$5\%$ CO2. Hearts were perfused with 250 mL recirculating buffer under a constant pressure of 100 mmHg. Temperature was maintained at 38°C, the core body temperature for a rat (Lomax, 1966), throughout the protocol. Functional parameters were measured using a PVC balloon inserted into the left ventricle. Rate‐pressure product (RPP) was calculated as the left ventricular developed pressure (LVDP) × heart rate. The ex vivo ischaemia/reperfusion protocol involved 32 min of aerobic perfusion at 100 mmHg, followed by 32 min of 0.3 mL.min─1gww─1 low‐flow ischaemia, followed by 32 min of aerobic reperfusion at 100 mmHg to assess functional recovery. Compounds (vehicle control, 40 nM glucagon, and 5 nM exenatide, alone or in combination with 40 nM or 200 nM glucagon, each $$n = 3$$ biological replicates) were administered directly into the recirculating perfusate 12 min before induction of ischaemia. The compounds remained in the buffer for the duration of the experiment. 40 nM was chosen as the glucagon concentration for investigation owing to its therapeutic effects demonstrated in hepatocytes (Boland et al., 2020), and its maximal effect on contractility in the working rat heart (Rodgers et al., 1981). 200 nM glucagon was used in combination with exenatide as antagonistic signaling pathways may have necessitated a higher concentration when agonists were administered in combination. The concentration of exenatide was 5 times higher than its half maximal effective concentration at the GLP‐1 receptor, and 16.6 times higher than a concentration previously shown to be effective in the Langendorff preparation (Darwesh et al., 2018). To investigate whether glucagon‐mediated inotropism persisted long enough to explain any improvement in functional recovery, a separate set of hearts ($$n = 6$$) were perfused for 64 min in aerobic conditions, with glucagon (40 nM) being administered via the perfusion buffer at 30 min. All results are expressed as mean [SD] (SD = standard deviation). Variance testing indicated unequal variance, therefore statistics were determined using the Welch's one‐way ANOVA and post‐hoc comparisons to test our hypotheses. ## Effect of glucagon upon contractile function and recovery All hearts displayed consistent absolute cardiac function during the 5 min before the addition of compound or vehicle, with no difference between groups by the Welch's one‐way ANOVA. Mean LVDP [SD] was 127.6 [22], 107.9 [14], 138.6 [4], 123.7 [10], and 129.8 [13] mmHg for groups administered vehicle, 40 nM glucagon, 5 nM exenatide, 5 nM exenatide plus 40 nM glucagon and 5 nM exenatide plus 200 nM glucagon, respectively. In the same group order, the mean heart rate was 280.5 [33], 294.1 [36], 292.7 [43], 308.5 [50], and 285.5 [19] bpm, respectively, while the mean RPP was 35,700 [10000], 33,900 [9000], 38,600 [4000], 38,700 [2000], and 34,900 [4000] mmHg.bpm, respectively. Administration of 40 nM glucagon during aerobic perfusion produced two notable effects. First, there was an acute inotropic effect on the heart, with a mean $84\%$, $38\%$, and $120\%$ increase in LVDP, heart rate, and RPP, respectively, compared with control (LVDP, $$p \leq 2.98$$ e−4; heart rate, ns; RPP, $$p \leq 1.79$$ e−2; Figures 1a–c). This enhancement was evident throughout the 12 min preceding the induction of ischaemia. Second, following ischaemia/reperfusion, there was a $33\%$, $49\%$, and $66\%$ improvement in the recovery of LVDP, heart rate, and RPP, respectively, in hearts administered with 40 nM glucagon compared with vehicle (LVDP, $$p \leq 2.39$$ e−3; heart rate, ns; and RPP, $$p \leq 2.55$$ e−4, respectively; Figures 1a–c). This represented >$90\%$ and $100\%$ recovery of preischaemic LVDP and RPP, respectively, compared with a $50\%$ recovery in control hearts. FIGURE 1: Effects of glucagon upon cardiac contraction and contractile recovery from ischaemia/reperfusion. (a) Percentage of pre‐administration left ventricular developed pressure, (b) heart rate (bpm), and (c) percentage of pre‐administration rate‐pressure product during aerobic perfusion and following ischaemia/reperfusion. At 12 min before the induction of ischaemia, hearts were administered either 40 nM glucagon or vehicle control. RPP was calculated as LVDP multiplied by heart rate. Significance, as determined by the Welch's ANOVA and post‐hoc Welch's correction, is denoted on the graph for the three 5‐minute periods at the end of reperfusion, and the 10 min prior to induction of ischaemia. Data are mean ± SD; n = 3 hearts per group. LVDP, left ventricular developed pressure; RPP, rate–pressure product; SD, standard deviation. ## Interaction between glucagon and exenatide The administration of 5 nM exenatide alone had no measurable inotropic or chronotropic effect in the isolated perfused rat heart (Figure 2a–c). However, following ischaemia/reperfusion, 5 nM exenatide mildly enhanced recovery of contractile function at 96 min, with $16\%$ greater recovery of LVDP relative to vehicle ($$p \leq 2.77$$ e−2; Figure 2a). While 40 nM glucagon co‐administered with 5 nM exenatide did not further enhance the contractile recovery relative to 5 nM exenatide alone, the administration of 200 nM glucagon alongside 5 nM exenatide instigated $34\%$ greater RPP recovery relative to the addition of 5 nM exenatide alone (Figure 2c, $$p \leq 2.53$$ e−2). FIGURE 2: Contractile effects of coadministration of glucagon with exenatide. (a) Percentage of pre‐administration of LVDP, (b) heart rate (bpm), and (c) percentage of pre‐administration RPP during aerobic perfusion and following ischaemia/reperfusion. At 12 min before induction of ischaemia, hearts were administered either 5 nM exenatide alone or in combination with 40 nM or 200 nM glucagon or vehicle control. RPP was calculated as LVDP multiplied by heart rate. Significance, as determined by the Welch's ANOVA and post‐hoc Welch's correction, is denoted on the graph for the three 5‐min periods at the end of reperfusion, and the 10 min prior to induction of ischaemia. Blue represents post‐hoc significance of 5 nM exenatide relative to vehicle; green represents post‐hoc significance of 200 nM glucagon plus 5 nM exenatide relative to 5 nM exenatide. (d) Represents the mean recovery of LVDP, heart rate, and RPP averaged across the 5‐min period before 96 min perfusion time. p < 0.05, p < 0.01, and p < 0.001. Red represents post‐hoc significance of 40 nM glucagon relative to vehicle, blue represents post‐hoc significance of 5 nM exenatide relative to vehicle, and green represents post‐hoc significance of 200 nM glucagon plus 5 nM exenatide relative to 5 nM exenatide. All data are mean ± SD; n = 3 hearts per group. bpm, beats per minute; LVDP, left ventricular developed pressure; RPP, rate–pressure product; SD, standard deviation. ## Contribution of Inotropism to improved functional recovery To determine whether the improved functional recovery following ischaemia/reperfusion (sections 3.1 and 3.2) might be explained by the inotropic effect of glucagon persisting postreperfusion, hearts were perfused aerobically for 64 min with glucagon (40 nM) administered at 30 min perfusion time (Figures 3a–c). The inotropic effect of glucagon persisted for <20 min, with both LVDP and RPP returning to pre‐administration levels within 12 and 20 min, respectively (Figures 3a,c). The chronotropic effect of glucagon also declined over time, though heart rate remained $16.1\%$ higher than in control hearts following 34 min (Figure 3b, $$p \leq 1.44$$ e−2). FIGURE 3: Duration of the contractile effects of glucagon. (a) LVDP (mmHg), (b) heart rate (bpm), and (c) RPP (mmHg.bpm) over 30 min aerobic perfusion without glucagon, and 34 min aerobic perfusion with 40 nM glucagon in the perfusion buffer. (d) Example trace showing left ventricular pressure (mmHg) before and after 40 nM glucagon administration. RPP was calculated as LVDP multiplied by heart rate. Exact statistical significance is annotated on the graph as relative to the 5‐min period before glucagon administration, as determined by the Student's t‐test for each subsequent 5‐min period following compound administration. Data are represented as mean ± SD; n = 6 hearts. bpm, beats per minute; LVDP, left ventricular developed pressure; RPP, rate–pressure product; SD, standard deviation. ## DISCUSSION These results show that glucagon elicited a strong inotropic response in the rat heart, both alone and when coadministrated with the GLP‐1 receptor agonist exenatide. Glucagon improved postischaemic recovery of cardiac contractility, and also enhanced the modest increase in contractile recovery mediated by GLP‐1 receptor agonism when co‐administered. This improvement of postischaemia/reperfusion function could not be accounted for by inotropic effects of glucagon persisting after ischaemia/reperfusion. In aerobically perfused hearts, the inotropic effect of glucagon ceased after 20 min, while hearts recovering from ischaemia/reperfusion still exhibited improved contractile recovery 76 min after glucagon administration. We cannot definitively exclude off‐target effects of glucagon such as cross‐talk with the GLP‐1 receptor as detailed by Selley et al. [ 2016]. However, agonism of the GLP‐1 receptor alone by exenatide did not result in any inotropism, which suggests that the effect of glucagon is not mediated via the GLP‐1 receptor. Furthermore, our study used a lower concentration of glucagon than others (Ali et al., 2015), which could be reasonably expected to limit any potential off‐target effects. GLP‐1 antagonists, or, ideally, different unimolecular agonists with known ratios of glucagon:GLP‐1 receptor agonism, could confirm this observation in the future. Our study, while limited in scope, provides important context for the field. We add to early observations of the positive inotropic effects of glucagon in dogs and rats (Farah & Tuttle, 1960; Rodgers et al., 1981). Our results also fit alongside reports of faster restoration of vasodilation postischaemia/reperfusion in isolated rat hearts administered 400 nM glucagon (Rosic et al., 2010). On the other hand, our findings contrast those of a previous study in the perfused mouse heart, which suggested that exogenous glucagon may impair contractile recovery, while glucagon receptor deletion may be protective (Ali et al., 2015). The same report observed no inotropic effect despite the use of a five‐fold higher concentration of glucagon. The differing inotropic responses from the hearts of the two rodent species may act as an indicator for how susceptible they are to glucagon‐mediated improvement of cardiac recovery post‐I/R. Rat hearts have a more positive force–frequency relationship with humans than mice do, and are better able to increase heart contractility in response to exercise (Milani‐Nejad & Janssen, 2014), which may make them better able to respond to glucagon. Further, it is possible that the improved survival reported in mice with cardiac‐specific deletion of the glucagon receptor may relate more to differing heart development than to loss of acute glucagon signaling at the time of the ex vivo ischaemic incident (Ali et al., 2015). Studies in humans have been similarly contradictory, with some demonstrating a lack of response to glucagon and others demonstrating inotropism alongside cardioprotection (Lvoff & Wilcken, 1972; Parmley et al., 1968). Goldstein et al. [ 1971] demonstrated that the responsiveness of human heart tissue to glucagon declined as heart failure progressed, which may explain some of these inconsistencies, especially since the studies reporting little response were carried out in patients with low cardiac output. In the multiple ongoing clinical trials of therapeutics with glucagon agonist function, there has been no increase in adverse cardiac events, making it unlikely that glucagon has a detrimental influence on the human heart (Parker et al., 2020, 2022). GLP‐1, glucagon, and GIP combinations are well‐established pharmacologically and exhibit effects, which could be pertinent for diseased hearts. Since the inotropic effect of glucagon does not fully account for the preserved contractile function we observed postischaemia/reperfusion, it may be that differences in their physiological signaling mechanisms underlie any synergy. GLP‐1 agonism decreased cAMP in the heart (Aravindhan et al., 2015), while glucagon has been shown to boost cardiac cAMP levels (Farah & Tuttle, 1960), so this may counteract a possible downside of GLP‐1 signaling. In the alternative scenario of the diabetic heart, where glucose metabolism is dysregulated, enhancement of glycolysis while preserving cAMP function may further help protect against ischaemia/reperfusion injury. Glucagon signaling, alone and in concert with GLP‐1 agonism, has also been shown to enhance autophagy and rejuvenation of dysfunctional mitochondrial populations (Boland et al., 2020), so it is possible that this aids recovery from ischaemia/reperfusion via removal of damaged, ROS producing organelles. It would be worthwhile to look at metabolite usage, markers of cell death, and cAMP signaling in future studies. In summary, these results show that glucagon administration mediates an improvement of contractile recovery following ischaemia/reperfusion in the isolated perfused rat heart. Glucagon also enhanced the mild improvement of contractile recovery attained with a GLP‐1 receptor agonist. Notable limitations of our study are the use of a single methodology and the absence of in vivo experiments. However, our study contributes to the overall picture surrounding the influence of glucagon and GLP‐1 agonism upon the heart. To explain similarities and discrepancies between previous studies, the field requires a rigorous comparison of glucagon's cardiac effects between species, and between different heart conditions (failing heart vs. healthy or diabetic). ## AUTHOR CONTRIBUTIONS RTL and AJM contributed to experimental conception and design and funding acquisition. RTL contributed to the acquisition, analysis, validation, and interpretation of data and drafting of the article. All authors contributed to the critical review of the article and approved the final version for submission. ## FUNDING INFORMATION This work was supported by British Heart Foundation (FS/$\frac{14}{59}$/31282), Research Councils UK (EP/E$\frac{500552}{1}$), and MedImmune. ## CONFLICT OF INTEREST STATEMENT RTL is affiliated with BioPharmaceuticals R&D, AstraZeneca, and declares no other competing financial interests. PA and LJ are employees and shareholders of AstraZeneca. MedImmune provided funding for this research and had no role in study design, data collection, analysis, and interpretation. AJM received support from MedImmune but declares no other competing interests. ## References 1. Ali S., Ussher J. R., Baggio L. L., Kabir M. G., Charron M. J., Ilkayeva O., Newgard C. B., Drucker D. J.. 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--- title: 'The effect of diabetes on COVID-19 incidence and mortality: Differences between highly-developed-country and high-migratory-pressure-country populations' authors: - Marta Ottone - Letizia Bartolini - Laura Bonvicini - Paolo Giorgi Rossi - Massimo Costantini journal: Frontiers in Public Health year: 2023 pmcid: PMC10031649 doi: 10.3389/fpubh.2023.969143 license: CC BY 4.0 --- # The effect of diabetes on COVID-19 incidence and mortality: Differences between highly-developed-country and high-migratory-pressure-country populations ## Abstract The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with $95\%$ confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 ($95\%$ CI 0.82–0.87) and 0.67 ($95\%$ CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 $95\%$ CI 1.22–1.53 vs. 1.20 $95\%$ CI 1.14–1.25; HRs for mortality: 3.96 $95\%$ CI 1.82–8.60 vs. 1.71 $95\%$ CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 $95\%$ CI 4.48–79.87 vs. 3.91 $95\%$ CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 $95\%$ CI: 0.88–1.12) and mortality (MRR: 0.89 $95\%$ CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 $95\%$ CI 1.41–2.11 among HDC vs. 1.41 $95\%$ CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants. ## Introduction Risk factors increasing COVID-19 mortality can act in two main ways: by increasing the probability of infection or by increasing the severity and lethality of the disease following infection. In addition to biological characteristics, a multitude of social and economic factors can influence both the probability of infection and the severity of the disease. Several studies have shown that mortality from COVID-19, as reported by routine statistics, provides an accurate snapshot of deaths in which the main cause was actually SARS-CoV-2 infection and its short-term consequences (1–3). ## COVID-19 and ethnicity The main clinical risk factors for COVID-19 mortality are age, male sex, obesity, and some chronic diseases, including in particular renal failure, diabetes, and dementia [4]. The effect of ethnicity and migrant status changes according to context [5, 6]. Most of the published studies were based on national data or on large cohorts built in the United Kingdom (UK) and the USA. These studies observed an excess of incidence and to a lesser extent of mortality from COVID-19 in the most deprived groups and in some ethnic minorities [7], particularly populations of Asian origin [8]. In the UK, the differential was greater for COVID-19 mortality than for other causes of death [5]. In Italy, during the first two waves, there was no overall excess of either infection or mortality in the immigrant population (9–12). Both incidence and mortality were lower in immigrants in the first wave of the pandemic. During the second wave, the risk of infection and mortality in immigrants was similar in females and, in males, slightly higher than in Italians. Only for hospitalizations, an excess risk in immigrants was constantly observed [13, 14]. A study conducted in the USA found some differences in the strength of risk factors for infection—but not for severity of disease—in African Americans compared to whites: a higher incidence in children, a greater effect of prior cancer, a lesser effect of crowded housing, and a greater effect of obesity [15]. ## Diabetes, obesity, and ethnicity and the effect on COVID-19 mortality Excess weight and diabetes are negative prognostic factors in COVID-19. A review [16] published at the beginning of the pandemic reported that chronic inflammation, increased coagulation activity, immune response impairment, and potential direct pancreatic damage by SARS-CoV-2 might be among the underlying mechanisms of the association between diabetes and COVID-19 severity. A more recent review [17] focused also on the relationship between body mass index (BMI) and COVID-19-related death and between blood glucose controls on poor COVID-19 outcomes. These factors appear to be useful as a prognostic tool. However, the strength of the association between BMI and COVID-19 severity and mortality varies according to ethnic group (15, 18–20). Diabetes and, in some contexts, obesity show higher prevalence among some ethnic minorities and immigrants from eastern countries, particularly people from South Asia [21]. The most deprived populations also often show the poorest glycemic control, possibly because of barriers to effective care and difficulties in reconciling diet regimens and physical activity with economic and time constraints. Nevertheless, epidemiological and clinical evidence suggest that, in South Asians, gaining good glycemic control is more difficult, independently from diet, physical activity, and treatment compliance [22, 23]. Studies carried out in the UK showed that the association between COVID-19 mortality and obesity was stronger in populations originating from South Asia, intermediate in populations of African origin and minor in populations of European origin [19, 24]. Differences were reduced by adjusting for pre-clinical conditions [24]. This greater effect was also found for people with diabetes. In addition, a study conducted in England in a large cohort of people with diabetes showed that the differences were reduced by adjusting for pre-COVID-19 clinical conditions, suggesting that part of the effect was due to worsened control of diabetes and its complications among the most deprived populations [18]. This study also observed that the effect of BMI on COVID-19 mortality was greater in populations of Asian and African origin [18]. Analysis of the effect of BMI and glycemic control in the sub-population of people with diabetes could help to disentangle the associations between ethnicity, diabetes, and COVID-19 mortality. BMI and glycemic control are routinely collected in the Reggio Emilia diabetes registry. ## Objective of the study The objective of this study was to compare the effect of diabetes and of pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Countries (HDCs) including Italians, and immigrants from High-Migratory-Pressure-Countries (HMPCs). Among the population with diabetes, we also aimed to compare the effect of glycemic control and BMI in Italians and immigrants. ## Study setting and design The province of Reggio Emilia has 532,000 inhabitants. A population-based cohort study was conducted. Information was gathered from routine clinical registries and surveillance databases. The study was approved by the Area Vasta Emilia Nord Ethics Committee (Approval No $\frac{2020}{0045199}$), which also allowed the population to be included without requiring informed consent, given the retrospective nature of the study. ## Study population All residents aged over 18 years in the province of Reggio Emilia as of December 31, 2019 were included. The outcomes included all infections reported by COVID-19 surveillance from the beginning of the epidemic (first case diagnosed in Reggio Emilia on February 27, 2020) until August 10, 2021. These cases were then followed up for COVID-19 mortality until September 20, 2021. ## Data sources Different information sources were used: A detailed description of record linkage operations is described in the report by Mangone and colleagues [25]. ## Outcomes We focused on mortality and not on fatality rates to measure the impact of diabetes on COVID-19 severity because, based on the assumption of more limited testing in immigrants and consequent undiagnosed disease, the incidence of infection in immigrants may be underestimated, thus overestimating fatality. Actually, in Emilia-Romagna Region, a reduced probability of testing has been demonstrated, especially for women [9] from HMPC populations [27], and increased screening activity related to international traveling has also been observed in people from HPMCs [28]. ## Stratification and exposures Migrant status was our stratification variable. This was defined, on the basis of country of birth, as high developed countries (HDC) and high migration pressure countries (HMPC). The HDC population included Italians and immigrants from Europe (Andorra, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Iceland, Liechtenstein, Luxemburg, Netherlands, Norway, Principality of Monaco, Portugal, Republic of Saint Marin, Spain, Sweden, Switzerland, United Kingdom and Vatican City), North America (United States and Canada), Australia (Australia and New Zeland) and Asia (Israel, Japan and South Korea). Immigrants from all the other countries were considered among the HMPC population [29]. Among HMPC a focus was presented about South Asians, because of the known high prevalence of diabetes in this population [21]. A sensitivity analysis restricted to people <65 years of age was performed. The rationale underlying the stratified analysis by age was met in the different age structure of the two populations: people from HMPC had a very low proportion of people over 65. Moreover, a strong interaction was observed between the effect of comorbidity and age on COVID-19 prognosis, thus adjusting may not be sufficient to take into account the difference in age structure. Type-2 diabetes was our main exposure analysis. In the descriptive analyses, we also considered other diabetes types (Type-1, other, and undefined), but people with other forms of diabetes were excluded from association analyses. In the sub-population with diabetes, the main determinants of outcomes were the most recent glycemic control and BMI measurements in the period between January 1, 2018, and December 31, 2019. BMI was divided according to the quartiles (<25.90, 25.90–29.07, 29.07–32.90, >32.90). We also included information about recent comorbidities—such as ischemic heart disease, chronic renal failure, hypertension, obesity, heart failure, arrhythmia, vascular disease and stroke—collected from hospital discharge databases (2015–2019). The Charlson Comorbidity Index (CCI) was calculated based on hospital admissions in the previous 5 years and it was used to evaluate the impact of comorbidity on selected outomes [30]. Our CCI incorporated 17 different comorbidities, each of which was weighted according to its potential impact on mortality. This index has been previously applied to COVID-19 patients [31]. Furthermore, the pandemic had different characteristics in different periods: different control measures, different variants, and finally different susceptibility of the populations after the vaccination campaign. For this reason, we added a stratified analysis by calendar period, which may suggest differences if control measures and variants were effect modifiers of the observed associations. ## Adjustment variables Age, sex and COVID-19 vaccination until August 10, 2021 were used as adjustment variables in the models. ## Statistical analysis All the analyses were stratified by place of birth, HDC (including Italy), and HMPC. A focus on South Asians known to have the highest risk and prevalence of diabetes was added. Crude COVID-19 incidence and mortality rates (IRs and MRs) were reported. We reported IRs and MRs per 1,000 persons, incidence and mortality in people from HDC and from HMPC were compared through incidence (IRR) and mortality rate ratios (MRR), with associated $95\%$ confidence intervals ($95\%$CI). Hazard ratios (HRs) with their associated $95\%$ CI adjusted for sex and age were calculated using the Cox proportional hazards model. For the outcome of COVID-19 infections only, a further adjustment for COVID-19 vaccination was considered. COVID-19 vaccination was the only variable included as a time-varying covariate in the model. For COVID-19 mortality, this adjustment was not possible because of the limited number of deaths among vaccinated individuals (Table 1), and thus we presented analyses restricted to non-vaccinated individuals instead of adjusting for vaccine status. Analyses restricted to the diabetic population, stratified by country of birth, were also reported. Table 1 \| Covariates \| Unnamed: 1 \| Unnamed: 2 \| HDC \| HDC.1 \| HDC.2 \| HDC.3 \| HDC.4 \| HMPC \| HMPC.1 \| HMPC.2 \| HMPC.3 \| HMPC.4 \| South Asian \| South Asian.1 \| South Asian.2 \| South Asian.3 \| South Asian.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| Residents \| SARS-CoV-2infection \| SARS-CoV-2infection \| COVID-19 mortality \| COVID-19 mortality \| Residents \| SARS-CoV-2infection \| SARS-CoV-2infection \| COVID-19 mortality \| COVID-19 mortality \| Residents \| SARS-CoV-2infection \| SARS-CoV-2infection \| COVID-19 mortality \| COVID-19 mortality \| \| \| \| \| N \| N \| Crude rate * 1,000 residents \| N \| Crude rate * 1,000 residents \| N \| N \| Crude rate * 1,000 residents \| N \| Crude rate * 1,000 residents \| N \| N \| Crude rate * 1,000 residents \| N \| Crude rate * 1,000 residents \| \| Overall \| \| \| 376016 \| 31943 \| 84.95 \| 1194 \| 3.18 \| 71494 \| 5441 \| 76.10 \| 28 \| 0.39 \| 12805 \| 1089 \| 85.04 \| 8 \| 0.62 \| \| Mean age (SD) \| \| \| 53.6 (19.1) \| 51.3 (19.5) \| 51.3 (19.5) \| 82.7 (10.0) \| 82.7 (10.0) \| 43.5 (14.0) \| 42.8 (13.5) \| 42.8 (13.5) \| 66.5 (12.2) \| 66.5 (12.2) \| 40.5 (0.1) \| 40.2 (0.4) \| 40.2 (0.4) \| 59.3 (3.8) \| 59.3 (3.8) \| \| Females \| \| \| 191222 \| 16136 \| 84.38 \| 547 \| 2.86 \| 37.561 \| 2860 \| 76.14 \| 9 \| 0.24 \| 5113 \| 432 \| \| 3 \| \| \| COVID-19 vaccination as of 10/08/21 \| Not vaccinated \| N \| 82030 \| 11229 \| 136.89 \| 1146 \| 0.01 \| 37.416 \| 2534 \| 67.73 \| 28 \| 0.75 \| 6195 \| 422 \| 68.12 \| 8 \| 2.15 \| \| COVID-19 vaccination as of 10/08/21 \| \| PY \| 721014.2 \| 31394.8 \| 31394.8 \| 1187.1 \| 1187.1 \| 149829.7 \| 5886.5 \| 5886.5 \| 32.3 \| 32.3 \| 19123.4 \| 1088.2 \| 1088.2 \| 5.4 \| 5.4 \| \| COVID-19 vaccination as of 10/08/21 \| 1st dose \| N \| 48.299 \| 13388 \| 277.19 \| 32 \| 0.66 \| 11.257 \| 1920 \| 170.56 \| 0 \| 0 \| 2890 \| 488 \| 168.86 \| 0 \| 0 \| \| COVID-19 vaccination as of 10/08/21 \| \| PY \| 41954.7 \| 40.4 \| 40.4 \| 1.3 \| 1.3 \| 3344.8 \| 2.9 \| 2.9 \| 0 \| 0 \| 354.8 \| 0.3 \| 0.3 \| 0 \| 0 \| \| COVID-19 vaccination as of 10/08/21 \| 1st and 2nd dose \| N \| 245687 \| 7326 \| 29.82 \| 16 \| 0.07 \| 22.821 \| 987 \| 43.25 \| 0 \| 0 \| 3720 \| 179 \| 48.12 \| 0 \| 0 \| \| COVID-19 vaccination as of 10/08/21 \| \| PY \| 52304.9 \| 54.0 \| 54.0 \| 1.3 \| 1.3 \| 2974.7 \| 4.4 \| 4.4 \| 0 \| 0 \| 272.8 \| 0.5 \| 0.5 \| 0 \| 0 \| \| Diabetes \| No \| \| 347480 \| 29492 \| 84.87 \| 859 \| 2.47 \| 67.364 \| 5067 \| 75.22 \| 16 \| 0.24 \| 11599 \| 961 \| 82.85 \| 4 \| 0.34 \| \| Diabetes \| Type 1 \| \| 965 \| 84 \| 87.05 \| 2 \| 2.07 \| 158 \| 10 \| 63.29 \| 0 \| 0 \| 19 \| 0 \| 0 \| 0 \| 0 \| \| Diabetes \| Type 2 \| \| 25844 \| 2216 \| 85.75 \| 315 \| 12.19 \| 3.829 \| 349 \| 91.15 \| 12 \| 3.13 \| 1144 \| 123 \| 107.52 \| 4 \| 3.50 \| \| Diabetes \| Other \| \| 271 \| 18 \| 66.42 \| 1 \| 3.69 \| 28 \| 2 \| 71.43 \| 0 \| 0 \| 9 \| 1 \| 111.11 \| 0 \| 0 \| \| Diabetes \| Undefined \| \| 1456 \| 133 \| 91.35 \| 17 \| 11.68 \| 115 \| 13 \| 113.04 \| 0 \| 0 \| 34 \| 4 \| 117.65 \| 0 \| 0 \| \| Ischemic heart disease \| \| \| 8345 \| 744 \| 89.16 \| 141 \| 16.90 \| 428 \| 37 \| 86.45 \| 2 \| 4.67 \| 110 \| 12 \| 109.09 \| 0 \| 0 \| \| Chronic renal failure \| \| \| 2500 \| 248 \| 99.2 \| 86 \| 34.40 \| 179 \| 21 \| 117.32 \| 0 \| 0 \| 45 \| 5 \| 111.11 \| 0 \| 0 \| \| Hypertension \| \| \| 17231 \| 1562 \| 90.65 \| 317 \| 18.40 \| 853 \| 90 \| 105.51 \| 4 \| 4.69 \| 155 \| 16 \| 103.23 \| 1 \| 6.45 \| \| Obesity \| \| \| 2385 \| 270 \| 113.21 \| 28 \| 11.74 \| 194 \| 22 \| 113.4 \| 2 \| 10.31 \| 30 \| 5 \| 166.67 \| 1 \| 33.33 \| \| Heart failure \| \| \| 5592 \| 580 \| 103.72 \| 167 \| 29.86 \| 191 \| 17 \| 89.01 \| 1 \| 5.24 \| 39 \| 4 \| 102.56 \| 0 \| 0 \| \| Arrhythmia \| \| \| 8235 \| 783 \| 95.08 \| 180 \| 21.86 \| 259 \| 20 \| 77.22 \| 3 \| 11.58 \| 31 \| 1 \| 32.26 \| 0 \| 0 \| \| Vascular diseases \| \| \| 3015 \| 261 \| 86.57 \| 62 \| 20.56 \| 129 \| 10 \| 77.52 \| 1 \| 7.75 \| 20 \| 3 \| 150.00 \| 0 \| 0 \| \| Stroke \| \| \| 7051 \| 738 \| 104.67 \| 173 \| 24.54 \| 344 \| 41 \| 119.19 \| 1 \| 2.91 \| 69 \| 9 \| 130.43 \| 0 \| 0 \| \| Charlson Comorbidity Index \| 0 \| \| 344737 \| 29151 \| 84.56 \| 669 \| 1.94 \| 69.414 \| 5256 \| 75.72 \| 22 \| 0.32 \| 12460 \| 1046 \| 83.95 \| 7 \| 0.56 \| \| Charlson Comorbidity Index \| 1 \| \| 13313 \| 1217 \| 91.41 \| 222 \| 16.68 \| 1.014 \| 87 \| 85.8 \| 2 \| 1.97 \| 207 \| 26 \| 125.60 \| 0 \| 0 \| \| Charlson Comorbidity Index \| 2 \| \| 12494 \| 1037 \| 83 \| 166 \| 13.29 \| 684 \| 65 \| 95.03 \| 2 \| 2.92 \| 90 \| 9 \| 100.00 \| 0 \| 0 \| \| Charlson Comorbidity Index \| 3 \| \| 5472 \| 538 \| 98.32 \| 137 \| 25.04 \| 382 \| 33 \| 86.39 \| 2 \| 5.24 \| 48 \| 8 \| 166.67 \| 1 \| 20.83 \| We decided not to use the term “significant” or “not-significant” since we did not set a significance threshold and we did not performe formal tests of hypothesis. Confidence intervals should be interpreted as continuous variables and should not be used to reject or accept the null hypothesis according to a pre-fixed threshold. Sensitivity analyses were performed and were restricted to the population <65 years of age. We added a stratified analysis according to the following four periods of time (1: February 22, 2020–June 1, 2020; 2: June 2, 2020–December 31, 2020; 3: January 1, 2021–June 30, 2021; 4: July 1, 2021–August 10, 2021), depending on the waves and variants of the infection, and these results are shown in the Supplementary material. Two direct acyclic graphs (DAG) were built: the first described the causal network from contact with the virus to infection and the second described the causal network leading from infection to COVID-19-related death (Figures 1, 2). Figure 1: Direct acyclic graph (DAG) of the relationship between ethnicity and diabetes from contact with SARS-CoV-2 to SARS-CoV-2 infection, in which the effect modifier is ethnicity, the exposures are obesity and diabetes, and the outcome is SARS-CoV-2 infection. Figure 2: COVID-19 vaccination coverage for HDC and HMPC populations for the adult population residing in Reggio Emilia, as of August 10, 2021. The first DAG starts from the association between ethnicity and socio-economic status (SES), characterizing immigrant status in Italy for the HMPC population [32]. Both ethnicity and SES influence behaviors that can impact obesity [through diet and physical activity [33, 34]], vaccination [35, 36] and—among individuals with diabetes—glycemic control [21] and access to health care [37]. Furthermore, linguistic and logistical barriers limit the accessibility of health care for immigrants [37, 38]. Behaviors and living and working conditions influence the probability that an individual will be in contact with SARS-CoV-2 [5, 6, 39]. Vaccination status, and possibly diabetes [40] and glycemic control [41], influences the ability of the immune system to block the development of a detectable infection once contact has occurred. In this latter step, we had not considered those infections that had not been detected, mostly because they were asymptomatic and did not lead to testing. More generally, the DAG does not account for factors influencing the probability of testing for screening, tracing or mild symptoms. As reported in the rationale for choosing the study outcomes, it is reasonable to think that migrant status could influence the probability of testing [9, 27, 42]. At the top of the second DAG, we have represented pre-existing conditions that might modify the probability of death from COVID-19–reported at the bottom right of the DAG. In Italy, ethnicity is associated with SES, because immigrants have lower SES, which in turn affects behaviors such as physical activity and diet, and they are thus prone to obesity and poor glycemic control. People's behaviors determine vaccination status, which modifies immune response. Moreover, SES and people's behaviors affect access to care for diabetes and other chronic diseases. This is then associated with poor glycemic control and with pre-existing cardiovascular disease (CVD) [37, 43]. In addition, genetic factors are linked to diabetes and glycemic control, in particular among the South Asian population [22, 23]. Diabetes not only affects the heart and kidneys but hyperglycemia in diabetes is also thought to cause an immune response dysfunction [44]. Both these factors are associated with hyperinflammation, coagulation dysregulation, and multi-organ failure [44]. SARS-CoV-2 infection is directly associated with pneumonia and respiratory failure, but also with multi-organ failure, and it is indirectly associated with immune response dysfunction, which can evolve into hyperinflammation, and then into coagulation dysregulation. In conclusion, respiratory failure, multi-organ failure, and coagulation dysregulation lead to COVID-19 death [45]. ## Overall population On December 31, 2019, 447,510 adults (18+) were residents in the province of Reggio Emilia. Of those individuals, 376,016 ($84\%$) were from HDC populations and 71,494 ($16\%$) were from HMPC populations. Generally, the HMPC group was younger than the HDC group, both in terms of the general population and for those with COVID-19. We counted 31,943 COVID-19 infections among HDC and 5,441 among HMPC population. Of these, 1,194 HDC and 28 HMPC died of COVID-19. Table 1 shows crude COVID-19 IRs and MRs per 1,000 individuals in the HDC and HMPC groups according to demographic and clinical characteristics. A lower incidence (IRR: 0.84 $95\%$ CI: 0.82–0.87) was observed in the HMPC group than in the HDC group (Supplementary Table 1). The MMR was also lower for HMPC, but it was based on 28 deaths in HMPC and the estimate was rather imprecise (MRR: 0.67 $95\%$ CI: 0.46–0.99). The risk factor that showed a substantial difference in the strength of the association was obesity, which had a greater effect on mortality among HMPC individuals than among the HDC population (HR for HMPC: 18.92 $95\%$ CI: 4.48–78.87; HR for HDC: 3.91 $95\%$ CI: 2.69–5.69). A slightly higher effect of type-2 diabetes on SARS-CoV-2 infection was observed in the HMPC group (HR: 1.37 $95\%$ CI: 1.22–1.53), and particularly in South Asian individuals (HR: 1.43 $95\%$ CI: 1.17–1.75), than in the HDC population (HR: 1.20 $95\%$ CI: 1.14–1.25), while a difference in the effect on mortality was observed for type-2 diabetes (HR for HMPC: 3.96 $95\%$ CI: 1.82–8.60; HR for HDC: 1.71 $95\%$ CI: 1.50–1.95) and arrhythmia (HR for HMPC: 5.59 $95\%$ CI: 1.61–19.45; HR for HDC: 1.73 $95\%$ CI: 1.47–2.04) among the HMPC population compared to the HDC population. Hypertension and vascular diseases also showed a greater impact on risk of death among HMPC individuals than among the HDC population, but the differences were compatible with random fluctuations (Table 2). COVID-19 vaccination was shown to be a protective factor against SARS-CoV-2 infection among both HDC and HMPC populations, with an increasingly protective effect as the number of doses increased (Table 2). Table 2 \| Covariates \| Unnamed: 1 \| HDC \| HDC.1 \| HDC.2 \| HDC.3 \| HMPC \| HMPC.1 \| HMPC.2 \| HMPC.3 \| South Asians (N = 12,805) \| South Asians (N = 12,805).1 \| South Asians (N = 12,805).2 \| South Asians (N = 12,805).3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| SARS-CoV-2 infection \| SARS-CoV-2 infection \| Death from COVID-19 $ \| Death from COVID-19 $ \| SARS-CoV-2 infection \| SARS-CoV-2 infection \| Death from COVID-19 $ \| Death from COVID-19 $ \| SARS-CoV-2 infection (N = 1,089) \| SARS-CoV-2 infection (N = 1,089) \| Death from COVID-19 (N = 8)$ \| Death from COVID-19 (N = 8)$ \| \| \| \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| \| COVID-19 vaccination as of August 10, 2021 \| Not vaccinated \| 1 \| \| / \| \| 1 \| \| / \| \| 1 \| \| / \| \| \| COVID-19 vaccination as of August 10, 2021 \| 1st dose \| 0.59 \| 0.54–0.64 \| \| \| 0.66 \| 0.49–0.89 \| \| \| 0.60 \| 0.26–1.39 \| \| \| \| COVID-19 vaccination as of August 10, 2021 \| 1st and 2nd dose \| 0.20 \| 0.18–0.22 \| \| \| 0.25 \| 0.16–0.38 \| \| \| 0.47 \| 0.17–1.30 \| \| \| \| Type-2 diabetes* \| \| 1.20 \| 1.14–1.25 \| 1.71 \| 1.50–1.95 \| 1.37 \| 1.22–1.53 \| 3.96 \| 1.82–8.60 \| 1.43 \| 1.17–1.75 \| 4.00 \| 0.93–17.20 \| \| Ischemic heart disease \| \| 1.24 \| 1.15–1.33 \| 1.75 \| 1.46–2.09 \| 1.32 \| 0.95–1.82 \| 2.12 \| 0.49–9.18 \| 1.40 \| 0.78–2.49 \| / \| \| \| Chronic renal failure \| \| 1.41 \| 1.24–1.60 \| 2.34 \| 1.87–2.94 \| 1.80 \| 1.15–2.72 \| / \| \| 1.46 \| 0.60–3.52 \| / \| \| \| Hypertension \| \| 1.29 \| 1.23–1.36 \| 1.96 \| 1.72–2.24 \| 1.59 \| 1.28–1.96 \| 3.05 \| 1.03–9.06 \| 1.31 \| 0.79–2.16 \| 3.39 \| 0.39–29.18 \| \| Obesity \| \| 1.43 \| 1.26–1.61 \| 3.91 \| 2.69–5.69 \| 1.61 \| 1.06–2.44 \| 18.92 \| 4.48–79.87 \| 2.14 \| 0.89–5.16 \| 21.91 \| 2.57–184.79 \| \| Heart failure \| \| 1.51 \| 1.39–1.64 \| 2.01 \| 1.70–2.38 \| 1.34 \| 0.83–2.17 \| 2.00 \| 0.26–15.11 \| 1.30 \| 0.49–3.49 \| / \| \| \| Arrhythmia \| \| 1.36 \| 1.27–1.47 \| 1.73 \| 1.47–2.04 \| 1.13 \| 0.73–1.76 \| 5.59 \| 1.61–19.45 \| 0.39 \| 0.05–2.75 \| / \| \| \| Vascular disease \| \| 1.19 \| 1.05–1.34 \| 2.05 \| 1.58–2.65 \| 1.13 \| 0.61–2.11 \| 4.28 \| 0.57–32.13 \| 1.86 \| 0.60–5.79 \| / \| \| \| Stroke \| \| 1.46 \| 1.35–1.57 \| 2.08 \| 1.77–2.45 \| 1.69 \| 1.25–2.31 \| 1.48 \| 0.20–11.14 \| 1.60 \| 0.83–3.10 \| / \| \| \| Charlson comorbidity index \| 0 \| 1 \| \| 1 \| \| 1 \| \| 1 \| \| 1 \| \| \| \| \| Charlson comorbidity index \| 1 \| 1.29 \| 1.22–1.37 \| 2.12 \| 1.81–2.48 \| 1.24 \| 1.00–1.53 \| 1.73 \| 0.40–7.55 \| 1.62 \| 1.09–2.40 \| / \| \| \| Charlson comorbidity index \| 2 \| 1.14 \| 1.07–1.21 \| 2.29 \| 1.92–2.72 \| 1.37 \| 1.07–1.75 \| 2.75 \| 0.64–11.94 \| 1.25 \| 0.64–2.41 \| / \| \| \| Charlson comorbidity index \| 3 \| 1.39 \| 1.28–1.52 \| 3.25 \| 2.69–3.93 \| 1.26 \| 0.89–1.78 \| 4.39 \| 1.01–19.11 \| 2.26 \| 1.12–4.56 \| 10.57 \| 1.21–92.11 \| If we analyzed the initial months of vaccination against COVID-19 in our cohort, cumulative coverage was higher among HDC individuals than HMPC individuals, for both the first and second doses of the vaccine (Figure 3). As of August 10, 2021, one dose vaccination coverage was 78 and $47\%$ for HDC and HMPC, respectively; while the two-dose vaccination coverage was 65 and $31\%$, respectively. The risk reduction in relation to SARS-CoV-2 infection was around $80\%$ among HDC individuals and $75\%$ among HMPC individuals with two doses of vaccination. Figure 3: Direct acyclic graph (DAG) of the relationship between ethnicity and diabetes and COVID-19-related death, in which the effect modifier is ethnicity, the exposures are obesity and diabetes, and the outcome is COVID-19-related death. ## Diabetic population Among the residents of the province of Reggio Emilia, 25,844 HDC ($87.1\%$) and 3,829 HMPC ($12.9\%$) individuals had type-2 diabetes (Table 3). Of these, there were 2,216 COVID-19 infections among HDC and 349 among HMPC. COVID-19 deaths were 315 among HDC and 12 among HMPC. Furthermore, the HMPC group showed generally similar incidence (IRR: 0.99 $95\%$ CI: 0.88–1.12) and mortality (MRR: 0.89 $95\%$ CI: 0.49–1.61) to that of HDC individuals (Supplementary Table 1). Table 3 \| Unnamed: 0 \| Unnamed: 1 \| Population with type-2 diabetes \| Population with type-2 diabetes.1 \| Population with type-2 diabetes.2 \| SARS-CoV-2 infection \| SARS-CoV-2 infection.1 \| Death from COVID-19 \| Death from COVID-19.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Covariates \| \| HDC \| HDC \| HMPC \| HDC \| HMPC \| HDC \| HMPC \| \| \| \| \| N (%) \| N (%) \| N (%) \| N (%) \| N (%) \| N (%) \| \| Overall \| \| \| 25844 \| 3829 \| 2216 \| 349 \| 315 \| 12 \| \| Mean age (SD) \| \| \| 72.1 (11.8) \| 57.9 (11.9) \| 71.6 (13.2) \| 55.9 (12.0) \| 82.2 (8.8) \| 65.1 (14.3) \| \| Females \| \| \| 11,235 (43.5) \| 1,833 (47.9) \| 933 (42.1) \| 163 (47.0) \| 135 (42.9) \| 5 (41.7) \| \| COVID-19 vaccination as of August 10, 2021 \| Not vaccinated \| N \| 3,812 (14.8) \| 1,408 (36.8) \| 696 (31.4) \| 116 (33.2) \| 300 (95.2) \| 12 (100.0) \| \| COVID-19 vaccination as of August 10, 2021 \| \| PY \| 53,229.2 (82.5) \| 8,761.9 (91.1) \| 2.129,8 (99.5) \| 423.4 (99.8) \| 313.7 (99.8) \| 12.5 (100.0) \| \| COVID-19 vaccination as of August 10, 2021 \| 1st dose \| N \| 1,407 (5.4) \| 350 (9.1) \| 983 (44.4) \| 153 (43.8) \| 11 (3.5) \| 0 (0.0) \| \| COVID-19 vaccination as of August 10, 2021 \| \| PY \| 2,843.9 (4.4) \| 279.2 (2.9) \| 2.2 (0.1) \| 0.1 (0.1) \| 0.32 (0.1) \| 0 (0.0) \| \| COVID-19 vaccination as of August 10, 2021 \| 1st and 2nd dose \| N \| 20,625 (79.8) \| 2,071 (54.1) \| 537 (24.2) \| 80 (22.9) \| 4 (1.3) \| 0 (0.0) \| \| COVID-19 vaccination as of August 10, 2021 \| \| PY \| 8,456.3 (13.1) \| 580.4 (6.0) \| 9.0 (0.4) \| 0.6 (0.1) \| 0.5 (0.2) \| 0 (0.0) \| \| Ischemic heart disease \| \| \| 2,401 (9.3) \| 185 (4.8) \| 246 (11.1) \| 20 (5.7) \| 57 (18.1) \| 1 (8.33 \| \| Chronic renal failure \| \| \| 943 (3.7) \| 64 (1.7) \| 113 (5.1) \| 8 (2.3) \| 45 (14.3) \| 0 (0.0) \| \| Hypertension \| \| \| 4,456 (17.2) \| 275 (7.2) \| 430 (19.4) \| 31 (8.9) \| 104 (33.0) \| 1 (8.3) \| \| Obesity \| \| \| 708 (2.7) \| 50 (1.3) \| 100 (4.5) \| 6 (1.7) \| 21 (6.7) \| 1 (8.3) \| \| Heart failure \| \| \| 1,746 (6.8) \| 71 (1.9) \| 196 (8.8) \| 7 (2.0) \| 62 (19.7) \| 0 (0.0) \| \| Arrhythmia \| \| \| 1,896 (7.3) \| 51 (1.3) \| 213 (9.6) \| 3 (0.9) \| 61 (19.4) \| 1 (8.3) \| \| Vascular disease \| \| \| 798 (3.1) \| 36 (0.9) \| 78 (3.5) \| 2 (0.6) \| 20 (6.4) \| 0 (0.0) \| \| Stroke \| \| \| 1,727 (6.7) \| 90 (2.4) \| 184 (8.3) \| 10 (2.9) \| 51 (16.2) \| 0 (0.0) \| \| Charlson comorbidity index \| 0 \| \| 19,395 (75.1) \| 3,382 (88.3) \| 1,580 (71.3) \| 305 (87.4) \| 151 (47.9) \| 10 (83.3) \| \| Charlson comorbidity index \| 1 \| \| 2,892 (11.2) \| 227 (5.9) \| 280 (12.6) \| 17 (4.9) \| 58 (18.4) \| 0 (0.0) \| \| Charlson comorbidity index \| 2 \| \| 2,150 (8.3) \| 135 (3.5) \| 211 (9.5) \| 17 (4.9) \| 58 (18.4) \| 1 (8.3) \| \| Charlson comorbidity index \| 3 \| \| 1,407 (5.4) \| 85 (2.2) \| 145 (6.5) \| 10 (2.9) \| 48 (15.24) \| 1 (8.3) \| \| BMI \| <25.90 \| \| 3,946 (23.1) \| 748 (29.2) \| 280 (19.9) \| 71 (28.2) \| 39 (24.1) \| 1 (20.0) \| \| BMI \| 25.90–29.07 \| \| 4,246 (24.9) \| 690 (26.9) \| 322 (22.9) \| 76 (30.2) \| 44 (27.2) \| 3 (60.0) \| \| BMI \| 29.07–32.90 \| \| 4,426 (25.9) \| 596 (23.2) \| 371 (26.4) \| 50 (19.8) \| 34 (21.0) \| 0 (0.0) \| \| BMI \| >32.90 \| \| 4,452 (26.1) \| 531 (20.7) \| 431 (30.7) \| 55 (21.8) \| 45 (27.8) \| 1 (20.0) \| \| Glycated hemoglobin \| ≤ 7 \| \| 13,548 (52.4) \| 1,409 (36.8) \| 1,126 (50.8) \| 142 (40.7) \| 157 (49.8) \| 5 (41.7) \| \| Glycated hemoglobin \| 7-8 \| \| 5,969 (23.1) \| 816 (21.3) \| 516 (23.3) \| 74 (21.2) \| 76 (24.1) \| 2 (16.7) \| \| Glycated hemoglobin \| >8 \| \| 6,327 (24.5) \| 1,604 (41,9) \| 574 (25.9) \| 133 (38.1) \| 82 (26.0) \| 5 (41.67) \| SARS-CoV-2 infection from COVID-19 showed an association with obesity in both HDC and HMPC individuals with diabetes (HRs: 1.73 $95\%$ CI 1.41–2.11 and 1.41 $95\%$ CI 0.63–3.17, respectively), even if estimates were very imprecise (Table 4). Finally, in individuals with diabetes, the association between the outcomes and BMI showed a consistent trend only in HDC for incidence, with HR for the upper quartiles of BMI (29.07–32.90) 1.13 ($95\%$ CI 0.97–1.32) and for BMI>32.90 HR: 1.29 ($95\%$ CI 1.10–1.50). For HMPC, no trend was appreciable. The effect of BMI on mortality was only appreciable for BMI >32.90 with HR: 2.04 ($95\%$ CI 1.31–3.19) in HDC, while in HMPC only one death was observed in those with BMI >32.90. Table 4 \| Unnamed: 0 \| Unnamed: 1 \| HDC \| HDC.1 \| HDC.2 \| HDC.3 \| HMPC \| HMPC.1 \| HMPC.2 \| HMPC.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| SARS-CoV-2 infection \| SARS-CoV-2 infection \| Death from COVID-19 $ \| Death from COVID-19 $ \| SARS-CoV-2 infection \| SARS-CoV-2 infection \| Death from COVID-19 $ \| Death from COVID-19 $ \| \| Covariates \| \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| HR \| 95% CI \| \| COVID-19 vaccination as of August 10, 2021 \| Not vaccinated \| 1 \| \| / \| \| 1 \| \| / \| \| \| COVID-19 vaccination as of August 10, 2021 \| 1st dose \| 0.96 \| 0.72–1.27 \| \| \| 0.54 \| 0.16–1.77 \| \| \| \| COVID-19 vaccination as of August 10, 2021 \| 1st and 2nd dose \| 0.40 \| 0.27–0.61 \| \| \| 0.55 \| 0.17–1.76 \| \| \| \| Obesity \| \| 1.73 \| 1.41–2.11 \| 4.36 \| 2.79–6.82 \| 1.41 \| 0.63–3.17 \| 6.43 \| 0.83–49.90 \| \| BMI \| <25.90 \| 1 \| \| 1 \| \| 1 \| \| 1 \| \| \| BMI \| 25.90–29.07 \| 1.04 \| 0.89–1.22 \| 1.12 \| 0.72–1.75 \| 1.14 \| 0.83–1.58 \| 3.30 \| 0.34–31.80 \| \| BMI \| 29.07–32.90 \| 1.13 \| 0.97–1.32 \| 1.09 \| 0.68–1.74 \| 0.87 \| 0.61–1.25 \| – \| \| \| BMI \| >32.90 \| 1.29 \| 1.10–1.50 \| 2.04 \| 1.31–3.19 \| 1.12 \| 0.78–1.61 \| 1.50 \| 0.09–25.48 \| \| Glycated hemoglobin \| ≤ 7 \| 1 \| \| 1 \| \| 1 \| \| 1 \| \| \| Glycated hemoglobin \| 7–8 \| 1.04 \| 0.94–1.16 \| 1.13 \| 0.85–1.49 \| 0.91 \| 0.68–1.20 \| 0.70 \| 0.14–3.60 \| \| Glycated hemoglobin \| >8 \| 1.09 \| 0.98–1.20 \| 1.20 \| 0.91–1.56 \| 0.81 \| 0.64–1.03 \| 0.89 \| 0.26–3.06 \| Glycated hemoglobin showed no association with the two outcomes, except for a slight excess risk of infection for HbA1c levels above $8\%$ appreciable only in HDC and possibly due to random fluctuation (Table 4). When the analysis was restricted to people <65 years of age, for deaths from COVID-19 among the HDC population, the HR for type-2 diabetes was 3.47 ($95\%$ CI: 1.81–6.67), and this became almost double among HMPC, with a HR of 6.12 ($95\%$ CI: 1.94–19.32) (Supplementary Table 2). As for the general population, vaccination against COVID-19 infection also played a protective role among diabetics, resulting in a reduction of 60 and $45\%$, respectively among HDC and HMPC populations following two doses of vaccination. Analyses stratified according to four time periods were presented in the Supplementary Tables 3–6. ## Main results In our cohort, we did not observe any increase in incidence and mortality in individuals from HMPC, compared to Italians and people from HDC. Our data are in line with previous literature that observed a larger association between diabetes and obesity and COVID-19 mortality among immigrants, particularly from South Asia than in Europeans [4, 8, 18, 19]. The effect of diabetes on the risk of infection was slightly higher in the HMPC and South Asian populations than in the HDC population. No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Among individuals with diabetes, the Reggio Emilia cohort did not show any difference in the strength of risk and prognostic factors between the HMPC population and the HDC population, although the very small number of deaths in the HMPC population did not allow to draw any certain. ## Limitations of the study and comparison with the international literature It is worth noting that our results did not confirm the excess of SARS-CoV-2 infections and COVID-19 mortality reported by studies conducted in the UK and the USA for populations of Asian and African origin. Our results are consistent with other Italian studies [46, 47], which also reported a lower incidence of COVID-19 among immigrants [11], while COVID-19 mortality depended on the period considered, alternating phases of slightly lower mortality among immigrants and vice versa [12, 28]. This occurred despite the fact that vaccine coverage was lower in the HMPC population than in the HDC population. Stratified analyses by calendar period did not suggest differences in the outcomes for diabetes and pathologies potentially related to diabetes between HDC and HMPC, suggesting control measures at the society level and virus variants were not effect modifiers of the observed associations. The main limitation of this study is that our estimates of associations concerning the mortality outcome in people from HMPC are extremely imprecise, due to the relatively small number of deaths in this population. A second limitation of the study is that it was not possible to consider the socio-economic and housing conditions of the population, as data on these variables were not available for our cohort. Both types of determinants (socio-economic and clinical) could explain some of the differences in the risk of infection between the HDC and HMPC populations when these differences were present. Studies conducted in the UK showed that by adjusting for socio-economic level, as well as for pre-existing clinical conditions, the excesses of incidence and mortality observed in populations of non-European origin decreased or disappeared, suggesting that some of the worst outcomes were due to the greater state of deprivation of these populations and the worse pre-existing clinical conditions [18, 24]. Previous studies found that the effect of comorbidities on COVID-19 mortality was much stronger in younger patients [31]. The immigrant population is actually much younger than the Italian population, and part of the phenomenon of the higher impact of specific comorbidities on COVID-19 mortality in immigrants could thus be due to their younger age. With reference to the HDC group, if we restricted the analyses to people aged <65 years, the strength of the associations between diabetes and COVID-19 mortality (and between diabetes, BMI, and COVID-19 mortality, for those individuals with diabetes) was greater than at all ages combined. Regarding the differences observed in the associations for diabetes between HMPC and HDC populations in SARS-CoV-2 infection, our results were unable to distinguish the portion of the effect caused by SES, behavior and access to care from the portion of the effect caused by the different biological factors and genetic background (Figure 1). However, a previous study on this population [48] had shown that despite the fact that South Asians attended diabetes clinics more than Italians and had a similar level to Italians for compliance with guidelines, they had poorer glycemic control, thus suggesting some biological determinants for the differences observed. Nevertheless we did not observed any overall COVID-19 mortality excess in the HMPC population despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in this population. This suggests that if any fragility factor due to genetic background is present, its effect on COVID-19 severity is small. The few studies that could adjust, at least in part, for socioeconomic conditions, including crowding and housing conditions found that the excess risk was extremely reduced in immigrants. Furthermore, in studies from UK, part of the excess mortality in non-white people with diabetes was due to worsening pre-existing chronic conditions. It should be considered that immigration in Italy is a relatively recent period, and the healthy migrant effect is still appreciable [48]. Thus, it is possible that, even among the immigrants with diabetes, the burden of chronic conditions is still low due to the selection of healthier people among those affording the move to another country [49]. A lower incidence of infection in immigrants may be due to more limited testing and consequent undiagnosed disease. A possible bias in testing has been observed in the Emilia-Romagna Region, but it did not always go in the direction of a lower probability of testing in immigrants. During the first wave, a reduced probability of testing has been demonstrated, especially for women [9] from HMPC populations [27]. On the contrary, increased screening activity related to international traveling has been observed in immigrants during the summer of 2020 [28]. This difference in the probability of COVID-19 diagnoses led us to not consider the fatality rate of COVID-19 as a reliable outcome to compare the interaction between metabolic risk factors and ethnicity or immigrant status. This is why, despite our conceptual framework presenting the causal chains in two steps, i.e., from contact with the virus to infection and from infection to death, we only presented the associations of factors assessed before infection with infection and with death. When we focused on individuals with diabetes, the excess in COVID-19 mortality linked to obesity and BMI was similar among the HPMC and HDC populations, as was the effect of glycemic control. This suggests that the stronger effect of obesity on COVID-19 mortality observed in the HMPC population compared with the general population was linked to the higher prevalence of diabetes in the HMPC population. This finding is not consistent with those reported by a large population-based study conducted in a cohort of people with diabetes in England, where the effect of BMI >40 on COVID-19 mortality was stronger among all non-white ethnicities than in those classified as white [11]. Nevertheless, our estimates were rather imprecise and confidence intervals included the estimates from the English cohort. ## Conclusions Diabetes and pathologies potentially related to diabetes are a worse risk factor for death from COVID-19 for HMPC individuals and particularly for South Asians than for the HDC population. Although there is evidence that some biological mechanisms contribute to worsening the outcome of COVID-19 in some ethnic groups, the fact that we did not observe an overall excess risk of COVID-19 mortality in immigrants in our cohort [50] suggests that this intrinsic disadvantage is small and does not justify the higher mortality observed in other studies [5, 7, 51], thus redirecting attention to socio-economic and environmental causes. ## Data availability statement The data analyzed in this study is subject to the following licenses/restrictions: Researchers who would like to access individual data should present their request, together with a study protocol, to the Area Vasta Emilia Nord Ethics Committee for approval (cereggioemilia@ausl.re.it). Requests to access these datasets should be directed to laura.bonvicini@ausl.re.it. ## Reggio Emilia COVID-19 working group Massimo Costantini, Roberto Grilli, Massimiliano Marino, Giulio Formoso, Debora Formisano, Ivano Venturi, Cinzia Campari, Francesco Gioia, Serena Broccoli, Marta Ottone, Pierpaolo Pattacini, Giulia Besutti, Valentina Iotti, Lucia Spaggiari, Chiara Seidenari, Licia Veronesi, Paola Affanni, Maria Eugenia Colucci, Andrea Nitrosi, Marco Foracchia, Rossana Colla, Marco Massari, Anna Maria Ferrari, Mirco Pinotti, Nicola Facciolongo, Ivana Lattuada, Laura Trabucco, Stefano De Pietri, Giorgio Francesco Danelli, Laura Albertazzi, Enrica Bellesia, Simone Canovi, Mattia Corradini, Tommaso Fasano, Elena Magnani, Annalisa Pilia, Alessandra Polese, Silvia Storchi Incerti, Piera Zaldini, Efrem Bonelli, Bonanno Orsola, Matteo Revelli, Carlo Salvarani, Carmine Pinto, Pamela Mancuso, Francesco Venturelli, Massimo Vicentini, Cinzia Perilli, Elisabetta Larosa, Eufemia Bisaccia, Emanuela Bedeschi, Alessandro Zerbini, and Paolo Giorgi Rossi. ## Author contributions PG designed the study, planned the data analysis, drafted the outline of the manuscript, and critically reviewed and revised the manuscript. MO and LBa contributed to designing the study, conducted the analyses, and drafted the methods and results of the manuscript. LBo contributed to designing the study, to analyzing the data, and to writing the manuscript. All authors approved the final manuscript as submitted. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'CAS Array: design and assessment of a genotyping array for Chinese biobanking' authors: - Zijian Tian - Fei Chen - Jing Wang - Benrui Wu - Jian Shao - Ziqing Liu - Li Zheng - You Wang - Tao Xu - Kaixin Zhou journal: Precision Clinical Medicine year: 2023 pmcid: PMC10031742 doi: 10.1093/pcmedi/pbad002 license: CC BY 4.0 --- # CAS Array: design and assessment of a genotyping array for Chinese biobanking ## Abstract ### Background Chronic diseases are becoming a critical challenge to the aging Chinese population. Biobanks with extensive genomic and environmental data offer opportunities to elucidate the complex gene–environment interactions underlying their aetiology. Genome-wide genotyping array remains an efficient approach for large-scale genomic data collection. However, most commercial arrays have reduced performance for biobanking in the Chinese population. ### Materials and methods Deep whole-genome sequencing data from 2 641 Chinese individuals were used as a reference to develop the CAS array, a custom-designed genotyping array for precision medicine. Evaluation of the array was performed by comparing data from 384 individuals assayed both by the array and whole-genome sequencing. Validation of its mitochondrial copy number estimating capacity was conducted by examining its association with established covariates among 10 162 Chinese elderly. ### Results The CAS Array adopts the proven Axiom technology and is restricted to 652 429 single-nucleotide polymorphism (SNP) markers. Its call rate of $99.79\%$ and concordance rate of $99.89\%$ are both higher than for commercial arrays. Its imputation-based genome coverage reached $98.3\%$ for common SNPs and $63.0\%$ for low-frequency SNPs, both comparable to commercial arrays with larger SNP capacity. After validating its mitochondrial copy number estimates, we developed a publicly available software tool to facilitate the array utility. ### Conclusion Based on recent advances in genomic science, we designed and implemented a high-throughput and low-cost genotyping array. It is more cost-effective than commercial arrays for large-scale Chinese biobanking. ## Introduction Chronic diseases are the major cause of mortality in the elderly.1,2 With the rapid progress of population aging, chronic diseases are becoming a critical public health issue and economic burden in China.3,4 Due to the complex gene–environment interplay in their aetiology, better understanding of the chronic disease mechanism and discovery of novel biomarkers are urgently required to facilitate precision medicine.5,6 Large prospective cohorts such as the UK Biobank, which collected extensive environmental information coupled with genomic data, have been proved capable of dissecting the complex aetiology of common chronic diseases.5–8 However, both the genetic background and environmental factors affecting those complex diseases can vary between populations.6 Therefore, large perspective cohort studies coupled with biobanks are essential to meet the challenge of Chinese population-specific precision medicine for the aging population. High-throughput and cost-effective genomic techniques have advanced dramatically. Whole-genome sequencing (WGS) can identify genetic variations accurately with any allele frequency across the whole genome.9 While the cost of WGS has dropped significantly, single-nucleotide polymorphism (SNP) genotyping arrays remain the most cost-effective way of collecting genomic data on a biobank scale. SNP arrays focus on more informative variants among the genome to achieve higher throughput at a lower cost. Together with imputation methods, SNP arrays can generate a relatively accurate genotype, except for extremely rare variants.9 Imputed genotypes derived from SNP arrays can provide similar statistical power to those from WGS for genome-wide association studies (GWAS).10,11 However, most commercial SNP arrays were designed to maximize genome coverage and imputation accuracy in populations of European ancestry. These arrays include a significant proportion of SNPs that are monomorphic while genotyping samples from the other ethnic groups, resulting in a loss of valid information content. That is why most national biobanks worldwide have chosen to design a customized SNP array for genomic data collection.12–14 As large-scale biobanks of Chinese cohorts are currently underway, an SNP array optimized for large Chinese prospective cohort studies is urgently needed. The existing SNP arrays designed for the Chinese population were mostly based on small global genome reference panels such as the 1000 Genomes Project (1kGP) or the HapMap established more than a decade ago.15,16 With the recent advance in large-scale population sequencing in the Chinese population,17 genomic mapping with higher resolution offered an opportunity to design a more efficient SNP array for Chinese biobanks. Another recent advance in human genetics is the confirmation of mitochondrial DNA copy number (MCN) as a novel biomarker of aging-related diseases and all-cause mortality.18,19 Studying MCN in large cohorts and biobanks was made possible by the development of methodologies that could estimate MCN through analysing raw genotyping intensity data from existing SNP arrays.20–22 However, none of the existing SNP arrays were optimized for MCN estimation, and had either insufficient markers or unbalanced intensities.20,22 Therefore, future SNP arrays could be designed to include more mitochondrial markers to facilitate MCN estimation as an extra type of genetic biomarker content for studies of ageing-related outcomes. Here we describe the design and assessment of a genome-wide SNP array, the CAS Array, specifically optimized for cost-effective whole genome genotyping in the Chinese population. The array design took advantage of a large high-quality Chinese genomic reference panel and incorporated the latest methodological developments for MCN estimation, providing an efficient tool for precision medicine in Chinese individuals. ## Datasets Three main datasets were used for the development and assessment of the CAS Array. The development dataset is part of the NyuWa reference panel, which includes deep (30x) WGS data of 2 641 Chinese individuals across China.17 It was mainly used to construct two reference panels for SNP selection and imputation validation. The evaluation dataset consists of another 384 Chinese individuals with both WGS and CAS Array data available.23 This was used for evaluating the genotyping accuracy and imputation performance. The validation dataset came from a large population cohort, which includes 10 162 elderlies recruited from Kunshan City, Jiangsu, China. These individuals were genotyped with the CAS Array to validate the MCN estimates by assessing their association with established age-related biomarkers recorded in the electronic health records. ## Construction of the Chinese reference panels For array design, a tagging reference marker panel was constructed from the development dataset of 2 641 Chinese individuals with WGS variant calls. Quality control [Variant Quality Score Recalibration (VQSR) passed, SNPs only, missing rate < 0.05, minor allele count ≥ 3, quality value ≥ 30, read depth (DP) ≥ 3, and Hardy–Weinberg equilibrium (HWE), P value > 10−6] was conducted by VCFtools.24 A total of 17.3 M SNPs, including 5 M common (minor allele frequency (MAF) ≥ 0.05) SNPs and 71 k rare (0.001 < MAF < 0.05) coding SNPs passed the quality control and were used for GWAS tagging marker selection. To derive the reference panel for imputation, slightly different quality control steps were applied to the development dataset. Among the SNPs passing VQSR, those with missing rate > 0.05, HWE P value < 10−6 or minor allele count < 3 were excluded. Samples that were probably contaminated (deviate ± 3 SD from mean heterozygosity rate), relatives within the third degree or abnormally recorded data were excluded. The sex of each individual was inferred by F coefficient and SNP observation on the Y chromosome. A putative XO type sample was marked as male to match the haploid state of the X chromosome. The relationship inference was done by KING software and other quality control steps were done by PLINK.25,26 The genotype was phased and converted to IMPUTE2 reference panel format by SHAPEIT2 software with a 0.5 Mb window size as recommended for WGS data.27 The genetic maps used for phasing were obtained from SHAPEIT4.28 The final reference panel contains 2 562 samples with 17.9 M SNPs. ## Array design As for genotyping arrays chosen by most national biobanks, the CAS Array utilized a ThermoFisher Axiom custom array harboring up to 675 k markers. The SNP markers were selected according to three priorities. Firstly, to achieve adequate coverage of common variants for imputation-based GWAS, common SNPs on the Axiom APMRA with proven technical efficacy were anchored.29 They were then complemented by greedy tagging on our reference panel to cover all the common (MAF > 0.05) SNPs. The second priority was to directly type as many coding variants with MAF > 0.001 as possible in our reference panel that Axiom technical efficacy allowed. Finally, a total of 776 mitochondrial markers were selected to enable more accurate MCN estimation. Additional markers were added to the array for a wider range of applications in medical research. Markers in the human leukocyte antigen (HLA) region, pharmacokinetic variants in drug absorption, distribution, metabolism, and excretion (ADME), ancestry informative markers (AIMS), and mitochondrial markers were selected based on the reference set validated by Illumina and Affymatrix. HLA markers, ADME markers and AIMS with MAF > 0.01 in our development dataset were included while all available mitochondrial markers were included on the array. ## Evaluation of coding variants coverage Coding variants were more likely to be identified as clinically relevant.30 However, clinical translation of such knowledge of precision medicine requires high genotyping accuracy to maintain reasonable sensitivity and specificity, which could be better achieved by directly genotyping rather than using imputed genotypes. The coverage of coding variants with MAF > 0.001 was examined on the latest ChinaMAP reference panel.31 Variants position, alleles labels, and frequencies derived from WGS data of 10 588 Chinese individuals were downloaded and annotated with ANNOVAR.32 There were 107.4 k variants marked as coding variants with MAF > 0.001 in ChinaMAP. The coding variants coverage of the CAS Array was defined as the proportion of variants having matched position and alleles with the designed markers on the arrays relative to the total of 107.4 k variants on ChinaMAP. ## Evaluation of genotyping accuracy Genotyping accuracy of the CAS Array was evaluated by calculating the concordance rate between WGS calls and array genotyping results in the array evaluation dataset. Quality control of WGS data was the same as that applied to the imputation reference panel. Array genotyping SNPs were called by APT software following the manufacturer's instructions.33 Five samples having inconsistent sex or that were duplicated were removed by PLINK.26 The array genotyping call rate was defined as the proportion of recommended variants relative to the total number of designed markers on the array. Within these successfully called SNPs on the array, concordance rate was calculated as the proportion of concordant genotypes relative to all non-missing variant calls from WGS. ## Evaluation of imputation performance The evaluation dataset was also used to evaluate the imputation performance of the CAS Array as compared to eight commonly used commercial arrays, including Genome-Wide Human SNP Array 6.0 (Affy SNP6), Axiom Precision Medicine Research Array (Axiom PMRA), Axiom Asia Precision Medicine Research Array (Axiom APMRA), Infinium Global Screening Array (Illumina GSA), Infinium Asian Screening Array (Illumina ASA), Infinium HumanOmni1 (Illumina Omni1), Infinium OmniExpress (Illumina OE), and Infinium OmniZhongHua (Illumina OZH). Manifest files were downloaded from the respective official websites of these arrays and the positions of the markers were converted to genome build hg38 by UCSC liftOver.34 Genotypes with matching physical position and alleles were extracted from the WGS dataset as simulated genotyping calls. Low-quality variants including those with call rate < 0.95, MAF < 0.01, or HWE P value < 10−6 were excluded before imputation. Autosomes and chromosome X genotypes of each array were phased by SHAPEIT2 using the genetic map from SHAPEIT4.27,28 The reference strands were aligned to our Chinese reference panel derived from the NyuWa reference panel by Genotype Harmonizer.35 Imputation was performed by IMPUTE2 with the same reference panel.36 The imputation performance of each array was evaluated by comparing the imputed genotypes with the original WGS outputs. We used imputation r2, discordance rate, and imputation-based genomic coverage to assess the performance of the arrays as in previous studies.13,14,37 The imputation r2 was defined as the squared Pearson correlation r2 between the allele dosages of WGS and imputed genotypes. The discordance rate was defined as the proportion of the mismatching genotypes between WGS results and the most possible genotypes at each site generated by imputation. Coverage was defined as the proportion of the variants having imputation r2 greater than a given threshold (typically r2 > 0.8). Average imputation r2 and discordance rate was calculated for each array. Coverage of common SNPs (MAF ≥ 0.05) and low-frequency SNPs (0.01 ≤ MAF < 0.05) were calculated separately for the arrays. ## MCN estimation MCN estimation was conducted in a similar manner as implemented by two previous MCN estimation pipelines, MitoPipeline and AutoMitoC.20,21 In brief, the MCN was estimated by the intensity of fluorescent signal of mitochondrial markers indicating the segments of mitochondrial DNA captured by the corresponding probes. The intensities of autosomal markers were used as a reference to capture latent confounding factors such as batch effects and variation in DNA concentrations. Firstly, raw genotyping intensity files were processed for quality control by APT Software.33 Genotype calls and normalized signal intensity were also generated by APT. Log R ratios (LRRs) were calculated as an intensity measure and corrected for GC content to adjust for genomic waves by PennCNV.38,39 To select high-quality markers for MCN estimation, PLINK and BLAST+ were used for quality control.26,40 Markers with multiple alignment of percentage of identical matches > $80\%$ were excluded for potential off-target. For autosomal markers, additional quality control including call rate > $95\%$, HWE P-value > 10−6, linkage disequilibrium (LD)-pruning (r2 < 0.3), and maximum spacing was done. After filtering, 47 102 autosomal markers and 166 mitochondrial markers were left as high-quality markers for MCN estimation. Principal component analysis (PCA) was applied on the LRRs of high-quality autosomal markers generating 80 PCs using R.41 The LRRs of high-quality mitochondrial markers were adjusted by regressing out the PCs of the autosomal markers. The final MCN estimates were extracted from the adjusted mitochondrial LRRs by PCA and converted to a standard normal distribution. After excluding samples with low genotyping quality (call rate < 0.95), fluctuating LRR (LRR SD > 0.35), inconsistent sex calling, or without available phenotype data, the validation data set was finally used to examine the association between estimated MCN and age-related biochemical traits such as white blood cells count (WBC), haemoglobin (HEMO), and platelets (PLT). The same pipeline was also applied on the evaluation dataset, where MCN estimated from array data could be compared directly with MCN estimated from WGS as twice the ratio of the sequencing depth between mitochondrial reads and autosomal reads. ## Content of CAS Array We designed an Axiom SNP array based on the large Chinese NyuWa genome reference panel of 2641 individuals.17 The CAS Array includes a total of 652 429 SNPs selected for different purposes (Table 1). Of these, 525 k variants were selected as genome-wide tagging SNPs (MAF > 0.01) for GWAS. Another 108 k of the markers offer high direct coverage of coding variants with MAF > 0.001 in the Chinese population. In addition to the small numbers of SNPs selected for other types of precision medicine investigations, 776 mitochondrial SNP markers were included for MCN estimation. Table 1. \| Category \| Number of markers \| Proportion of markers \| \| --- \| --- \| --- \| \| GWAS tagging markers \| 525 113 \| 80.49% \| \| Coding variants \| 108 261 \| 16.59% \| \| HLA markers \| 14 843 \| 2.28% \| \| ADME markers \| 1 403 \| 0.22% \| \| AIMS \| 2 033 \| 0.31% \| \| Mitochondrial markers \| 776 \| 0.12% \| \| Total \| 652 429 \| 100.00% \| ## Genotyping call rate and accuracy Call rate and accuracy of the CAS Array were evaluated by assaying 384 Chinese individuals with both the CAS Array and WGS. Of the 652 577 SNP markers on the array (including technical markers of Axiom), 645 327 were genotyped and passed quality control, resulting in a raw call rate of $98.89\%$. Of the 582 342 non-ambiguous variants that overlapped between CAS Array and WGS, the average concordance rate across samples was $99.89\%$. These results indicate that the in-house genotyping accuracy of the CAS Array was comparable to most commercial SNP arrays.13,14,42 ## Coverage of coding variants in the Chinese population To evaluate the coverage of coding variants in the Chinese population, we utilized the large external genome reference panel of the ChinaMAP.31 Out of the 107 403 coding variants with MAF > 0.001 in the ChinaMAP, 74 470 ($69.3\%$) were directly captured by the CAS Array and passed quality control. Compared to other commonly used commercial SNP arrays, CAS Array has a much higher direct coverage of coding variants that are more relevant to precision medicine (Table 2). Table 2. \| Array name \| Number of coding variants covered \| Proportion of coding variants covered \| \| --- \| --- \| --- \| \| CAS Array \| 74 470 \| 69.3% \| \| Affy SNP6 \| 6 528 \| 6.1% \| \| Axiom PMRA \| 6 917 \| 6.4% \| \| Axiom APMRA \| 31 155 \| 29.0% \| \| Illumina GSA \| 12 732 \| 11.9% \| \| Illumina ASA \| 22 657 \| 21.1% \| \| Illumina Omni1 \| 27 584 \| 25.7% \| \| Illumina OE \| 16 740 \| 15.6% \| \| Illumina OZH \| 22 463 \| 20.9% \| ## Imputation performance Imputation performance was evaluated on both accuracy and coverage using the evaluation dataset. Within the post quality control WGS data, there are 4.2 M common SNPs (MAF ≥ 0.05) and 1.6 M low-frequency SNPs (0.01 ≤ MAF < 0.05). Figure 1 shows the imputation r2 distribution across the allele frequency spectrum for the nine arrays. CAS Array demonstrated the highest overall imputation accuracy, probably due to the fact that up to $90.6\%$ of its limited contents are common and informative to imputation. A similar pattern was observed when discordance rate was used to evaluate accuracy (supplementary Tables 1 and 2, see online supplementary material). When imputed genotypes with r2 > 0.8 were set as the good coverage target, CAS Array achieved rates of $98.3\%$ and $63.0\%$ for common and rare SNPs respectively, higher than most commercial arrays containing more SNP markers. These results indicate that CAS Array outperformed most commonly used commercial SNP arrays on imputation accuracy and genome coverage despite its limited SNP content. Figure 1.: Comparison of imputation r2 between CAS Array and other SNP arrays. Simulated genotyping results of CAS Array and eight commonly used commercial SNP arrays were extracted from whole-genome sequencing genotypes of 384 Chinese individuals. Imputation was conducted with the simulated array genotyping results and the accuracy was evaluated by imputation r2 stratified by minor allele frequency. ## MCN estimation and validation We developed a pipeline to estimate the MCN from raw genotyping intensity data of the CAS Array and applied it to the validation dataset. After quality control, 378 individuals in the evaluation dataset had their MCN estimated by 47 878 high-quality markers, including 134 mitochondrial markers. The MCN estimated from CAS Array was positively correlated with the MCN estimated from WGS (spearman correlation rho = 0.52, $P \leq 2.2$ × 10−16). For the validation dataset, a total of 8 584 individuals passed quality control and their MCN was estimated by 47 268 high-quality markers, including 166 mitochondrial markers. As shown in Fig. 2, MCN estimates were significantly associated with age, sex, WBC, HEMO, and PLT, in keeping with previous studies.22,43,44 The pipeline was packed into an R package available on GitHub (https://github.com/Zijian-Tian/CASMCN). Figure 2.: Association between mitochondrial copy number estimates and different phenotypes. The plot shows the spearman rank correlation coefficients with 95% confidence intervals (CI) and P-values of the association between mitochondrial copy number estimated from CAS Array and corresponding phenotypes in 8 584 Chinese individuals. ## Discussion We designed an Axiom SNP array that is suitable for high-throughput and low-cost genotyping in large Chinese cohorts. With a limited content of ∼675 k markers, the CAS Array achieved a relatively high genotyping accuracy and high genome coverage via imputation. Given the design features of direct coverage on coding variants and MCN estimation, the CAS Array should become a good choice for biobank-scale genotyping and precision medicine in Chinese population. As with other custom-designed genotyping arrays for biobanks, the main purpose of CAS Array is to facilitate cost-effective large-scale GWAS via imputation.36 The comparison of post-imputation accuracy and genome coverage shows that CAS Array is generally more suitable than most commercial arrays for GWAS in the Chinese population. Axiom APMRA achieved better performance than the CAS array at the low-frequency (MAF < 0.05) end of SNP distribution, but only at the cost of ∼150 k extra rare markers on the array (supplementary Table 3, see online supplementary material). At the high-frequency (MAF > 0.2) end, the Illumina OmniZhongHua (OZH) array outperformed CAS Array at the cost of genotyping a total of 1.1 M SNPs with reduced throughput. Therefore, on the balance of cost effectiveness, CAS Array is a more reliable and attractive option for low-cost and high-throughput genotyping in the Chinese population. In addition to facilitating the marker selection on the CAS array, the high-quality Chinese reference panel also played an important role in improving its imputation performance. Our results show that all SNP arrays had better imputation performance when using the large NyuWa Chinese reference panel compared to the widely-used 1kGP reference panel,17,45 especially on low-frequency SNPs (Fig. 1, supplementary Figs. 1–5, see online supplementary material). This advantage is likely driven by the fact that our reference panel was not only larger than the extended 1kGP panel but also more representative of the Chinese population. As described in the original publications, the 1kGP reference panel included 585 east Asian individuals and only 163 of them are southern Han Chinese.45 In contrast, the NyuWa reference panel consists of 2 562 Chinese individuals from 23 of 34 administrative divisions in China.17 Therefore, the CAS Array would serve genotyping of Chinese individuals better, especially with the large Chinese imputation reference panels that are increasingly available. The designing priority to directly genotype more coding variants is another key feature of CAS Array. This group of variants has been proven by accumulating GWAS results to be the most likely type of causal variants for a wide range of complex phenotypes.46 The direct calling of these variants would enable more accurate genotyping than imputation. In turn, the downstream association analyses and genetic risk profiling would be more powerful and accurate with these directly assayed genotypes. More importantly, these more accurate genotype calls would also benefit the translation of genomic knowledge into potential clinical practice. As suggested by multiple biobanks around the world, pre-emptive genotyping of key pharmacogenetic variants, which are mostly coding variants, would benefit from more reliable genotype data to achieve high specificity.47 CAS Array is the first genotyping array designed with MCN estimation in mind, aiming to better serve the investigations into complex age-related diseases. Compared to other commonly used arrays with dozens to 300 mitochondrial probes, CAS Array harbors 776 mitochondrial SNP markers. Therefore, it has more comprehensive data and statistical power to estimate MCN. We also implemented an array-specific pipeline to estimate MCN from raw genotyping intensity signals. Using the large validation dataset, we further demonstrated that the MCN estimated from CAS Array was indeed associated with established biomarkers, paving the path to use the array for more precision medicine research in the elderly. The CAS Array design is inherently limited by the total number of markers it can carry, in order to meet the requirement of cost-effective genotyping. However, with the support of more comprehensive Chinese reference genome panels, the CAS Array outperformed most commercial arrays in terms of imputation-based GWAS for complex trait gene mapping. Although coding variants were prioritized on the CAS Array, higher coverage of variants with translational potential is still limited. A more purpose-built translation-oriented genotyping array will become a useful tool when more Chinese-specific functional variants are discovered by large-scale biobank studies. It is also worth noting that the accuracy of array-based MCN estimation is prone to technical fluctuations, and is thus more appropriate for large sample investigations. In conclusion, we designed and implemented the CAS Array based on a large comprehensive Chinese reference genome panel. Albeit restricted by the SNP content, its relatively high genotyping accuracy and imputation performance, high coverage of coding variants, and convenient MCN estimation, together make the array a cost-effective tool for large Chinese biobanking and precision medicine studies. ## Conflict of interests The authors declared no conflict of interest. Besides, as an Editorial Board Member of Precision Clinical Medicine, the corresponding author Kaixin Zhou was blinded from reviewing and making decision on this manuscript. ## Author contributions K.Z. and T.X. conceived and designed the study. Z.T., F.C., and J.W. performed the analyses. Z.L., L.Z., and Y.W. recruited the participants. 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--- title: 'Estimated impact from the withdrawal of primary care financial incentives on selected indicators of quality of care in Scotland: controlled interrupted time series analysis' authors: - Daniel R Morales - Mark Minchin - Evangelos Kontopantelis - Martin Roland - Matt Sutton - Bruce Guthrie journal: The BMJ year: 2023 pmcid: PMC10031759 doi: 10.1136/bmj-2022-072098 license: CC BY 4.0 --- # Estimated impact from the withdrawal of primary care financial incentives on selected indicators of quality of care in Scotland: controlled interrupted time series analysis ## Abstract ### Objective To determine whether the withdrawal of the Quality and Outcomes Framework (QOF) scheme in primary care in Scotland in 2016 had an impact on selected recorded quality of care, compared with England where the scheme continued. ### Design Controlled interrupted time series regression analysis. ### Setting General practices in Scotland and England. ### Participants 979 practices with 5 599 171 registered patients in Scotland, and 7921 practices with 56 270 628 registered patients in England in 2013-14, decreasing to 864 practices in Scotland and 6873 in England in 2018-19, mainly due to practice mergers. ### Main outcome measures Changes in quality of care at one year and three years after withdrawal of QOF financial incentives in Scotland at the end of the 2015-16 financial year for 16 indicators (two complex processes, nine intermediate outcomes, and five treatments) measured annually for financial years from 2013-14 to 2018-19. ### Results A significant decrease in performance was observed for 12 of the 16 quality of care indicators in Scotland one year after QOF was abolished and for 10 of the 16 indicators three years after QOF was abolished, compared with England. At three years, the absolute percentage point difference between Scotland and England was largest for recording (by tick box) of mental health care planning (−40.2 percentage points, $95\%$ confidence interval −45.5 to −35.0) and diabetic foot screening (−22.8, −33.9 to −11.7). Substantial reductions were, however, also observed for intermediate outcomes, including blood pressure control in patients with peripheral arterial disease (−18.5, −22.1 to −14.9), stroke or transient ischaemic attack (−16.6, −20.6 to −12.7), hypertension (−13.7, −19.4 to −7.9), diabetes (−12.7, −15.0 to −12.4), or coronary heart disease (−12.8, −14.9 to −10.8), and for glycated haemoglobin control in people with HbA1c levels ≤75 mmol/mol (−5.0, −8.4 to −1.5). No significant differences were observed between Scotland and England for influenza immunisation and antiplatelet or anticoagulant treatment for coronary heart disease three years after withdrawal of incentives. ### Conclusion The abolition of financial incentives in Scotland was associated with reductions in recorded quality of care for most performance indicators. Changes to pay for performance should be carefully designed and implemented to monitor and respond to any reductions in care quality. ## Introduction Pay-for-performance schemes have been implemented in many healthcare systems in both high income and low-middle income countries.1 2 3 Some evidence suggests that pay for performance improves quality of care when introduced, although improvements are only consistently seen for process indicators, are variable between studies, and are typically small at best.1 3 The Quality and Outcomes Framework (QOF) pay-for-performance system for primary care in the National Health Service was introduced in 2004 in all four UK countries (England, Scotland, Wales, and Northern Ireland).4 QOF provided financial incentives for a large number of quality of care indicators aimed at improving the clinical management of chronic diseases, with incentives paid to ensure that a certain proportion of patients on a number of chronic disease registers achieved performance targets. Over time, the scale and scope of financial incentives in QOF was reduced, with fewer indicators incentivised and smaller proportions of general practice income dependent on pay for performance, reflecting concerns about the costs of documenting quality of care without actually improving it, and other unintended adverse consequences, such as neglect of non-incentivised activity.5 6 When QOF was introduced across the UK in 2004, there were 66 clinical quality indicators across 12 domains (eg, diabetes, cancer, and cervical smear tests), along with 56 organisational indicators (eg, relating to medicine management systems). Progressively, almost all organisational indicators have been removed, as where many clinical process indicators (eg, incentives to measure blood pressure) were perceived to be ineffective and unnecessary.7 Simultaneously new domains were added (eg, osteoporosis, depression), with 54 clinical quality indicators across 20 domains incentivised in England in financial year 2015-16. The choice and design of indicators were also increasingly diverging between the UK countries. These changes were consistent with the recommendations of many designers of pay-for performance programmes to refresh incentives to ensure that they were targeted at areas where performance was poor and that they should be withdrawn once improvement was sustained.1 Neither the implementation nor the withdrawal of QOF underwent a planned robust evaluation, however, and relatively few studies have investigated the effect of withdrawal of incentives on quality of care. Consistent with economic theory that targeting external motivation with incentives may crowd out internal professional motivation to improve care,8 qualitative research with providers in high income9 and low income10 11 countries found that incentive withdrawal is perceived to have negative consequences on motivation and quality. The results of studies examining this association quantitatively have been mixed, with some studies finding no decline in quality12 13 14 15 16 17 but others observing a worsening of quality after the removal of incentives,18 19 20 21 22 23 24 often back to similar quality levels before incentivisation or sometimes to lower levels.18 19 20 22 24 Three studies examined the effect of incentive withdrawal on quality of care in QOF. One of these studies examined changes in eight indicators that were incentivised from 2004 to 2006 and found no difference in quality of care after withdrawal of the incentives up to 2012.16 Seven of the eight indicators were, however, process indicators where the process remained partially incentivised in a matching intermediate outcome indicator (for example, incentives to monitor blood pressure in people with coronary heart disease were withdrawn, but practices were still incentivised to control blood pressure in a way that indirectly incentivised blood pressure measurement). In contrast, a study of England-wide incentives for screening of alcohol problem drinking observed no benefit from the introduction of incentivisation in 2008 but a rapid decline in screening to below 2008 levels when incentives were withdrawn in 2015.24 The remaining study examined the impact of withdrawing incentives for 12 indicators in England in 2014 and found that documented quality decreased for all indicators in the first year after financial incentives were removed, with reductions generally being largest for indicators related to documenting the provision of health advice.23 All three studies used time series methods to examine indicators that had been specifically targeted for removal, either because quality was believed to be high and stable or because the indicator was considered to be measuring care that was less important than other potential indicators. Such findings may not be generalisable to the overall question of what happens to performance when financial incentives are withdrawn. Although the NHS is a universal healthcare system across the UK regions, governance and decision making is devolved to country level. In April 2016, Scotland abolished the QOF but continued collecting national data on performance for a subset of QOF indicators. This abolition was done to reduce the bureaucratic burden on general practitioners and to free-up their time for patients. The abolition of QOF in Scotland created a natural experiment to compare indicators that were consistently measured in Scotland and England (where incentives were maintained) before and after April 2016. We evaluated the impact of QOF withdrawal on the quality of care in Scotland across a range of indicators, compared with changes in quality of care in England in the same period. ## Data sources For this controlled interrupted time series analysis of population level data for 16 quality of care indicators we extracted data on QOF primary care indicators in Scotland and England from the electronic medical records of family practices in both countries using UK-wide data specification. Data were extracted and reported annually for financial years April to March, and additionally in Scotland were collected for three financial years after the withdrawal of financial incentives (2016-17, 2017-18, and 2018-19). All data consist of practice level aggregates and are non-disclosive at patient level. English and Scottish QOF data are published and available for download from NHS Digital and Public Health Scotland, respectively. Scottish data post-QOF were collected as part of the transitional quality arrangements and were obtained from Public Health Scotland. Analysis uses population quality (proportion of people with the condition receiving the specified care or achieving the specified target) rather than payment quality where patients are excluded if unsuitable. ## Indicator selection and definition We examined 16 of 25 potential quality of care indicators available during the transitional quality arrangements that were incentivised in both Scotland and England. These covered the three years before and after the withdrawal of QOF in Scotland until the end of the 2018-19 financial year. The 16 indicators included two that required affirmation by tick box to indicate that complex processes had been delivered (completion of a care plan in people with serious mental illness, and comprehensive foot screening in people with diabetes), nine intermediate outcome indicators (blood pressure control in people with peripheral arterial disease, stroke or transient ischaemic attack, hypertension, coronary heart disease, or diabetes), two indicators for different thresholds of blood pressure (≤$\frac{150}{90}$ mm Hg and ≤$\frac{140}{80}$ mm Hg), and three indicators of glycaemic control in people with diabetes (HbA1c (glycated haemoglobin) thresholds ≤75 mmol/mol, ≤64 mmol/mol, and ≤59 mmol/mol), and five treatment indicators (influenza immunisation in people with stroke or transient ischaemic attack, chronic obstructive pulmonary disease (COPD), coronary heart disease, or diabetes, and antithrombotic (antiplatelet or anticoagulant) treatment in people with coronary heart disease). See table 1 for full details of indicators. Table 1 \| Indicator identifier \| Code (formerly) \| Indicator type \| Indicator description \| \| --- \| --- \| --- \| --- \| \| Complex processes \| \| \| \| \| Mental health care planning \| MH02 (MH002(S)) \| Complex process recording \| Percentage of patients with schizophrenia, bipolar affective disorder, and other psychoses who have a comprehensive care plan documented in the record (in preceding 15 months), agreed between individuals, their family, or carers as appropriate \| \| Diabetic foot screening \| DM12 (DM012(S)) \| Complex process recording \| Percentage of patients with diabetes, on the register, with a record of a foot examination and risk classification: low risk (normal sensation, palpable pulses), increased risk (neuropathy or absent pulses), high risk (neuropathy or absent pulses plus deformity or skin changes in previous ulcer), or ulcerated foot within preceding 15 months \| \| Intermediate outcomes \| \| \| \| \| Blood pressure ≤150/90 mm Hg: \| \| \| \| \| Peripheral arterial disease \| PAD02 (PAD002(S)) \| Intermediate outcome \| Percentage of patients with peripheral arterial disease in whom the last blood pressure reading (measured in preceding 15 months) is ≤150/90 mm Hg \| \| Stroke or transient ischaemic attack \| STIA03 (STIA003(S)) \| Intermediate outcome \| Percentage of patients with a history of stroke or transient ischaemic attack in whom the last blood pressure reading (measured in preceding 15 months) is ≤150/90 mm Hg \| \| Hypertension \| HYP06 (HYP006(S)) \| Intermediate outcome \| Percentage of patients with hypertension in whom the last blood pressure reading (measured in preceding 12 months) is ≤150/90 mm Hg \| \| Coronary heart disease \| CHD02 (CHD002(S)) \| Intermediate outcome \| Percentage of patients with coronary heart disease in whom the last blood pressure reading (measured in preceding 15 months) is ≤150/90 mm Hg \| \| Diabetes \| DM02 (DM002(S)) \| Intermediate outcome \| Percentage of patients with diabetes, on the register, in whom the last blood pressure reading (measured in preceding 15 months) is ≤150/90 mm Hg \| \| Blood pressure ≤140/80 mm Hg: \| \| \| \| \| Diabetes \| DM03 (DM003(S)) \| Intermediate outcome \| Percentage of patients with diabetes, on the register, in whom the last blood pressure reading (measured in preceding 15 months) is ≤140/80 mm Hg \| \| HbA1c (mmol/mol): \| \| \| \| \| ≤75 \| DM09 (DM009(S)) \| Intermediate outcome \| Percentage of patients with diabetes, on the register, in whom the last IFCC-HbA1c is ≤75 mmol/mol in preceding 15 months \| \| ≤64 \| DM08 (DM008(S)) \| Intermediate outcome \| Percentage of patients with diabetes, on the register, in whom the last IFCC-HbA1c is ≤64 mmol/mol in preceding 15 months \| \| ≤59 \| DM07 (DM007(S)) \| Intermediate outcome \| Percentage of patients with diabetes, on the register, in whom the last IFCC-HbA1c is ≤59 mmol/mol in preceding 15 months \| \| Treatments \| \| \| \| \| Influenza immunisation: \| \| \| \| \| Stroke or transient ischaemic attack \| STIA09 (STIA009(S)) \| Treatment \| Percentage of patients with stroke or transient ischaemic attack who have had influenza immunisation in preceding 1 August to 31 March \| \| COPD \| COPD07 (COPD007(S)) \| Treatment \| Percentage of patients with COPD who have had influenza immunisation in preceding 1 August to 31 March \| \| Coronary heart disease \| CHD07 (CHD007(S)) \| Treatment \| Percentage of patients with coronary heart disease who have had influenza immunisation in preceding 1 August to 31 March \| \| Diabetes \| DM18 (DM018(S)) \| Treatment \| Percentage of patients with diabetes, on the register, who have had influenza immunisation in preceding 1 August to 31 March \| \| Antiplatelet/oral anticoagulants in coronary heart disease \| CHD05 (CHD005(S)) \| Treatment \| Percentage of patients with coronary heart disease with a record in preceding 15 months that aspirin, an alternative antiplatelet treatment, or an anticoagulant is being taken \| ## Statistical analysis Data for the 16 indicators consisted of three annual measurements before and three annual measurements after the year financial incentives were withdrawn in Scotland. We defined performance as the percentage of patients on each disease register who were not excluded by automatic criteria, such as recent practice registration, who received the specified care. We plotted the time series to check the validity of the data and to confirm assumptions of linearity. The focus of our study was the estimated change in quality of care performance one year and three years after 2015-16 compared with that expected based on the pre-intervention trend. We specified this in a stepwise approach. Initially we used single group analysis of trends in Scotland before and after withdrawal of financial incentives at the end of the 2015-16 financial year, using interrupted time series linear regression to estimate immediate changes in quality in 2016-17, and change in trend. In the subsequent primary analysis, we performed interrupted time series linear regression analysis for multiple groups using the itsa command in Stata to examine changes in recorded quality in Scotland relative to changes in England used as a control.25 We used this analysis to calculate absolute differences in documented quality of care in Scotland compared with England at three years after the removal of financial incentives. Analyses were conducted in Stata version 14. ## Patient and public involvement Although no patients or members of the public were involved in the conduct of the study owing to covid-19 restrictions, the idea for the study was inspired by speaking to patients and healthcare professionals while working in primary care before the pandemic. ## Results The analysis included data from 979 general practices with 5 599 171 registered patients in Scotland and 7921 practices with 56 270 628 registered patients in England in 2013-14; a decline in practice numbers to 864 in Scotland and 6873 in England in 2018-19 was mainly because of practice mergers. ## Single country analyses in Scotland In the single country analyses, trends in quality of care for seven of the 16 indicators were shown to be declining before the removal of financial incentives in Scotland for three intermediate outcomes (in people with diabetes: blood pressure ≤$\frac{150}{90}$ mm Hg, HbA1c ≤64 mmol/mol, and HbA1c ≤59 mmol/mol) and for four treatment indicators (influenza immunisation in people with stroke or transient ischaemic attack, COPD, coronary heart disease, or diabetes), although absolute changes from year to year were small (table 2). For the remainder of the indicators, no significant trend was observed during the baseline period. Table 2 \| Indicator \| Code* \| Baseline performance 2013-14 (%) \| Trend before QOF withdrawal (percentage point change per year) (95% CI) \| Step change in year after QOF withdrawal (percentage point change)† (95% CI) \| Change in trend after QOF withdrawal (additional percentage point change per year) (95% CI) \| End performance 2018-19 (%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| \| Mental health care planning \| MH02 \| 64.9 \| −0.4 (−0.8 to 0.01) \| −30.4 (−35.2 to −25.5) \| −4.4 (−7.6 to −1.4) \| 24.3 \| \| Diabetic foot screening \| DM12 \| 80.0 \| 1.0 (−3.0 to 5.0) \| −12.6 (−22.5 to −2.8) \| −4.5 (−9.2 to 0.1) \| 64.4 \| \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| \| Blood pressure ≤150/90 mm Hg: \| \| \| \| \| \| \| \| Peripheral arterial disease \| PAD02 \| 85.3 \| 0.1 (−0.7 to 1.0) \| −11.8 (−16.4 to −7.3) \| −2.8 (−5.6 to −0.01) \| 69.0 \| \| Stroke or transient ischaemic attack \| STIA03 \| 85.0 \| −0.1 (−1.1 to 1.0) \| −9.0 (−12.7 to −5.2) \| −2.6 (−4.7 to −0.4) \| 71.2 \| \| Coronary heart disease \| CHD02 \| 87.6 \| −0.1 (−0.6 to 0.4) \| −7.3 (−9.6 to −5.0) \| −2.2 (−3.6 to −0.8) \| 75.9 \| \| Diabetes \| DM02 \| 85.7 \| −0.5 (−0.7 to −0.6) \| −5.3 (−8.0 to −2.6) \| −1.8 (−3.4 to −0.1) \| 77.0 \| \| Hypertension \| HYP06 \| 79.0 \| 0.1 (−0.7 to 0.9) \| −11.0 (−14.7 to −7.2) \| −2.0 (−4.25 to 0.3) \| 65.2 \| \| Blood pressure ≤140/80 mm Hg: \| \| \| \| \| \| \| \| Diabetes \| DM03 \| 67.8 \| −0.8 (−0.9 to 0.6) \| −6.8 (−9.6 to −4.0) \| −1.8 (−3.6 to 0.03) \| 54.0 \| \| HbA1c (mmol/mol): \| \| \| \| \| \| \| \| ≤75 \| DM09 \| 78.2 \| 0.04 (−0.7 to 0.8) \| −1.9 (−3.6 to −0.1) \| −0.6 (−1.4 to 0.2) \| 75.6 \| \| ≤64 \| DM08 \| 65.9 \| −0.6 (−1.2 to −0.1) \| −0.6 (−1.9 to 0.7) \| −0.1 (−0.6 to 0.5) \| 62.2 \| \| ≤59 \| DM07 \| 57.2 \| −1.1 (−1.9 to −0.2) \| 0.3 (−1.7 to 2.3) \| 0.3 (−0.6 to 1.2) \| 52.9 \| \| Treatments \| Treatments \| Treatments \| Treatments \| Treatments \| Treatments \| Treatments \| \| Influenza immunisation: \| \| \| \| \| \| \| \| Stroke or transient ischaemic attack \| STIA09 \| 78.7 \| −1.0 (−1.5 to −0.6) \| −3.5 (−5.4 to −1.6) \| 0.6 (−0.5 to 1.8) \| 71.2 \| \| COPD \| COPD07 \| 81.4 \| −1.4 (−1.6 to −1.16) \| −3.2 (−5.1 to −1.3) \| 0.6 (−0.6 to 1.8) \| 72.4 \| \| Coronary heart disease \| CHD07 \| 82.1 \| −1.2 (−2.0 to −0.46) \| −2.7 (−5.1 to −0.3) \| 0.6 (−0.8 to 1.9) \| 74.6 \| \| Diabetes \| DM18 \| 78.2 \| −1.4 (−1.9 to −1.0) \| −3.0 (−5.8 to −0.2) \| 0.6 (−1.1 to 2.4) \| 69.0 \| \| Antiplatelet or oral anticoagulation in coronary heart disease \| CHD05 \| 91.7 \| 0.1 (−0.6 to 0.8) \| −0.9 (−2.5 to 0.6) \| −0.7 (−1.4 to 0.01) \| 90.1 \| One year after the removal of financial incentives, decreases in quality were documented for 13 of the 16 indicators in Scotland. Reductions occurred in recording of both complex processes (mental health care planning and diabetic foot screening), seven intermediate outcomes (blood pressure ≤$\frac{150}{90}$ mm Hg in people with peripheral arterial disease, stroke or transient ischaemic attack, hypertension, coronary heart disease, and diabetes; blood pressure control ≤$\frac{140}{80}$ mm Hg in people with diabetes; and HbA1c ≤75 mmol/mol), and four treatment indicators (influenza immunisation in people with stroke or transient ischaemic attack, COPD, coronary heart disease, or diabetes). Reductions at one year ranged from −30.4 percentage points ($95\%$ confidence interval −35.2 to −25.5) for mental health care planning to −1.9 (−3.6 to −0.1) for HbA1c ≤75 mmol/mol. A change to a negative trend occurred in five of the 16 indicators over the three year period (mental health care planning, and blood pressure ≤$\frac{150}{90}$ mm Hg in people with peripheral arterial disease, stroke or transient ischaemic attack, coronary heart disease, or diabetes). Negative trends ranged from −4.4 percentage point change per year ($95\%$ confidence interval −7.6 to −1.4) for mental health care planning to −1.8 percentage points per year (−3.4 to −0.1) for blood pressure ≤$\frac{150}{90}$ mm Hg in people with diabetes. Supplementary figure S1 shows the results of the single group trend analysis for Scotland and supplementary figure S2 shows the results for England. ## Multiple group analysis In the multiple group analysis when data from England were included as control, statistically significant reductions were still observed in 12 of the 16 indicators in Scotland one year after removal of QOF (table 3). Large reductions were still observed in recording of both complex processes: mental health care planning (−31.0 percentage points, $95\%$ confidence interval −35.0 to −27.1) and diabetic foot screening (−13.8, −20.4 to −7.2). Statistically significant reductions were also observed in eight intermediate outcomes: blood pressure ≤$\frac{150}{90}$ mm Hg in people with peripheral arterial disease, stroke or transient ischaemic attack, hypertension, coronary heart disease, or diabetes; blood pressure ≤$\frac{140}{80}$ in people with diabetes; and HbA1c ≤75 mmol/mol or ≤64 mmol/mol. Statistically significant reductions in intermediate outcomes at one year ranged from −12.5 percentage points (−15.6 to −9.4) for blood pressure ≤$\frac{150}{90}$ mm Hg in people with peripheral arterial disease to −2.4 percentage points (−4.8 to −0.05) for HbA1c ≤64 mmol/mol. Statistically significant reductions at one year were, however, only observed for two treatment indicators—influenza immunisation in people with stroke or transient ischaemic attack (−3.9, −6.9 to −0.9) or with COPD (−3.8, −6.9 to −0.8). Table 3 \| Indicator \| Code* \| % (95% CI) \| % (95% CI).1 \| % (95% CI).2 \| \| --- \| --- \| --- \| --- \| --- \| \| Indicator \| Code* \| Change at 1 year post-QOF Scotland v England \| Difference in trend post-QOF Scotland v England \| Absolute difference between Scotland and England at 3 years \| \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| Complex processes \| \| Mental health care planning \| MH02 \| −31.0 (−35.0 to −27.1) \| −4.6 (−6.7 to −2.5) \| −40.2 (−45.5 to −35.0) \| \| Diabetic foot screening \| DM12 \| −13.8 (−20.4 to −7.2) \| −3.2 (−5.0 to −1.3) \| −22.8 (−33.9 to −11.7) \| \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| Intermediate outcomes \| \| Blood pressure ≤150/90 mm Hg: \| \| \| \| \| \| Peripheral arterial disease \| PAD02 \| −12.5 (−15.6 to −9.4) \| −2.7 (−4.5 to −0.8) \| −18.5 (−22.1 to −14.9) \| \| Stroke or transient ischaemic attack \| STIA03 \| −10.2 (13.0 to −7.4) \| −2.4 (−3.8 to −1.1) \| −16.6 (−20.6 to −12.7) \| \| Hypertension \| HYP06 \| −10.5 (−14.3 to −6.8) \| −1.7 (−3.3 to −0.1) \| −13.7 (−19.4 to −7.9) \| \| Coronary heart disease \| CHD02 \| −8.0 (−9.7 to −6.3) \| −2.2 (−3.1 to −1.2) \| −12.8 (−14.9 to −10.8) \| \| Diabetes \| DM02 \| −6.2 (−8.2 to −4.1) \| −1.7 (−2.8 to −0.5) \| −10.4 (−13.0 to −7.8) \| \| Blood pressure ≤140/80 mm Hg: \| \| \| \| \| \| Diabetes \| DM03 \| −7.8 (−10.1 to −5.6) \| −2.4 (−3.9 to −1.0) \| −12.7 (−15.0 to −10.4) \| \| HbA1c (mmol/mol): \| \| \| \| \| \| ≤75 \| DM09 \| −3.2 (−5.4 to −0.9) \| −0.4 (−1.4 to 0.5) \| −5.0 (−8.4 to −1.5) \| \| ≤64 \| DM08 \| −2.4 (−4.8 to −0.05) \| −0.5 (−1.6 to 0.7) \| −3.4 (−6.7 to −0.03) \| \| ≤59 \| DM07 \| −1.9 (−4.5 to 0.8) \| −0.5 (−1.8 to 0.9) \| −2.1 (−5.7 to 1.6) \| \| Treatments \| \| \| \| \| \| Influenza immunisation: \| \| \| \| \| \| Stroke or transient ischaemic attack \| STIA09 \| −3.9 (−6.9 to −0.9) \| −0.1 (−1.7 to 1.5) \| −3.9 (−7.8 to 0.1) \| \| COPD \| COPD07 \| −3.8 (−6.9 to −0.8) \| −0.2 (−1.9 to 1.5) \| −3.4 (−7.3 to 0.4) \| \| Coronary heart disease \| CHD07 \| −3.2 (−6.3 to 0.03) \| −0.2 (−1.8 to 1.4) \| −3.2 (−7.6 to 1.2) \| \| Diabetes \| DM18 \| −3.3 (−6.9 to 0.2) \| −0.01 (−1.8 to 1.8) \| −2.4 (−7.2 to 2.5) \| \| Antiplatelet or oral anticoagulation in coronary heart disease \| CHD05 \| −0.8 (−1.8 to 0.3) \| −0.4 (−0.4 to −0.3) \| −1.4 (−3.3 to 0.6) \| Compared with baseline trends, a statistically significant change to a negative trend was observed in nine indicators over the three year period in Scotland (two complex processes, six intermediate outcomes, and one treatment outcome) (table 3, fig 1, fig 2, and fig 3). Reductions ranged from −4.6 percentage point change per year ($95\%$ confidence interval −6.7 to −2.5) for mental health care planning to −0.4 percentage point change per year (−0.4 to −0.3) for antiplatelet or oral anticoagulant treatment in people with coronary heart disease. Fig 1: Multiple group interrupted time series analysis comparing indicators related to complex processes between Scotland and England. Vertical reference line indicates the first observation after withdrawal of the Quality and Outcomes Framework (QOF) at the end of the 2015-16 financial year in Scotland. The counterfactual for Scotland is based on Scottish prior trends and English immediate and post-QOF withdrawal effects. Regression was performed with Newey-West standard errors, and lag(0) Fig 2: Multiple group interrupted time series analysis comparing indicators related to intermediate outcomes between Scotland and England. Vertical reference line indicates the first observation after withdrawal of the Quality and Outcomes Framework (QOF) at the end of the 2015-16 financial year in Scotland. The counterfactual for Scotland is based on Scottish prior trends and English immediate and post-QOF withdrawal effects. Regression was performed with Newey-West standard errors, and lag(0). BP=blood pressure; CHD=coronary heart disease; HbA1c=glycated haemoglobin; TIA=transient ischaemic attack Fig 3: Multiple group interrupted time series analysis comparing treatment related indicators between Scotland and England. Vertical reference line indicates the first observation after withdrawal of the Quality and Outcomes Framework (QOF) at the end of the 2015-16 financial year in Scotland. The counterfactual for Scotland is based on Scottish prior trends and English immediate and post-QOF withdrawal effects. Regression was performed with Newey-West standard errors, and lag(0). CHD=coronary heart disease; COPD=chronic obstructive pulmonary disease; TIA=transient ischaemic attack The absolute percentage point difference between Scotland and England at three years after withdrawal of financial incentives was observed to be statistically significant for 10 indicators (two complex processes and eight intermediate outcomes). The largest reductions were observed for the two complex processes of mental health care planning (absolute difference in percentage points −40.2, $95\%$ confidence interval −45.5 to −35.0) and diabetic foot screening (−22.8, −33.9 to −11.7). Substantial reductions were also observed in blood pressure related intermediate outcomes compared with England for ≤$\frac{150}{90}$ mm Hg in people with peripheral arterial disease (−18.5, −22.1 to −14.9), stroke or transient ischaemic attack (−16.6, −20.6 to −12.7), hypertension (−13.7, −19.4 to −7.9), or coronary heart disease (−12.8, −14.9 to −10.8), and blood pressure ≤$\frac{140}{80}$ mm Hg in people with diabetes (−12.7, −15.0 to −10.4). Reductions in HbA1c intermediate outcomes were smaller but still observed to be significant for HbA1c ≤75 mmol/mol (−5.0, −8.4 to −1.5) and ≤64 mmol/L (−3.4, −6.7 to −0.03) but not for HbA1c ≤59 mmol/mol (−2.1, −5.7 to 1.6). At three years, no significant difference was observed for any of the treatment indicators between Scotland and England (table 3). ## Discussion In time series analysis of Scotland-wide data, withdrawal of QOF financial incentives in 2016 was associated with a reduction in documented quality of care in 12 of the 16 indicators measured at one year, and with a negative change in trend over the three year period in nine of the 16 indicators compared with England (where incentives were maintained). Similarly, statistically significant reductions were also documented for quality of care in Scotland three years after withdrawal for 10 of the 16 indicators examined, which were large (>20 percentage point differences) for the two complex processes, large for blood pressure control (10-20 percentage point differences), and small for two of the three glycaemic control indicators (<5 percentage point differences). We found no statistically significant differences between Scotland and England for indicators of glycaemic control in diabetes, or for the treatment indicators at three years; although small, short term reductions were observed at one year. ## Strengths and limitations of this study Two key strengths of the study are the use of interrupted time series analysis, which is a robust method for examining the impact of an intervention when randomisation is not possible,26 and the availability of population data from two countries where the indicators examined were incentivised in both countries before April 2016, but with incentives subsequently withdrawn in Scotland and maintained in England. Limitations are that the time series included relatively few time points (three years before and three years after the withdrawal of QOF) and precludes examination for the presence of autocorrelation. Follow-up duration is, however, constrained by the post-QOF Scottish data being collected for only three years and by the onset of the covid-19 pandemic. The size of each dataset also meant that there was relatively little noise (random variation) in the time series data, and the availability of an English control population increases confidence that the observed changes in Scotland were the result of withdrawal of financial incentives. A further limitation is that we restricted analysis to indicators that were implemented in both England and Scotland in the three years April 2013 to March 2016 and therefore our study does not cover the full range of indicators implemented in each country in this period. Reasons for this are because different indicator definitions were used or because QOF indicators might have been removed over time, such as the percentage of patients with myocardial infarction treated with an angiotensin converting enzyme inhibitor (or angiotensin II receptor blocker if intolerant to an angiotensin converting enzyme inhibitor), aspirin or an alternative antiplatelet treatment, β blocker, or statin, retired in April 2015 in England. The 16 indicators we examined represent $30\%$ of the clinical indicators implemented in Scotland in financial year 2015-16, but they are weighted towards cardiovascular domains and might not be representative of all indicators. The findings are, however, consistent with those observed in published analysis of a different range of withdrawn indicators in England, and we believe the results are likely to be generalisable.23 The interrupted time series method assumes no other exogenous factors—in this case, whether another local or national policy change or intervention occurred during the study period. No other national interventions targeted the examined indicators, although we cannot rule out changes at local level (eg, clinical commissioning group or practice). Even if such changes had occurred, it is unlikely to explain the effect sizes we observed. Importantly, we examined changes in incentivised quality of care, which is only a subset of care processes and outcomes, and we did not examine the impact on non-incentivised or hard to measure care because the data are not available. ## Comparison with other studies Several studies have documented declines in quality of care after the withdrawal of financial incentives,18 19 20 21 22 23 24 often back to levels similar to or worse than before the incentivisation.18 19 20 22 24 However, other studies have observed no decline in quality of care,13 14 15 16 17 although for one of these studies, care remained partially incentivised through other indicators for seven of the eight measures examined.16 Another study that followed-up a trial of incentives for diabetes care did not observe declines in quality when incentives were withdrawn, but the study also did not observe consistent improvements when incentives were introduced, making lack of change after withdrawal hard to interpret.15 27 For the remaining three studies where quality did not decline when incentives were withdrawn, incentives were part of more comprehensive quality improvement interventions13 14 17 and it is plausible that other intervention components led to sustainability. Consistent with this finding, a factorial trial of short term (12 week) financial incentives, and training and support interventions for alcohol screening and intervention found sustained (nine month) benefit of training and support but not of incentives.22 Our study examined a larger set of indicators, and, as with previous analysis of England only data,23 we observed no decline in quality for some indicators and decline in quality for others. Despite the observed changes in quality of care, it remains likely that some of the observed decline in Scotland relates to a reduction in documentation within the medical record, as opposed to care not being delivered. This is consistent with our observation that changes in treatment or glycaemic control were small and often not statistically significant, whereas changes in blood pressure control and the recording of complex process delivery by tick box were larger. A key difference between these types of indicators is that prescriptions and laboratory tests were reliably captured within the electronic medical record if they were done, because almost all prescriptions are created electronically, and practice requested laboratory tests are automatically entered into the record. In contrast, blood pressure measurements and complex processes can be recorded in free text as well in the coded fields counted in QOF indicators, and both are therefore more prone to gaming. For example, practices can decide which blood pressures to record as values (counted in indicators) versus in free text (visible for clinical care but not counted in indicators), and evidence suggests differences in the how blood pressures were documented when the thresholds for incentivised indicators were revised in the past.12 Changes in the recording of complex processes are even harder to interpret,23 since payment depends on ticking a box affirming that, for example, a care plan has been completed, but with no evidence required as to quality or completeness. The extent to which the large changes in complex process recording represent equally large reductions in quality of care is therefore uncertain. It is possible, however, that these differences might not be solely due to documentation because they are relatively time consuming, and it is possible that they may not be prioritised. This difficulty in observing the actual effects of incentives is a dilemma for all evaluations of pay-for-performance schemes, where improvements in performance on introduction of the incentives and declines on their withdrawal may both be primarily driven by changes in documentation. We focused on treatment and glycaemic control indicators where documentation of performance is least likely to have an impact, and we concluded that withdrawal of financial incentives likely had small negative effects on actual quality of care. ## Policy implications Our results suggest that removal of QOF in Scotland was associated with a reduction in documented quality of care for some but not all indicators, with variation in the relative size of changes and only small reductions in indicators that were least subject to changes in how practices document (as opposed to deliver) care. These findings are highly relevant to designers of pay-for-performance and healthcare quality improvement programmes internationally, as well as to post-covid-19 discussions about the future of QOF in the other UK countries.28 29 Assuming that high levels of quality of incentivised care will be sustained after incentives are withdrawn is problematic, and so retaining the ability to evaluate what happens in pay-for-performance systems after their removal is critical. A key recommendation therefore should be that data continue to be collected for a period after the withdrawal of any indicator or performance scheme to monitor the impact, and ideally that data are collected in ways that minimise the effect of documentation. For example, evaluation of changes in recorded blood pressure would be usefully complemented by collection of data on the intensity of antihypertensive treatment. Responses to any observed changes would then be better based on evidence, allowing the targeting of quality improvement interventions (of which incentives are only one) where required. Research examining what happens when incentives are withdrawn has largely focused on changes in incentivised measures, and more work is required to understand the actual impact on quality of incentivised care (for example, by teasing out changes in documentation and gaming from actual changes in care, for example) such as when examining prescriptions of antihypertensives as well as values of recorded blood pressure together with admissions for incident myocardial infarction in people with hypertension. Additionally, the introduction of incentives was associated with a negative impact on the quality of care for non-incentivised conditions,5 and evaluating quality of care more broadly would be invaluable, not least because the withdrawal of QOF incentives in Scotland was accompanied by the introduction of new approaches to quality improvement,30 and this new approach may have had positive effects on general practitioner satisfaction, recruitment, and retention.31 Although improvements in quality of care from the introduction of QOF appeared relatively small, evidence suggested that QOF reduced variation between practices and in particular narrowed the quality gap between practices serving socioeconomically deprived versus affluent practices.5 Research is needed to examine the impact on variation between practices and inequities of withdrawing incentives. Finally, randomised controlled trials are also needed into the effects of large scale incentive schemes with embedded process evaluation. ## Conclusion Withdrawal of QOF in Scotland in 2016 compared with England where financial incentives were maintained was associated with reductions in recorded quality of care for 12 of 16 indicators after one year and 10 of 16 indicators after three years. Further research is needed to better understand the full impact of QOF withdrawal and the accompanying refocusing of quality improvement resources. ## Ethical approval Not required as the study used routinely collected and publicly available aggregate practice level data. ## Data availability statement English Quality and Outcomes Framework (QOF) data are publicly available, as are the Scottish QOF data before withdrawal of incentives. Scottish data on quality of care in the three years after QOF withdrawal (transitional quality arrangements data) can be obtained from Public Health Scotland. 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--- title: Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors authors: - Jiahua Lyu - Ningjing Yang - Ling Xiao - Xinyu Nie - Jing Xiong - Yudi Liu - Min Zhang - Hangyue Zhang - Cunhan Tang - Shiyi Pan - Long Liang - Hansong Bai - Churong Li - Hao Kuang - Tao Li journal: Frontiers in Nutrition year: 2023 pmcid: PMC10031770 doi: 10.3389/fnut.2023.1113875 license: CC BY 4.0 --- # Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors ## Abstract ### Objectives It remains controversial whether sarcopenia has any significant impact on the efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) or immune checkpoint inhibitors (ICIs) in patients with advanced non-small cell lung cancer (NSCLC). Therefore, in this study, we aimed to assess the association between sarcopenia and clinical outcomes in patients with advanced NSCLC receiving EGFR-TKIs or ICIs as a first-line therapy. ### Methods We retrospectively enrolled 131 patients with advanced NSCLC treated with first-line EGFR-TKIs or ICIs between 1 March 2019 and 31 March 2021. To estimate sarcopenia, we calculated skeletal muscle index (SMI) as the ratio of skeletal muscle area (cm2) to height squared (m2). Associations between sarcopenia and overall survival (OS) and progression-free survival (PFS) were evaluated using the Kaplan–Meier method and log-rank tests, respectively. A Cox proportional hazards regression model was used to assess the factors associated with OS and PFS. The Student’s t-test or Mann–Whitney U test was used to compare the SMI between patients with or without objective response and disease control. The chi-squared test was used to compare adverse events (AEs) between patients with and without sarcopenia. ### Results Among the 131 patients, 35 ($26.7\%$) were diagnosed with sarcopenia. Sarcopenia was an independent predictor of poor OS and PFS ($p \leq 0.05$) overall and in the EGFR-TKI- and ICI-treated cohorts. Among all patients, those with sarcopenia showed significantly shorter OS and PFS than those without sarcopenia (median OS and PFS: 13.0 vs. 26.0 months and 6.4 vs. 15.1 months; both $p \leq 0.001$). These associations were consistent across the subtypes of most clinical characteristics. Statistically significant differences between the objective response (OR) and non-OR groups were also observed in the mean SMI (OR group, 43.89 ± 7.55 vs. non-OR group, 38.84 ± 7.11 cm2/m2; $p \leq 0.001$). In addition, we observed similar results with disease control (DC) and non-DC groups (DC group, 42.46 ± 7.64 vs. non-DCR group, 33.74 ± 4.31 cm2/m2; $p \leq 0.001$). The AEs did not differ significantly between the sarcopenia and non-sarcopenia groups. ### Conclusion Sarcopenia before treatment might be a significant predictor of poor clinical outcomes (shorter OS and PFS, fewer ORs, less DC) in patients with advanced NSCLC treated with EGFR-TKIs or ICIs as the first-line therapy. ## 1. Introduction Lung cancer is the second most common malignant tumor and the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) comprises the majority ($85\%$) of all lung cancers [1]. Approximately, $25\%$ of NSCLC patients present with an advanced stage at initial diagnosis [2], with a 5-year survival rate of less than $20\%$ [3]. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and immune checkpoint inhibitors (ICIs) have been shown to significantly improve the survival of metastatic NSCLC patients with mutant and wild-type EGFR, respectively [4, 5]. However, not all eligible patients can benefit equally from EGFR-TKIs or ICIs [6, 7]. Although EGFR mutation and the PD-L1 expression level have been reported as potential predictors of the therapeutic efficacy for EGFR-TKIs and ICIs, it is essential to identify additional biomarkers that can help determine those patients most likely to benefit from these therapies. Sarcopenia is characterized by progressive loss of skeletal muscle strength and mass and is associated with decreased muscle protein synthesis and increased protein degradation [8, 9]. Sarcopenia has been reported in approximately $50\%$ of lung cancer patients and is associated with a decrease in the efficacy of surgery or chemotherapy, toxicity, and a worse quality of life [10, 11]. However, the impact of sarcopenia on the efficacy and toxicity of EGFR-TKIs and ICIs remains unclear. Several articles have suggested that sarcopenia is correlated with poor clinical outcomes in patients receiving PD-1/PD-L1 inhibitors [12, 13], however, other researches have reached inconsistent conclusions (14–16). The same controversy also exists In NSCLC patients treated with EGFR-TKIs. A retrospective study showed that sarcopenia did not affect the response to gefitinib in patients with EGFR-mutated NSCLC [17]. In contrast, another retrospective study enrolling 72 NSCLC patients treated with erlotinib found that sarcopenia was a negative biomarker that was significantly associated with response and survival outcomes [18]. In brief, there is no consensus as to whether sarcopenia is a prognostic biomarker for EGFR-TKI or ICI treatment of advanced NSCLC, especially when used as the first-line treatment. Consequently, we sought to investigate the potential predictive value of sarcopenia on the efficacy of EGFR-TKIs in NSCLC patients harboring EGFR mutations or of ICIs in patients with wild-type EGFR. ## 2.1. Patients In this study, we retrospectively collected the data of patients with pathologically confirmed metastatic NSCLC who were treated with EGFR-TKIs or ICIs as the first-line therapy at Sichuan Cancer Hospital, China, from 16 January 2018 to 8 June 2021. Patients who met the inclusion criteria below were enrolled: [1] histologically confirmed stage IV metastatic NSCLC; [2] treated with EGFR-TKIs or ICIs as first-line therapies; and [3] underwent a chest/abdominal CT scan within 4 weeks prior to EGFR-TKI or ICI therapy. This study was approved by the Ethics Committee of Sichuan Cancer Hospital and carried out in strict accordance with the Declaration of Helsinki. ## 2.2. Data collection We consecutively enrolled 205 patients with advanced NSCLC treated with EGFR-TKIs or ICIs in our hospital. After selection according to the inclusion and exclusion criteria, 54 eligible patients receiving ICIs and 77 eligible patients receiving EGFR-TKIs were included (Supplementary Figure 1). Subsequently, we obtained basic demographic and clinical data for all eligible patients, including age, sex, history of smoking and alcohol consumption, Karnofsky performance status (KPS) score, histopathology, height, weight, routine biochemical and hematological test results, CT images, EGFR mutation status, PD-L1 expression before EGFR-TKI or ICI initiation, treatment option, treatment response, and toxicity. The follow-up date ended at the date of the outcome event, the date of death, or the end of follow-up, whichever came first. ## 2.3. Skeletal muscle measurement and definition of sarcopenia The chest/abdomen CT scan for sarcopenia evaluation was obtained within 4 weeks before the start of EGFR-TKIs or ICIs. The skeletal muscle area was measured at the L3 level by two experienced radiologists (NJY and XYN) using sliceOmatic (TomoVision 5.0, Magog, QC, Canada) with –29 to 150 Hounsfield unit (HU) (Supplementary Figure 2). The skeletal muscle index (SMI) was calculated using the formula (L3 muscle area in cm2)/(patient height in m2). Sarcopenia was defined as a low SMI as follows: [1] for women, SMI < 31.6 cm2/m2; [2] for men, SMI < 40.2 cm2/m2 [19]. ## 2.4. Follow-up Tumor response evaluations were performed based on chest CT according to the Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1 (for patients receiving EGFR-TKIs) or iRECIST criteria (for patients receiving ICIs). PFS and overall survival (OS) were calculated from the date of initiation of EGFR-TKI or ICI treatment to the date of progression (for PFS) or patient death (for OS) or to the last follow-up. The incidence and severity of all adverse events (AEs) were monitored and evaluated according to the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE V5.0). ## 2.5. Statistical analysis We used R software, version 4.0.2 (R Foundation for Statistical Computing), for statistical analysis. Multiple Cox regression analyses were conducted to evaluate the impact of sarcopenia and other candidate prognostic factors on the OS and PFS. The Kaplan–Meier method and log-rank test were used to compare OS and PFS between patients with and without sarcopenia. The association between the presence of sarcopenia and demographic, clinical, and laboratory parameters, treatment response, and occurrence of AEs was established using the exact Fisher test and χ2 test. Scatter plots were used to graphically represent the association between sarcopenia and hemoglobin, total protein, albumin, prealbumin, serum triglyceride, and serum cholesterol levels and BMI using Spearman’s correlation. All statistics were two-tailed, and p-values ≤ 0.05 were considered statistically significant. ## 3.1. Patient characteristics The baseline characteristics of the 131 enrolled patients are presented in Table 1. A total of 77 and 54 patients received first-line treatment with EGFR-TKIs or ICIs, respectively. Patients were categorized into two groups (sarcopenia and non-sarcopenia) according to the previously defined criteria for sarcopenia. Among all patients, 35 ($26.7\%$) were diagnosed with sarcopenia, including 17 EGFR-TKI-treated and 18 ICI-treated patients. A full comparison of the baseline characteristics between the sarcopenia and non-sarcopenia groups is presented in Table 1. Sarcopenia was significantly more common in patients who had smoked ($$p \leq 0.038$$) or drunk ($$p \leq 0.041$$) regularly, or those who had a low KPS score ($$p \leq 0.002$$) or low albumin ($$p \leq 0.023$$). The baseline characteristics of the EGFR-TKI and ICI cohorts are listed in the Supplementary Tables 1, 2, respectively. TABLE 1 \| Characteristic \| Non-sarcopenia \| Sarcopenia \| p-value \| \| --- \| --- \| --- \| --- \| \| N \| 96(73.3%) \| 35(26.7%) \| \| \| Sex, n (%) \| \| \| 0.076 \| \| Female \| 44 (45.8%) \| 10 (28.6%) \| \| \| Male \| 52 (54.2%) \| 25 (71.4%) \| \| \| Smoking, n (%) \| \| \| 0.038 \| \| No \| 58 (60.4%) \| 14 (40%) \| \| \| Yes \| 38 (39.6%) \| 21 (60%) \| \| \| Drinking, n (%) \| \| \| 0.041 \| \| No \| 77 (80.2%) \| 22 (62.9%) \| \| \| Yes \| 19 (19.8%) \| 13 (37.1%) \| \| \| Histopathology, n (%) \| \| \| 0.764 \| \| AC \| 79 (82.3%) \| 28 (80%) \| \| \| SCC \| 17 (17.7%) \| 7 (20%) \| \| \| EGFR mutations, n (%) \| \| \| 0.152 \| \| No \| 36 (37.5%) \| 18 (51.4%) \| \| \| Yes \| 60 (62.5%) \| 17 (48.6%) \| \| \| EGFR mutation sites, n (%) \| \| \| 0.142 \| \| \| 36 (37.5%) \| 18 (51.4%) \| \| \| Exon 19 \| 34 (35.4%) \| 13 (37.1%) \| \| \| Exon 20 \| 1 (1%) \| 1 (2.9%) \| \| \| Exon 21 \| 25 (26%) \| 3 (8.6%) \| \| \| PD-L1 expression, n (%) \| \| \| 0.081 \| \| <1% \| 15 (15.6%) \| 7 (20%) \| \| \| 1–50% \| 12 (12.5%) \| 10 (28.6%) \| \| \| >50% \| 10 (10.4%) \| 1 (2.9%) \| \| \| Unknown \| 59 (61.5%) \| 17 (48.6%) \| \| \| Chemotherapy, n (%) \| \| \| 0.237 \| \| No \| 52 (54.2%) \| 23 (65.7%) \| \| \| Yes \| 44 (45.8%) \| 12 (34.3%) \| \| \| EGFR-TKI therapy, n (%) \| \| \| 0.152 \| \| No \| 36 (37.5%) \| 18 (51.4%) \| \| \| Yes \| 60 (62.5%) \| 17 (48.6%) \| \| \| EGFR-TKI drugs, n (%) \| \| \| 0.176 \| \| \| 36 (37.5%) \| 18 (51.4%) \| \| \| 1st generation \| 26 (27.1%) \| 5 (14.3%) \| \| \| 2nd generation \| 5 (5.2%) \| 0 (0%) \| \| \| 3rd generation \| 29 (30.2%) \| 12 (34.3%) \| \| \| ICI therapy, n (%) \| \| \| 0.152 \| \| No \| 60 (62.5%) \| 17 (48.6%) \| \| \| Yes \| 36 (37.5%) \| 18 (51.4%) \| \| \| ICI drugs, n (%) \| \| \| 0.053 \| \| \| 60 (62.5%) \| 17 (48.6%) \| \| \| PD-1PD-L1 \| 35 (36.5%)1 (1%) \| 15 (42.9%)3 (8.6%) \| \| \| KPS score, n (%) \| \| \| 0.002 \| \| 70 \| 4 (4.2%) \| 6 (17.1%) \| \| \| 80 \| 49 (51%) \| 24 (68.6%) \| \| \| 90 \| 42 (43.8%) \| 4 (11.4%) \| \| \| 100 \| 1 (1%) \| 1 (2.9%) \| \| \| Age (years), mean ± SD \| 59.08 ± 9.93 \| 59.09 ± 10.20 \| 0.999 \| \| BMI, mean ± SD \| 22.91 ± 3.03 \| 22.12 ± 2.97 \| 0.188 \| \| Hemoglobin, mean ± SD \| 126.61 ± 18.04 \| 122.71 ± 16.14 \| 0.263 \| \| hCRP, median (IQR) \| 3.49 (0.77, 11.55) \| 6.77 (1.69, 22.07) \| 0.165 \| \| Total protein, mean ± SD \| 65.45 ± 6.31 \| 64.06 ± 4.72 \| 0.239 \| \| Albumin, mean ± SD \| 38.64 ± 4.85 \| 36.54 ± 3.95 \| 0.023 \| The relationship between nutritional status and L3 SMI is shown in Figure 1. The scatter plot shows a high correlation between nutritional status factors, including BMI ($p \leq 0.001$, Figure 1A), hemoglobin level ($$p \leq 0.014$$, Figure 1B), albumin level ($$p \leq 0.029$$, Figure 1D), prealbumin level ($$p \leq 0.018$$, Figure 1E), and cholesterol level ($$p \leq 0.016$$, Figure 1F), and L3 SMI, indicating that poor nutritional status is a risk factor for sarcopenia in NSCLC patients. FIGURE 1: Association between BMI (A), hemoglobin (B), total protein (C), albumin (D), prealbumin (E), cholesterol (F), and L3 SMI. BMI, body mass index; SMI, skeletal muscle index. ## 3.2. Effect of sarcopenia on OS and PFS The Kaplan–Meier curves for OS and PFS grouped by sarcopenia and non-sarcopenia are shown in Figure 2. Analysis of the entire patient cohort showed a significant difference in OS between patients with sarcopenia and those without sarcopenia, with a median OS of 13 and 26 months, respectively ($p \leq 0.001$; Figure 2A). Kaplan–Meier analysis also revealed that patients with sarcopenia had a significantly shorter PFS than those without sarcopenia (6.4 months vs. 15.1 months, $p \leq 0.001$; Figure 2D). FIGURE 2: Overall survival (OS) and progression-free survival (PFS) curves. (A) OS for the two groups in the whole cohort; (B) OS for the two groups treated with EGFR-TKIs; (C) OS for the two groups treated with ICIs; (D) PFS for the sarcopenia and non-sarcopenia groups in the whole cohort; (E) PFS for the two groups treated with EGFR-TKIs; (F) PFS for the two groups treated with ICIs. In the EGFR-TKI-treated cohort ($$n = 77$$), the median OS and PFS for patients with sarcopenia were significantly shorter than those for patients without sarcopenia (OS: 12.7 vs. 28.0 months; PFS: 8.6 vs. 14.1 months, respectively; both $p \leq 0.001$; Figures 2B, E). The median OS and PFS for patients with sarcopenia in the ICI-treated cohort were significantly shorter than those for patients without sarcopenia (OS: 13.4 vs. 25.8 months, $$p \leq 0.011$$; PFS: 5.1 vs. 16.4 months, $p \leq 0.001$; Figures 2C, F). In the entire patient cohort, univariate Cox regression analysis revealed that BMI, sarcopenia, KPS score, levels of hemoglobin, hypersensitive C-reactive protein (hCRP) and albumin were significant prognostic factors for OS (all $p \leq 0.05$; Table 2). Multivariate analysis of all the above potential factors identified only sarcopenia [hazard ratio (HR): 2.187, $95\%$ confidence interval (CI): 1.230–3.891, $$p \leq 0.008$$; Table 2] and albumin [hazard ratio (HR): 0.921, $95\%$ confidence interval (CI): 0.860–0.987, $$p \leq 0.019$$; Table 2] as strong independent predictors of OS. Univariate and multivariate Cox regression analyses of both the EGFR-TKI-treated and ICI-treated cohorts confirmed that sarcopenia was an independent negative factor for OS (EGFR-TKI-treated group, HR: 2.806, $95\%$ CI: 1.304–6.037, $$p \leq 0.008$$; Supplementary Table 3; ICI-treated group, HR: 2.155, $95\%$ CI: 1.107–4.484, $$p \leq 0.028$$; Supplementary Table 4). TABLE 2 \| Characteristics \| Total (n) \| Univariate analysis \| Univariate analysis.1 \| Multivariate analysis \| Multivariate analysis.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Hazard ratio (95% CI) \| p-value \| Hazard ratio (95% CI) \| p-value \| \| Sex \| 131.0 \| \| \| \| \| \| Female \| 54.0 \| Reference \| \| \| \| \| Male \| 77.0 \| 1.210 (0.729–2.010) \| 0.460 \| \| \| \| Age \| 131.0 \| 1.005 (0.980–1.031) \| 0.689 \| \| \| \| Smoking \| 131.0 \| \| \| \| \| \| No \| 72.0 \| Reference \| \| \| \| \| Yes \| 59.0 \| 1.119 (0.684–1.830) \| 0.655 \| \| \| \| Drinking \| 131.0 \| \| \| \| \| \| No \| 99.0 \| Reference \| \| \| \| \| Yes \| 32.0 \| 1.205 (0.691–2.101) \| 0.510 \| \| \| \| Histopathology \| 131.0 \| \| \| \| \| \| AC \| 107.0 \| Reference \| \| \| \| \| SCC \| 24.0 \| 1.258 (0.668–2.370) \| 0.477 \| \| \| \| Ch‘emotherapy \| 131.0 \| \| \| \| \| \| No \| 75.0 \| Reference \| \| \| \| \| Yes \| 56.0 \| 1.070 (0.652–1.754) \| 0.789 \| \| \| \| EGFR-TKIs therapy \| 131.0 \| \| \| \| \| \| Yes \| 77.0 \| Reference \| \| \| \| \| No \| 54.0 \| 1.389 (0.848–2.274) \| 0.192 \| \| \| \| Body mass index \| 131.0 \| 0.918 (0.844–1.000) \| 0.050 \| 0.948 (0.866–1.038) \| 0.252 \| \| Sarcopenia status \| 131.0 \| \| \| \| \| \| Non-sarcopenia \| 63.0 \| Reference \| \| Reference \| \| \| Sarcopenia \| 68.0 \| 2.940 (1.744–4.956) \| <0.001 \| 2.187 (1.230–3.891) \| 0.008 \| \| KPS score \| 131.0 \| \| \| \| \| \| 70 \| 10.0 \| Reference \| \| Reference \| \| \| 80 \| 73.0 \| 0.423 (0.188–0.951) \| 0.037 \| 0.437 (0.178–1.074) \| 0.071 \| \| 90 \| 46.0 \| 0.180 (0.074–0.439) \| <0.001 \| 0.358 (0.128–1.001) \| 0.050 \| \| 100 \| 2.0 \| 0.000 (0.000–Inf) \| 0.996 \| 0.000 (0.000–Inf) \| 0.996 \| \| Hemoglobin \| 131.0 \| 0.982 (0.969–0.995) \| 0.005 \| 1.000 (0.983–1.018) \| 0.967 \| \| hCRP \| 131.0 \| 1.010 (1.001–1.019) \| 0.035 \| 1.004 (0.992–1.016) \| 0.538 \| \| Total protein \| 131.0 \| 0.973 (0.936–1.012) \| 0.168 \| \| \| \| Albumin \| 131.0 \| 0.904 (0.864–0.946) \| <0.001 \| 0.921 (0.860–0.987) \| 0.019 \| Univariate Cox regression analysis revealed that BMI, sarcopenia, KPS score, hCRP level, and albumin level were significant prognostic factors for PFS (all $p \leq 0.05$; Table 3). Multivariate analysis confirmed the independent prognostic relevance of BMI (HR, 0.883; $95\%$ CI: 0.814–0.958, $$p \leq 0.003$$) and sarcopenia (HR, 2.830; $95\%$ CI: 1.662–4.817, $p \leq 0.001$) for PFS (Table 3). Univariate and multivariate Cox regression analyses of both the EGFR-TKI and ICI cohorts confirmed that sarcopenia was an independent negative factor for PFS (EGFR-TKI cohort, HR: 2.946, $95\%$ CI: 1.430–6.068, $$p \leq 0.003$$, Supplementary Table 5; ICI cohort, HR: 3.567, $95\%$ CI: 1.647–7.724, $$p \leq 0.001$$, Supplementary Table 6). TABLE 3 \| Characteristics \| Total (n) \| Univariate analysis \| Univariate analysis.1 \| Multivariate analysis \| Multivariate analysis.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Hazard ratio (95% CI) \| p-value \| Hazard ratio (95% CI) \| p-value \| \| Sex \| 131.0 \| \| \| \| \| \| Female \| 54.0 \| Reference \| \| \| \| \| Male \| 77.0 \| 1.051 (0.683–1.616) \| 0.822 \| \| \| \| Age \| 131.0 \| 0.985 (0.964–1.007) \| 0.174 \| \| \| \| Smoking \| 131.0 \| \| \| \| \| \| No \| 72.0 \| Reference \| \| \| \| \| Yes \| 59.0 \| 0.971 (0.631–1.493) \| 0.893 \| \| \| \| Drinking \| 131.0 \| \| \| \| \| \| No \| 99.0 \| Reference \| \| \| \| \| Yes \| 32.0 \| 0.865 (0.514–1.455) \| 0.584 \| \| \| \| Histopathology \| 131.0 \| \| \| \| \| \| AC \| 107.0 \| Reference \| \| \| \| \| SCC \| 24.0 \| 1.187 (0.689–2.047) \| 0.537 \| \| \| \| Chemotherapy \| 131.0 \| \| \| \| \| \| No \| 75.0 \| Reference \| \| \| \| \| Yes \| 56.0 \| 1.042 (0.681–1.596) \| 0.849 \| \| \| \| EGFR-TKIs therapy \| 131.0 \| \| \| \| \| \| No \| 54.0 \| Reference \| \| \| \| \| Yes \| 77.0 \| 1.372 (0.893–2.110) \| 0.149 \| \| \| \| Body mass index \| 131.0 \| 0.908 (0.846–0.974) \| 0.007 \| 0.883 (0.814–0.958) \| 0.003 \| \| Sarcopenia status \| 131.0 \| \| \| \| \| \| Non-sarcopenia \| 96.0 \| Reference \| \| Reference \| \| \| Sarcopenia \| 35.0 \| 3.590 (2.236–5.762) \| <0.001 \| 2.830 (1.662–4.817) \| < 0.001 \| \| KPS score \| 131.0 \| \| <0.001 \| \| \| \| 70 \| 10.0 \| Reference \| \| Reference \| \| \| 80 \| 73.0 \| 0.977 (0.419–2.278) \| 0.958 \| 1.638 (0.651–4.125) \| 0.295 \| \| 90 \| 46.0 \| 0.281 (0.110–0.714) \| 0.008 \| 0.696 (0.238–2.035) \| 0.508 \| \| 100 \| 2.0 \| 0.000 (0.000–Inf) \| 0.995 \| 0.000 (0.000–Inf) \| 0.996 \| \| Hemoglobin \| 131.0 \| 0.989 (0.978–1.000) \| 0.055 \| 1.002 (0.987–1.018) \| 0.773 \| \| hCRP \| 131.0 \| 1.010 (1.001–1.018) \| 0.022 \| 1.006 (0.994–1.018) \| 0.347 \| \| Total protein \| 131.0 \| 0.971 (0.940–1.004) \| 0.085 \| 0.991 (0.950–1.035) \| 0.689 \| \| Albumin \| 131.0 \| 0.930 (0.893–0.968) \| <0.001 \| 0.977 (0.911–1.047) \| 0.508 \| Stratified analyses were performed to clarify the relationship between sarcopenia and the HRs of OS and PFS in various patient subgroups (Figures 3, 4). Overall, sarcopenia was consistently associated with poor OS and PFS across most subgroups of patients. FIGURE 3: The association between sarcopenia and hazard ratios of OS in various subgroups. BMI, body mass index; KPS, Karnofsky performance status; hCRP, hypersensitive C-reactive protein. FIGURE 4: The association between sarcopenia and hazard ratios of PFS in various subgroups. BMI, body mass index; KPS, Karnofsky performance status; hCRP, hypersensitive C-reactive protein. ## 3.3. OR, DC, and treatment-related AEs Of the 131 patients, 81 had an OR and 125 had DC. The mean SMI was significantly lower in the non-OR group than in the OR group, 38.84 ± 7.11 vs. 43.89 ± 7.55 cm2/m2, respectively ($p \leq 0.001$; Figure 5A). Similarly, a significant difference was also found in SMI between the DC group and non-DC group (42.46 ± 7.64 vs. 33.74 ± 4.31 cm2/m2, $$p \leq 0.002$$; Figure 5D). All analyses were repeated in the subgroups of patients treated with EGFR-TKIs or ICIs, and the findings were similar to those of the primary analysis (Figures 5B, C, E, F). The patients in the OR and DC groups had a significantly higher SMI than those in the non-OR and non-DC groups, regardless of whether the patients received EGFR-TKI (Figures 5B, E) or ICI (Figures 5C, F) treatment. FIGURE 5: The mean SMI of the OR and DC groups in all patients (A,D), patients treated with EGFR-TKIs (B,E), and patients treated with ICIs (C,F). SMI, skeletal muscle index; OR, objective response; DC, disease control. In our study, 51 patients ($38.9\%$) experienced treatment-related AEs: 12 ($\frac{12}{35}$, $34.3\%$) in the sarcopenia group and 39 ($\frac{39}{96}$, $40.6\%$) in the non-sarcopenia group. There was no statistically significant difference between the two groups ($$p \leq 0.550$$). The most frequent AEs were hypothyroidism and skin rashes. ## 4. Discussion The present study presents considerable real-world data on sarcopenia as a prognostic marker in patients with advanced NSCLC receiving first-line EGFR-TKIs or ICIs. We confirmed that patients with sarcopenia had significantly shorter OS and PFS than those without sarcopenia in the entire patient, EGFR-TKI-treated, and ICI-treated cohorts. In addition, statistically significant differences were observed in mean SMI between the OR and non-OR groups and the DC and non-DC groups. Therefore, nutritional intervention and physical activity programs are recommended to patients with sarcopenia receiving immunotherapy or EGFR-TKI therapy to improve the therapy outcome. Although there are many studies on the relationship between sarcopenia and immunotherapy in lung cancer, their conclusions are inconsistent. Most of these studies found that a low SMI or a sarcopenia diagnosis is associated with shortened survival in advanced NSCLC patients treated with PD1/PD-L1 checkpoint inhibitors (14, 16, 20–25). However, other studies showed no differences in OS and PFS between patients with and without sarcopenia (26–29). In these studies, ICIs were used in different treatment lines, which may have partially affected the results. The majority of patients included in these studies were treated with second-line or later immunotherapy, whereas only four studies enrolled patients who were receiving first-line immunotherapy, and the sample size in these was relatively small [16, 22, 23, 29]. Currently, immunotherapy is increasingly used as the first-line treatment for advanced lung cancer; therefore, our study included advanced lung cancer patients receiving ICIs only as the first-line therapy. Our study included the largest sample size of patients receiving first-line immunotherapy reported to date and provided strong evidence of the negative impact of sarcopenia on the prognosis of lung cancer when using first-line ICIs. Similarly, using univariate and multivariate analyses of EGFR-TKI subgroups, we found that patients without sarcopenia had significantly longer OS and PFS than those with sarcopenia. In the few studies evaluating the prognostic impact and predictive value of sarcopenia in NSCLC patients harboring EGFR mutations and treated with EGFR-TKIs, as with ICI therapies, the results are inconsistent; however, most studies agree that sarcopenia does not affect PFS and OS (30–32). In a retrospective study conducted by Sabrina et al., sarcopenia did not affect the response to gefitinib in patients with EGFR-mutated NSCLC, even though it was an indicator of poor prognosis in terms of OS [17]. In contrast, Atakan et al. found that sarcopenia was an independent factor of poor prognosis for OS and PFS in NSCLC patients receiving EGFR-TKI-targeted therapy [18]. These inconsistent results in different studies might come from different study design, different inclusion and exclusion criteria, different sample size and different way of measuring muscle area or definition of sarcopenia, etc. Next, stratified analyses were performed to clarify the relationship between sarcopenia and the HRs of OS and PFS in various patient subgroups. Overall, sarcopenia was consistently associated with both poor OS and PFS across most subgroups of patients except for patients with BMI > 25kg/m2. The reason may be that, for cancer patients, body weight and body fat are also important indicators to reflect the nutritional status of patients and significantly affect the treatment outcome of patients [33]. Therefore, for obese (BMI > 25kg/m2) cancer patients, a comprehensive body composition analysis may be a better prognostic indicator more than a single myopenia. The underlying mechanisms by which sarcopenia affects the efficacy of ICIs and EGFR-TKIs are not yet fully understood. Previous studies have found that interleukin-15 is the most abundant cytokine expressed in the skeletal muscle that can regulate CD8+ T cells and promote T cell survival [34, 35], which is important for maintaining immune function. Serum interleukin-15 levels decrease in older adults with the loss of muscle mass, suggesting that muscle loss may lead to impaired immune function, which may have some relevance to sarcopenia. Additionally, CD4+FoxP3+ Tregs infiltrate damaged skeletal muscles, suggesting that sarcopenia may play an important role in tumor immune escape [36]. Another possible mechanism for the poor prognosis of NSCLC patients with sarcopenia could be different drug clearance rates in cancer patients with or without sarcopenia, as there is a strong association between pembrolizumab clearance and OS. Patients with high ICI clearance rates had worse survival rates than those with low clearance rates. Some researchers believe the primary method of ICI elimination may be related to the development of cancer cachexia and sarcopenia. Procatabolic status can affect survival by leading to faster protein turnover through monoclonal antibody clearance [37]. The mechanism by which sarcopenia affects the efficacy of EGFR-TKIs is still unclear, but some of the reasons may be similar to those for immunotherapy drugs, such as drug clearance. Retrospective studies have shown that patients with the same body weight and BMI may have different skeletal muscle masses and adipose tissue levels, which could affect EGFR-TKI therapy outcomes [13]. When administered, EGFR-TKIs, including gefitinib, are widely distributed in various tissues of the human body, and when bound to human serum albumin and α1-acid glycoprotein, they can have half-lives of up to 48 h. Researchers have demonstrated in animal models that gefitinib is present in lower concentrations in the skin and fat and in higher concentrations in highly perfused organs [38]. In addition, studies have shown that gefitinib lasts for up to 96 h in muscle and for only 2 h in fat after oral consumption [39]. Therefore, as the diffusion and disposition of drugs in fat are different from those in muscle, this could be one of the mechanisms by which sarcopenia affects the prognosis and toxicity of EGFR-TKIs. Whether sarcopenia is associated with treatment-related toxicity in lung cancer remains unclear. In this study, we found that the treatment-related toxicities in patients with sarcopenic and non-sarcopenic lung cancer were similar [31, 34]. Nie et al. reported that treatment-related toxicity occurred more frequently in patients with sarcopenic lung cancer using afatinib [30]. In contrast, Alessio et al. did not find a significant relationship between baseline SMI and AEs [14]. The toxicities of EGFR-TKIs or ICIs are closely related to the duration of medication. As the survival times of non-sarcopenia patients were longer than those of sarcopenia patients, this may have affected the incidence of adverse reactions, resulting in the lack of a statistically significant difference between the two groups. Our study has several strengths. It is the first to include both EGFR-TKIs and ICIs, and the targeted immunotherapy included in our study was a first-line treatment, which conforms to the current standard treatment regimen. In addition, compared with similar studies, ours has the largest number of cases, and there are few studies focusing on both OS and PFS in patients, as in our study. Our study also has several limitations. First, this was a retrospective, single-center study. Second, sarcopenia was defined only according to SMI and was not based on muscle strength and function, such as grip strength. ## 5. Conclusion In conclusion, sarcopenia before first-line EGFR-TKI or ICI therapy might be a significant predictor of poor clinical outcomes, leading to shortened OS and PFS and reduced OR and DC. Sarcopenia should be considered before using EGFR-TKIs or ICIs in clinical practice. ## Data availability statement The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of Sichuan Cancer Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions JL and TL were responsible for conceptualizing and designing this study, data collection, data interpretation, and manuscript drafting. NY, LX, and XN played a major role in body composition assessment and data analysis. JX, YL, MZ, HZ, CT, SP, LL, HB, CL, and HK participated in acquisition of clinical records, data analysis, and revision of the manuscript. All authors read and approved the final version of manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Complex clinical manifestations and new insights in RNA sequencing of children with diabetes and WFS1 variants authors: - Yu Ding - Zhe Li - Qianwen Zhang - Niu Li - Guoying Chang - Yirou Wang - Xin Li - Juan Li - Qun Li - Ru-en Yao - Xin Li - Xiumin Wang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10031778 doi: 10.3389/fendo.2023.1066320 license: CC BY 4.0 --- # Complex clinical manifestations and new insights in RNA sequencing of children with diabetes and WFS1 variants ## Abstract ### Background WFS1-related disorders involve a wide range of clinical phenotypes, including diabetes mellitus and neurodegeneration. Inheritance patterns of pathogenic variants of this gene can be autosomal recessive or dominant, and differences in penetrance present challenges for accurate diagnosis and genetic counselling. ### Methods Three probands and one elder brother from three families were systematically evaluated and the clinical data of other family members were collected from the medical history. Whole-exome sequencing was performed on the probands, and RNA sequencing was performed on four patients, their parents with WFS1 variants, and four gender- and age-matched children with type 1 diabetes mellitus. ### Results There were six patients with diabetes. Dilated cardiomyopathy, a rare manifestation of WFS1-related disease, was identified in one patient, along with MRI findings of brain atrophy at age 7 years and 3 months, the earliest age of discovery we know of. Whole-exome sequencing revealed five pathogenic or likely pathogenic variants in the WFS1 gene, including c.1348dupC (p.His450Profs93), c.1381A>C (p.Thr461pro), c.1329C>G (p.Ser443Arg), c.2081delA (p.Glu694Glyfs16), c.1350-1356delinsGCA (p.His450Glnfs26), of which 3 variants (c.1348dupC, c.2081delA, c.1350-1356delinsGCA) were novel that have not been previously reported. The differentially expressed genes were mainly associated with immune-related pathways according to the Gene Ontology enrichment analysis of the RNA sequencing data. The exon 1 region of HLA-DRB1 in two patients was not transcribed, while the transcription of the region in their parents was normal. ### Conclusion This study emphasizes the clinical and genetic heterogeneity in patients, even in the same family with WFS1 variants. MRI evaluation of the brain should be considered when WFS1-related disorder is first diagnosed. ## Introduction The WFS1 gene is located on 4p16.1, spans more than 33.4 kb of genomic DNA and consists of 8 exons. Wolframin 1, a protein encoded by the WFS1 gene, consists of 890 amino acids. It is a complete endoglycosidase-H sensitive membrane glycoprotein, located in endoplasmic reticulum (ER) and widely expressed in all tissues of the body [1, 2]. Wolframin 1 lacks homology with other known proteins and their exact function is not yet elucidated. However, the defects of Wolframin 1 are considered to cause ER stress, damage the cell cycle process, and affect calcium homeostasis [3]. According to the Human Gene Mutation Database Professional, most of pathogenic variants of WFS1 gene are distributed in the coding region, and no variant hotspot has been found. The pathogenic variation of WFS1 is inherited in an autosomal recessive or dominant manner. Autosomal recessive inheritance (double allele) often leads to severe Wolfram syndrome 1 (MIM 222300). This syndrome may also be caused by other specific situations, such as uniparental disomy of chromosome 4 [4]. One of the typical clinical features of Wolfram syndrome 1 is non-autoimmune insulin-dependent diabetes, with other manifestations such as diabetes insipidus, optic atrophy, and deafness. Autosomal dominant inheritance (heterozygous variation) can lead to Wolfram syndrome like disease (MIM 614296) and most have mild diabetes, independent of insulin treatment [5, 6]. Heterozygous variation of WFS1 can also lead to isolated non-insulin-dependent diabetes (MIM 125853), cataracts (MIM 116400), and deafness associated with pathogenic mutation (automatic dominant $\frac{6}{14}$/38) (MIM 600965). The pathogenic variation of WFS1 is closely related to the occurrence and development of diabetes. We report the clinical and genetic characteristics of six patients with diabetes with WFS1 variation from three families and further explored their complex clinical phenotypes to deepen the understanding of the disease caused by gene variation. The clinical phenotype of WFS1 variation was widely heterogeneous, even within the same family in our clinical cohort. The cause of this wide genetic heterogeneity is unknown and whether it is related to the abnormality of the noncoding region and transcriptome level is worth studying. Of the Mendelian disease-related genes, $70.6\%$ are expressed in the peripheral blood, which greatly improves the feasibility of peripheral blood transcriptome sequencing to study genetic related diseases [7]. In 2020, the diagnosis of de Lange syndrome (neurodysplasia related syndrome) was confirmed by sequencing the transcriptome of peripheral blood B lymphocytes [8]. In our study, the differential expression data of peripheral blood transcriptome in patients and their parents were analysed for the first time to preliminarily explore the causes for the difference in diabetes phenotype caused by variation of WFS1, to improve the accuracy of diagnosis and recognition of this disease. ## Patients From January 2018 to November 2021, three children (probands) from three different families, who were hospitalized in our hospital due to diabetes and suspected of monogenic associated syndrome were enrolled in this study. Three probands and the elder brother of one proband were systematically evaluated, including the endocrine and metabolic, urinary, nervous, and cardiovascular systems, and ophthalmology and hearing. The clinical data of other family members were collected. The* genetic detection and analysis were performed on probands and thirteen other members in family 1 which was initially described in the previous clinical cohort [9], one proband and four relatives in family 2, and the third proband and parents in family 3. Four gender- and age-matched children with type 1 diabetes mellitus (T1DM) from different families were recruited as a control group and RNA sequencing was performed on their blood samples. This study was approved by the ethics committee of Shanghai Children’s Medical Centre. Written informed consent was obtained from each family. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. ## DNA sequencing and sequence analysis Whole-exome sequencing was performed on three patients (the probands) as previously described [10]. The patient in family 2 was simultaneously sequenced for mitochondrial DNA. Briefly, DNA was extracted from patients’ peripheral blood and was then sheared to create fragments of 150–200 bp. Sequencing library was prepared using the SureSelect XT Human All Exon V6 kit (Agilent Technologies, Santa Clara, CA, USA), and sequencing was performed on the Illumina (San Diego, CA, USA) NovaSeq 6000 System. After base calling, quality assessment, and alignment of the sequence reads to the reference human genome (Human 37.3; SNP135), all single nucleotide variants and indels were saved as a VCF format file, which was then uploaded to the Ingenuity® Variant Analysis™ (Ingenuity Systems, Redwood City, CA, USA) for filtering and annotating. The WFS1 variants identified by whole-exome sequencing were validated by Sanger sequencing using the ABI 3700 sequencer (Applied Biosystems, Foster City, CA, USA) in indicated patients, their parents, and other relatives. The pathogenicity of variation was classified according to the guidelines [11] of the American Academy of Medical Genetics and Genomics, and the variation was divided into benign, likely benign, uncertain significance, likely pathogenic, and pathogenic, which were further improved by the clingen sequence variant interpretation working group (https://www.clinicalgenome.org/Working-groups/Sequence-Variant-expression/). ## RNA sequencing and data processing RNA sequencing was performed in four patients, their parents with WFS1 variants, and four gender- and age-matched children with T1DM. Whole-blood samples were collected and shipped in Paxgene RNA tubes for processing. At least 1.0 μg of RNA was used for further processing. Isolated total RNA was analysed on an Agilent Bioanalyzer 2100 for RNA integrity number quality check. Globin mRNA was removed before cDNA library construction. All RNA-seq library construction and sequencing steps were performed by Novogene (https://en.novogene.com/). Paired-end 150 bp sequencing was performed on Illumina NovaSeq 6000 instruments. Reads were aligned to the reference human genome (hg38) with STAR v.2.7.1a [12]. We used an hg38 genome reference and gencode v.26 for annotation (https://www.gencodegenes.org/human/release_26.html). Principal component analysis (PCA) on gene expression was performed on the basis of TPM (transcript per million) values calculated with the software RNA-Seq by Expectation-Maximization v1.3.1 [13]. Differential expression analysis was performed using the DESeq2 R package 1.26.0 [14] and were determined at an adjusted p-value threshold of 0.05. Gene Ontology enrichment analysis of those genes and gene set enrichment analysis of ER stress related genes were performed using the ‘clusterProfiler’ R package 3.12.0 [15]. We used Portcullis v1.2.4 [16] for the quantification of junction reads. Sashimi plot of RNA splicing was drawn with ggsashimi v0.5.1 [17]. ## Clinical characteristics: description of the patients A total of six patients with diabetes were found in three families (Table 1). Patient 1 and 2 were siblings. Patient 1 was diagnosed at the age of 14. The initial diagnosis was insulin-dependent diabetes, congenital meningocele, neurogenic bladder, amblyopia, and optic atrophy. During the follow-up, hearing impairment occurred and signs of encephalatrophy were found by MRI (Figures 1A, E). Patient 2 was diagnosed at the age of 4 years and 9 months, initially diagnosed with T1DM; MRI results showed that the neurohypophysis was abnormally small (Figures 1B, F). During the follow-up, she was found to have optic atrophy at 7 years and 2 months of age. The 53-year-old aunt (father’s sister) was diagnosed with high blood glucose levels, which remained elevated for 1 year with a random blood glucose level of more than 10 mmol/L. The doctor at the local hospital diagnosed her with type 2 diabetes and did not provide special treatment. Patient 4 (aunt’s son) had similar clinical manifestations as patient 1. Patient 5, aged 7 years and 3 months, was initially diagnosed with T1DM, neurogenic bladder, optic atrophy, dilated cardiomyopathy, severe anaemia, cardiac insufficiency, and brain atrophy (Figures 1C, G). Patient 6 was diagnosed with cataracts due to blurred vision. Routine examination before operation found that blood glucose was increased and no abnormality was found in brain MRI (Figures 1D, H). The patients’ adrenal cortex, thyroid, liver, and kidney functions have been normal since follow-up. Patients 5 and 6 were the only children in the family without a family history of diabetes, eye disease, and hearing impairment. The parents of all the patients were non-consanguineous. ## Identification and in silico analysis of the WFS1 variants in patients A total of six WFS1 variants were found in three families. The c.1367G>A (p.Arg456His) detected in family 2 had a relatively high carrying rate in the population and belonged to the variant without clear meaning. The remaining five variants were determined as pathogenic or likely pathogenic variants according to the American Academy of Medical Genetics and Genomics variation classification standard and their characteristics are listed in Table 2. The variant of WFS1 (c.1348dupC (het), p.His450Profs93) was detected in family 1. Two patients (Patient 1 and Patient 2), their father, their father’s sister (Patient 3), and their aunt’s son (Patient 4) carried the same heterozygous variation. In addition, patient 2, their mother, and their sister carried c.1381A>C (het) (p.Thr461Pro). No WFS1 variation was found in other family members. Patient 5 in family 2, his mother, and grandmother carried c.2081delA(p. Glu694Glyfs16) heterozygous variants. Patient 5 and his grandfather carried the c.1329C>G (p.Ser443Arg) heterozygous variant. The results of mitochondrial DNA sequencing of patient 5 were normal. Patient 6 carries a homozygous variant of c.1350-1356delinsGCA (p.Gis450Glnfs26), which came from their parents respectively. The distribution of variation in families is shown in Figure 2. Three variants (c.1348dupC, c.2081delA, c.1350-1356delinsGCA) were not included in known public databases (i.e., gnomAD, Human Gene Mutation Database, and ClinVar) and were not reported in previous cases, suggesting they were novel. Among the five sites, two missense variants and three nonsense variants, which could form truncated proteins, were found. The amino acids encoded by c.1350-1356delinsGCA (p.His450Glnfs26), c.1348dupC (p.His450profs93), and c.1329C>G (p.Ser443Arg) were located in the fourth transmembrane region of WFS1 protein. The amino acids encoded by c. 1381A>C(p. Thr461Pro) were located in the non-transmembrane region of cytoplasm. The amino acid encoded by c.2081delA (p.Glu694Glyfs16) was located in the COOH terminal domain of the ER (Figure 3), which may have affected the protein function. The online software SWISS-MODEL predicted that the protein structure of the amino acid residues encoded by c.1381A>C(p. Thr461Pro) and c.1329C>G(p. Ser443Arg) variants can form an α-spiral structure, but the local subspace structure was abnormal (Figure 3). Figure 3: Localization of variation site (P/LP) in protein region and prediction of protein structure in amino acid residue region. The amino acids encoded by c.1348, c.1329, and c.1350–1356 were located in the fourth transmembrane region of WFS1 protein. The amino acids encoded by c.1381 were located in the non-transmembrane region of the cytoplasm. The amino acid encoded by c. 2081 was located in the COOH terminal domain of the endoplasmic reticulum. The c.1381A>C and c.1329C>G variations did not affect the formation of the α-spiral structure. ## Differential gene expression between the patient group and that of parents carrying WFS1 variants Differential gene expression analysis revealed that there were 80 candidate differentially expressed genes in the patient group (P), which contained 47 down-regulated genes and 33 up-regulated genes (Figures 4A, B). According to the Gene Ontology enrichment analysis, the differentially expressed genes were mainly enriched to biological processes related to immune function like antigen binding (Figure 4C). There was no significant difference in the expression of WFS1 between the two groups (adjusted $p \leq 0.05$). Gene set enrichment analysis showed that differentially expressed genes were not significantly enriched in ER stress related genes (Figure 4D). Figure 4: Analysis results of differentially expressed genes and splicing abnormalities by RNA sequencing. (A) Volcano plot showing gene expression differences between patients and their parents. Differentially expressed genes with an adjusted p <0.05 and a fold-change FC ≥ \|2\| are depicted in red (up-regulated) and blue (down-regulated). (B) Heatmap of all differentially expressed genes. Gene expression levels were quantified by transcript per million and scaled by gene. Red indicates higher expression level while blue indicates lower. (C) Gene Ontology (GO) enrichment scatter plot of differential expressed genes. The significance of GO term enrichment is represented by adjusted p-value and mapped to the scatter plot by point colour, while point size indicates the number of candidate genes annotated with a GO term. (D) Gene Set Enrichment Analysis (GSEA) showed that ER stress related genes exhibited little differential expression. (E) Sashimi plot of HLA-DRB1 gene of family 1 patient and their parents. Exon 1 of HLA-DRB1 were not transcribed in patients (Patient 1 and Patient 2), compared with their parents as normal transcripts. The last sashimi plot shows the average level of junction reads from 755 GTEx whole blood samples. (F) The expression level of HLA-DRB1 in different samples. The expression level of HLA-DRB1 was lower in other patients with WFS1 variants and T1DM, compared to the parent group without diabetes. ## Splicing data analysis of RNA transcripts We found that HLA-DRB1 in the patient group from family 1 had abnormal splicing based on the splicing data analysis of RNA transcripts. There were six exons in HLA-DRB1 and the exon 1 region of two patients (Patient 1 and Patient 2) was not transcribed, while the transcription of the region of their parents was normal. Compared with the average reads of 755 whole blood samples from healthy normal humans in GTEx (genetic tissue expression) datasets [18], the junction reads in the front segment of the transcripts in the patient group were relatively low (Figure 4E). After quantification of junction read counts with Portcullis, we normalized the junction read counts between exon 1 and exon 2 with the total number of junction read counts at HLA-DRB1. The normalized ratio was then transformed to the Z-score. We found that the Z-score of the patient group from family 1 was -1.24, which is relatively low among all GTEx whole blood samples. Differences in HLA-DRB1 expression were analysed in two other families and in the T1DM group. The expression level of the patient group was lower than that of the parent group, and higher than that of the T1DM group (Figure 4F). However, the expression of this gene fluctuated greatly in the peripheral blood according to 755 GTEx whole blood samples [18], with a median value of TPM 89.18, a minimum value of 1.47, and a maximum value of 1147. HLA-DRB1 was previously reported by GWAS to be associated with asthma [19], rheumatoid arthritis [20] and systemic lupus erythematosus [21], implying its potential role in immune system modulation and it could thus be a candidate modifier of the disease. ## Discussion Phenotypic penetrance was present in family 1, but the possibility that other alleles that had not been identified might be present could not be ruled out. WFS1 variants can cause different clinical phenotypes through different inheritance patterns and different degrees of clinical symptoms can also appear in the same inheritance pattern [22, 23]. The clinical phenotype was complex and lacked the correlation of gene variation on protein function. In the same family, WFS1 variants can also lead to different clinical phenotypes, and the presence of autosomal dominant and recessive WFS1-related disorders has been described in the same family [24]. The lack of genotype-phenotype correlations for WFS1 variants was also further supported by observations in the probands and their family members in our study. Reports of WFS1-related cardiomyopathy are rare. An OMIM description of the phenotype of Wolfram syndrome 1 (MIM: 222300) proposed a possible clinical phenotype of cardiomyopathy, but no relevant reports were found by searching the literature with the keywords including cardiomyopathy and WFS1. Previously reported cardiac abnormalities were mainly structural defects [25, 26]. However, in our research group, it was found that Patient 5 developed dilated cardiomyopathy and cardiac insufficiency one year after the diagnosis of diabetes. After 2 months of symptomatic treatment such as with cardiac diuresis and vasodilators, cardiac function improved, and the ventricle became smaller. Based on the clinical presentation of patient 5, differentiation from mitochondrial disease was required. Mitochondrial diseases are a group of heterogeneous multisystem diseases caused by mutations in nuclear or mitochondrial DNA, which can lead to abnormal myocardial structure and function, accompanied by heart failure which worsen dramatically during metabolic crisis [27]. No abnormal mutation was found in the patient’s mitochondrial DNA by sequencing, which could preliminarily rule out the disease. Current research suggests that the WFS1 gene is associated with mitochondrial dysfunction. After silencing the WFS1 gene in human embryonic kidney cells, mitochondrial dysfunction and up-regulation of related signalling pathway genes lead to cell destruction and degeneration [28]. Therefore, the cardiomyopathy of patient 5 may be related to mitochondrial dysfunction caused by WFS1. Some irritants such as infection and poor blood sugar control may aggravate the cardiomyopathy and cause clinical manifestations of acute cardiac insufficiency. However, the relationships between WFS1, mitochondrial function, and cardiomyopathy need to be further studied. In clinical work, attention should be paid to strengthening the follow-up of cardiac diseases in these patients and regular electrocardiogram and echocardiography examinations will help in early detection and prevent the rapid progression of the disease. WFS1-associated disorders can present as progressive neurodegenerative conditions such as cerebellar ataxia, brainstem dysfunction, peripheral neuropathy, and epilepsy. Cerebral and cerebellar atrophy can even precede the first clinical neurologic signs [29]. Limited data is currently available regarding encephalatrophy. The imaging changes of encephalatrophy were found by MRI in patients aged 16–56 [30, 31]. The MRI findings of patient 5 showed encephalatrophy at the age of 7 years and 3 months, which is the earliest age of discovery to the best of our knowledge. The WFS1 gene encodes a transmembrane protein located in the ER. Current studies have found that missense changes in the terminal domain of COOH can cause sensorineural hearing impairment [32, 33] and Wolfram-like lesions [5, 6]. In this study, there was a patient with a variation in this region, which was consistent with previous reports. The four pathogenic variation (P) and likely pathogenic variation sites in this study were all located in exon 8, which was similar to the studies of other populations. Exon 8 is thus a hot spot mutation region [34, 35]. The expression data of WFS1 transcriptome obtained by RNA sequencing showed that there was no significant difference in the expression of WFS1 in peripheral blood between the patient and the parent group; we thus speculated that the variation of these sites might affect the spatial structure of WFS1 protein. The variant p.Trp314Arg of WFS1 was found in patients with type 2 diabetes. Patient fibroblasts were analysed using western blotting and immunostaining technology. The results showed that this variation did not change the level of WFS1 protein and subcellular localization, but the variation of WFS1 (p.Trp314Arg) in HEK293T cell line showed a decrease in the ability to inhibit the ER stress response [23], which further confirmed that variations in WFS1 may affect protein function without affecting its expression. The natural WFS1 protein is a tetramer structure composed of homologous WFS1 monomers. The tetramer composed of mutant and wild-type monomers may be structurally unsound or have incomplete functionality, leading to the occurrence of disease through a dominant negative mechanism [2], which may be related to the pathogenesis of some patients in the same family. According to the RNA sequencing results in this study, compared with the parent group, the differentially expressed genes were enriched in the biological processes of antigen binding and immunoglobulin receptor binding. In the temporal lobe of mice with Wfs1 deleted, gene expression analysis detected the upregulation of growth hormone transcripts and revealed the activation of growth hormone pathways [36]. In pancreatic islets of Wfs1-deficient mice, RNA sequencing showed that Wfs1 deficiency significantly influenced the pathways related to tissue morphology, endocrine system development and function, and molecular transport network [37]. The gene expression profile of the hypothalamus in Wfs1 mutant mice indicated a reduction in G protein signalling, which was significantly similar to the profiles of other biological functions [38]. Differences in RNA sequencing results may be related to differences in species and sequenced tissues. Peripheral blood is mainly composed of lymphocytes and granulocytes, which may be one of the reasons for more differential expression enrichment in the immune response pathway. However, the possibility of immune system dysfunction caused by WFS1 gene mutation cannot be ruled out. Currently, most studies of WFS1 focus on ER stress. Excessive ER stress can affect insulin signalling, insulin biosynthesis, and β cell function, resulting in a reduction of insulin synthesis and sensitivity [39]. However, differences in down-regulation or up-regulation of ER stress marker gene transcriptome levels were not statistically significant between the patient and parent group; this is consistent with the RNA sequencing results of pancreatic cells in a WFS1 mutant mouse model [37]. This may be explained by the fact that gene transcription and expression may not always correspond to the protein level; alternatively, WFS1 may have had less effect on peripheral blood ER stress markers due to its specific expression in tissue and cells. HLA encodes the major histocompatibility complex. Three subregions on DP, DQ, and DR determine the type II major histocompatibility complex molecules of human leukocytes; they play an important role in the mutual recognition of cells that produce immune responses. Genetic polymorphism of HLA-DRB1 is associated with the occurrence of T1DM. For example, the DRB103:01 allele can increase the susceptibility to T1DM in children [40]. The first exon of HLA-DRB1 cannot be transcribed due to abnormal splicing, which may also affect the occurrence and development of diabetes. Previous studies focused more on the risk analysis of HLA gene polymorphisms and T1DM, and no report of transcriptome expression data of these loci had been reported. Our study found that the expression of the HLA-DRB1 transcriptome in the patient group was higher than that in the parent group without diabetes. In addition, the expression of HLA-DRB1 seemed to be lower in patients with T1DM. However, the expression range of HLA-DRB1 in peripheral blood of normal people fluctuated greatly. Whether the abnormal expression or splicing of HLA-DRB1 was related to diabetes was not clear but deserves further attention and experimental investigation. In conclusion, the clinical phenotypes of diseases caused by WFS1 variants are complex and accompanied by penetrance insufficiency, which increases the difficulty of diagnosis and genetic counselling of these diseases. For children with insulin-dependent diabetes and optic atrophy, WFS1 pathogenic variants need to be further excluded; exome sequencing is helpful for the diagnosis of this disease. In these patients, although the incidence of cardiomyopathy is rare, it may still occur, and timely evaluation of cardiomyopathy should be improved during follow-up. Brain atrophy can occur in early childhood and attention should be paid to the evaluation of brain MRIs when the disease is first diagnosed. Peripheral blood RNA sequencing results of patients and carriers suggest that WFS1 may affect immune-related pathways and the transcriptional expression of HLA-DRB1 may be related to the pathogenesis of diabetes. However, due to the small sample size, the implications for the occurrence of the disease and the pathogenic mechanism are limited and further studies are needed in the future. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NGDC database (https://ngdc.cncb.ac.cn/), accession number PRJCA012415. ## Ethics statement The studies involving human participants were reviewed and approved by the ethics committee of Shanghai Children’s Medical Centre. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article. ## Author contributions XW and XL (11th author) (co-corresponding authors) designed the study experiments. YD, GC, YW, XL (7th author), JL, and QL were responsible for recruiting patients and collecting clinical features. QZ, ZL, NL, and R-EY were responsible for the sequencing work. YD and ZL drafted the manuscript, tables, and figures. All authors contributed to the article and approved the submitted version. ## Conflict of interest The reviewer YY declared a shared parent affiliation with the authors YD, QZ, NL, GC, YW, XL, JL, QL, RY, XW to the handling editor at the time of review. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'Predictors of Influenza and Pneumococcal Vaccination Among Participants in the Women’s Health Initiative' authors: - Jonathan Fix - Macarius M. Donneyong - Stephen R. Rapp - Maryam Sattari - Beverly M. Snively - Jean Wactawski-Wende - Emily W. Gower journal: Public Health Reports year: 2022 pmcid: PMC10031837 doi: 10.1177/00333549221081817 license: CC BY 4.0 --- # Predictors of Influenza and Pneumococcal Vaccination Among Participants in the Women’s Health Initiative ## Body The Centers for Disease Control and Prevention (CDC) estimated that during the 2017-2018 influenza season, nearly 400 000 hospitalizations and 30 000 influenza-related deaths occurred. 1 Although older adults accounted for only $16\%$ of the population, 2 they contributed $54.5\%$ of influenza-related hospitalizations and $77\%$ of influenza-related deaths. 1 Similarly, older adults are most susceptible to pneumococcal disease complications, accounting for $60\%$ of pneumococcal disease–related hospitalizations and $72\%$ of deaths yearly. 3 In the United States, vaccination rates for these diseases are suboptimal among older adults. In the 2019-2020 season, seasonal influenza vaccine coverage among people aged ≥65 years was $69.3\%$. Vaccination rates varied across demographic groups, with non-Hispanic Black and Hispanic people having had the lowest rates. 4 Similarly, only $69.0\%$ of women aged ≥65 years reported having received a pneumococcal vaccine in 2018, and proportions vaccinated varied by demographic characteristics. 5 Identifying characteristics associated with likelihood of vaccination coverage is important for improving adherence with vaccination recommendations. Prior studies have explored sociodemographic factors and comorbidities associated with vaccine coverage among older adults.5-10 However, few studies have examined the relationship between lifestyle variables and vaccination. This study used data from the Women’s Health Initiative (WHI) to examine a broad range of potential predictors of vaccination among a cohort of women aged ≥65 years with a high rate of vaccination. This information should guide the development of targeted policies and programs to increase access to and awareness of essential vaccines for older women. ## Abstract ### Objective: Older adults typically experience higher rates of severe disease and mortality than the general population after contracting an infectious disease. Vaccination is critical for preventing disease and severe downstream outcomes; however, vaccination rates among older adults are suboptimal. We assessed predictors associated with pneumococcal and seasonal influenza vaccination among older women. ### Methods: We used data from the Women’s Health Initiative, a nationwide cohort of women. We ascertained seasonal influenza and pneumococcal vaccination status through a questionnaire administered in 2013. We limited analyses to women aged ≥65 years at questionnaire administration. We used logistic regression to estimate associations between demographic, lifestyle, and health-related factors and vaccination and explored stratification by race. ### Results: Of participants who responded to each question, $84.3\%$ ($$n = 60$$ 578) reported being vaccinated for influenza and $85.5\%$ ($$n = 59$$ 015) for pneumonia. The odds of reporting influenza vaccination were significantly lower among non-Hispanic Black participants than among non-Hispanic White participants (odds ratio [OR] = 0.53; $95\%$ CI, 0.49-0.58), women with no health insurance versus private health insurance (OR = 0.61; $95\%$ CI, 0.54-0.68), and women living in rural versus urban settings (OR = 0.84; $95\%$ CI, 0.73-0.96). Current smoking, lower education levels, and having comorbid conditions were associated with lower likelihood of being vaccinated for influenza (than not); past pneumonia diagnosis and being currently married were associated with a higher likelihood. We observed similar associations for pneumococcal vaccination coverage. ### Conclusions: These findings reinforce the need to enact policy and implement programs to improve access to, education and awareness about, and provider recommendations for these critical disease-prevention tools. Results from our study should guide strategies for SARS-CoV-2 vaccination. ## Study Population The WHI is an ongoing longitudinal study, initiated in 1993, that enrolled about 161 000 postmenopausal women in the United States. The study initially included participants in either a clinical trial or an observational study. Although the interventional components have ended, the WHI continues to follow participants regularly. The WHI contacts participants via mail or telephone annually to complete questionnaires and provide updated health-related and sociodemographic information. 11 During some annual contacts, the study administers supplemental questionnaires to capture additional information. We restricted the current analyses to participants who completed a 2013 supplemental questionnaire, the only questionnaire through which participants were asked to provide vaccination information. The institutional review board at the University of North Carolina at Chapel Hill determined this study to be exempt from continuing review. ## Ascertainment of Vaccination Status The WHI’s 2013 supplemental questionnaire included 2 vaccination questions: [1] “During the past 12 months, have you had a seasonal flu shot?” and [2] “A pneumonia shot or pneumococcal vaccine is usually given only once or twice in a person’s life and is different from the flu shot. Have you ever had a pneumonia shot?” For both questions, participants could respond yes, no, or don’t know/not sure. Our primary analyses excluded those who responded with “don’t know/not sure.” In a secondary analysis, we considered this response as a discrete group. We restricted the population to women aged ≥65 years, per CDC’s Advisory Committee on Immunization Practices recommendation for universal pneumococcal pneumonia vaccination 12 and to maintain consistency between the influenza and pneumonia vaccination analytic sets. ## Predictor Identification and Classification We evaluated sociodemographic and health-related characteristics (Online-Only Supplementary Table 1). Briefly, we used self-reported baseline data for race and ethnicity, education, urbanicity, annual household income, marital status, and health insurance. Through a 2011 questionnaire, participants reported their current smoking status, alcohol usage, exercise history, and self-rated health. The 2013 supplemental questionnaire included questions about pneumonia history and use of the internet for health information. Information on key medical conditions was collected throughout the study by using updates to annual medical history. We used participants’ 2010 zip code and the US Department of Agriculture’s Rural–Urban Commuting Area codes 13 and accompanying classifications to categorize the urbanicity of participants’ residence. To assess chronic conditions, we grouped participant reports into the following: asthma and emphysema; dementia, Alzheimer disease, and Parkinson disease; diabetes, hypertension, and high cholesterol; and myocardial infarction, stroke, transient ischemic attack, deep venous thrombosis, and pulmonary embolism. We created these groups to combine diseases with similar etiology, morbidity, and health care utilization. ## Statistical Methods A total of 74 852 WHI participants completed the vaccination history questionnaire. We excluded from further analyses 10 participants who were aged <65 years when they provided their vaccination history, resulting in a final analytic sample of 74 842. We calculated means and SDs for continuous variables and counts and percentages for categorical variables. We modeled influenza and pneumococcal vaccination separately, and we modeled the probabilities of reporting not being vaccinated by using logistic regression. In multivariable regression, we included all predictors that were significant in the univariate analysis at an α of ≤.10. We performed 2 sets of secondary analyses. First, we further investigated participants who responded “don’t know/not sure” to the vaccination questions by evaluating the association between each potential predictor and vaccination status, with 3 possible outcomes—yes, no, and don’t know/missing—to determine whether the don’t-know group was similar to another group. Separately, we assessed predictors of vaccination coverage limited to those who self-classified as either non-Hispanic White or non-Hispanic Black to better understand race- and ethnic-specific risk and protective factors associated with vaccination. We conducted all statistical analyses in SAS version 9.4 (SAS Institute, Inc). ## Results Of the 74 842 participants in our analysis, the mean age was 78.2 years (range, 65.4-98.0 y); $88.7\%$ were non-Hispanic White, $46.8\%$ had a college degree or higher, and $97.6\%$ lived in an urban area or large rural city/town (Table 1). Table 1. \| Characteristic \| All questionnaire respondents (N = 74 842) \| Influenza-vaccinated respondents b (n = 60 578) \| Pneumonia-vaccinated respondents c (n = 59 015) \| \| --- \| --- \| --- \| --- \| \| Mean age, y \| 78.2 \| 78.3 \| 78.2 \| \| Race and ethnicity \| Race and ethnicity \| Race and ethnicity \| Race and ethnicity \| \| Non-Hispanic White \| 88.7 \| 89.8 \| 90.0 \| \| Non-Hispanic Black \| 5.5 \| 4.8 \| 4.7 \| \| Hispanic \| 2.4 \| 2.2 \| 2.2 \| \| American Indian/Alaska Native \| 0.3 \| 0.3 \| 0.3 \| \| Asian/Pacific Islander d \| 2.1 \| 2.1 \| 2.0 \| \| Other e \| 0.9 \| 0.8 \| 0.9 \| \| Education \| Education \| Education \| Education \| \| Some high school or less \| 2.5 \| 2.2 \| 2.2 \| \| High school diploma/GED \| 14.9 \| 14.7 \| 14.5 \| \| School after high school \| 35.8 \| 35.1 \| 35.6 \| \| College degree or higher \| 46.8 \| 48.0 \| 47.7 \| \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| \| <10 000 \| 2.0 \| 1.8 \| 1.8 \| \| 10 000-34 999 \| 28.7 \| 27.7 \| 28.0 \| \| 35 000-74 999 \| 44.6 \| 44.8 \| 44.9 \| \| ≥75 000 \| 24.8 \| 25.8 \| 25.4 \| \| Urbanicity f \| Urbanicity f \| Urbanicity f \| Urbanicity f \| \| Urban or large rural city/town \| 97.6 \| 97.7 \| 97.7 \| \| Small rural town \| 2.4 \| 2.3 \| 2.3 \| \| Marital status \| Marital status \| Marital status \| Marital status \| \| Never married \| 4.1 \| 4.0 \| 4.0 \| \| Divorced or separated \| 14.8 \| 13.9 \| 14.3 \| \| Widowed \| 12.2 \| 12.0 \| 11.9 \| \| Currently married \| 67.0 \| 68.3 \| 68.0 \| \| Marriage-like relationship \| 1.9 \| 1.8 \| 1.8 \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| Current \| 2.1 \| 1.9 \| 2.0 \| \| Past \| 45.9 \| 46.5 \| 46.4 \| \| Never \| 52.0 \| 41.6 \| 51.7 \| \| Alcohol use, no. of times per week g \| Alcohol use, no. of times per week g \| Alcohol use, no. of times per week g \| Alcohol use, no. of times per week g \| \| Never \| 30.5 \| 29.6 \| 30.0 \| \| <1-4 \| 52.7 \| 53.1 \| 53.1 \| \| ≥5 \| 16.8 \| 17.3 \| 16.9 \| \| Exercise, no. of days per week h \| Exercise, no. of days per week h \| Exercise, no. of days per week h \| Exercise, no. of days per week h \| \| 0 or 1 \| 59.3 \| 58.7 \| 58.9 \| \| 2 or 3 \| 27.6 \| 28.2 \| 28.0 \| \| ≥4 \| 13.1 \| 13.1 \| 13.1 \| \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| \| Excellent \| 13.1 \| 12.7 \| 12.7 \| \| Very good \| 44.5 \| 44.5 \| 44.6 \| \| Good \| 34.7 \| 35.0 \| 35.0 \| \| Fair \| 7.3 \| 7.3 \| 7.3 \| \| Poor \| 0.5 \| 0.5 \| 0.5 \| \| Stayed at nursing home in past year \| 2.2 \| 2.2 \| 2.2 \| \| Use of internet for health information \| 58.9 \| 59.7 \| 60.7 \| \| Past pneumonia diagnosis \| 25.3 \| 26.0 \| 27.5 \| \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| \| \| 3.5 \| 3.0 \| 3.1 \| \| Medicaid \| 0.6 \| 0.5 \| 0.5 \| \| Private, Medicare, military, or other \| 95.9 \| 96.5 \| 96.4 \| \| Chronic diseases i \| Chronic diseases i \| Chronic diseases i \| Chronic diseases i \| \| History of cancer (any) \| 17.3 \| 17.8 \| 17.8 \| \| Alzheimer disease, Parkinson disease, or dementia \| 5.6 \| 5.4 \| 4.9 \| \| Asthma or emphysema \| 13.1 \| 13.3 \| 13.8 \| \| Diabetes, hypertension, or high cholesterol \| 77.0 \| 78.0 \| 77.9 \| \| Myocardial infarction, stroke, transient ischemic attack, deep vein thrombosis, or pulmonary embolism \| 2.8 \| 15.2 \| 15.3 \| \| History of hip fracture \| 2.8 \| 2.7 \| 2.8 \| ## Influenza Vaccination Overall, 60 578 ($84.3\%$) of the 71 858 participants responding to the influenza vaccine question reported having received the vaccination in the 12 months before completing the questionnaire (Table 1). The univariable (Online-Only Supplementary Table 2) and multivariate (Table 2) analyses yielded similar results. In the multivariate analysis, vaccination increased with increasing age (odds ratio [OR] = 1.03 per year; $95\%$ CI, 1.03-1.04), and non-Hispanic Black and American Indian/Alaska Native participants were less likely than non-Hispanic White participants to have received the influenza vaccine (OR = 0.53 [$95\%$ CI, 0.49-0.58] and OR = 0.61 [$95\%$ CI, 0.42-0.87], respectively). Participants with less education than a college degree (vs those with a college degree or higher) and those living in small rural towns (vs an urban area or large rural city/town) were less likely to be vaccinated (OR range = 0.72-0.83 and OR = 0.84 [$95\%$ CI, 0.73-0.96], respectively), while being currently married (vs other categories of marital status) was associated with the highest odds of vaccination. Table 2. \| Characteristic \| Influenza vaccination b \| Influenza vaccination b.1 \| Pneumonia vaccination c \| Pneumonia vaccination c.1 \| \| --- \| --- \| --- \| --- \| --- \| \| Characteristic \| Proportion vaccinated, % \| Odds ratio (95% CI) \| Proportion vaccinated, % \| Odds ratio (95% CI) \| \| Age, y \| \| 1.03 (1.03-1.04) \| \| 1.04 (1.04-1.05) \| \| Race and ethnicity \| Race and ethnicity \| Race and ethnicity \| Race and ethnicity \| Race and ethnicity \| \| Non Hispanic White \| 85.3 \| 1 [Reference] \| 86.6 \| 1 [Reference] \| \| Non-Hispanic Black \| 72.8 \| 0.53 (0.49-0.58) \| 73.6 \| 0.50 (0.46-0.55) \| \| Hispanic \| 77.5 \| 0.88 (0.76-1.01) \| 76.8 \| 0.74 (0.64-0.85) \| \| American Indian/Alaska Native \| 76.4 \| 0.61 (0.42-0.87) \| 81.7 \| 0.80 (0.53-1.20) \| \| Asian/Pacific Islander d \| 85.3 \| 0.99 (0.84-1.16) \| 84.6 \| 0.95 (0.80-1.12) \| \| Other e \| 76.7 \| 0.57 (0.46-0.71) \| 79.6 \| 0.62 (0.50-0.78) \| \| Education \| Education \| Education \| Education \| Education \| \| Some high school or less \| 77.3 \| 0.72 (0.62-0.84) \| 77.9 \| 0.65 (0.56-0.76) \| \| High school diploma/GED \| 83.3 \| 0.83 (0.77-0.89) \| 83.7 \| 0.78 (0.72-0.84) \| \| School after high school \| 82.8 \| 0.78 (0.74-0.83) \| 84.9 \| 0.85 (0.80-0.90) \| \| College degree or higher \| 86.2 \| 1 [Reference] \| 86.9 \| 1 [Reference] \| \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| \| <10 000 \| 76.6 \| 1 [Reference] \| 79.2 \| 1 [Reference] \| \| 10 000-34 999 \| 82.0 \| 0.92 (0.78-1.09) \| 84.5 \| 0.95 (0.80-1.14) \| \| 35 000-74 999 \| 84.7 \| 1.03 (0.87-1.22) \| 85.8 \| 1.00 (0.83-1.20) \| \| ≥75 000 \| 87.3 \| 1.22 (0.74-1.46) \| 87.1 \| 1.10 (0.91-1.33) \| \| Urbanicity f \| Urbanicity f \| Urbanicity f \| Urbanicity f \| Urbanicity f \| \| Urban or large rural city/town \| 84.4 \| 1 [Reference] \| 85.6 \| 1 [Reference] \| \| Small rural town \| 80.7 \| 0.84 (0.73-0.96) \| 82.0 \| 0.81 (0.70-0.93) \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Never married \| 82.3 \| 0.86 (0.77-0.97) \| 84.8 \| 0.97 (0.85-1.10) \| \| Divorced or separated \| 79.1 \| 0.75 (0.70-0.80) \| 82.5 \| 0.87 (0.81-0.93) \| \| Widowed \| 84.0 \| 0.84 (0.78-0.91) \| 84.9 \| 0.80 (0.74-0.87) \| \| Currently married \| 85.8 \| 1 [Reference] \| 86.4 \| 1 [Reference] \| \| Marriage-like relationship \| 79.9 \| 0.68 (0.58-0.79) \| 83.0 \| 0.82 (0.70-0.97) \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| Current \| 76.1 \| 0.73 (0.64-0.84) \| 79.5 \| 0.79 (0.68-0.92) \| \| Past \| 85.5 \| 1.11 (1.06-1.17) \| 86.3 \| 1.05 (1.00-1.10) \| \| Never \| 83.7 \| 1 [Reference] \| 85.2 \| 1 [Reference] \| \| Alcohol use, times per week g \| Alcohol use, times per week g \| Alcohol use, times per week g \| Alcohol use, times per week g \| Alcohol use, times per week g \| \| Never \| 82.3 \| 1 [Reference] \| 84.6 \| 1 [Reference] \| \| <1-4 \| 84.7 \| 1.12 (1.07-1.19) \| 85.8 \| 1.04 (0.98-1.10) \| \| ≥5 \| 86.9 \| 1.23 (1.14-1.33) \| 86.5 \| 1.00 (0.92-1.08) \| \| Exercise, days per week h \| Exercise, days per week h \| Exercise, days per week h \| Exercise, days per week h \| Exercise, days per week h \| \| 0 or 1 \| 83.6 \| 1 [Reference] \| 85.3 \| — \| \| 2 or 3 \| 85.7 \| 1.18 (1.11-1.24) \| 86.2 \| — \| \| ≥4 \| 85.0 \| 1.13 (1.05-1.22) \| 85.7 \| — \| \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| \| Excellent \| 81.6 \| 1 [Reference] \| 82.2 \| 1 [Reference] \| \| Very good \| 83.8 \| 1.15 (1.07-1.23) \| 85.0 \| 1.17 (1.09-1.25) \| \| Good \| 85.7 \| 1.37 (1.27-1.48) \| 87.0 \| 1.37 (1.27-1.48) \| \| Fair \| 86.4 \| 1.52 (1.35-1.71) \| 88.1 \| 1.55 (1.37-1.76) \| \| Poor \| 84.9 \| 1.29 (0.91-1.84) \| 87.0 \| 1.39 (0.94-2.05) \| \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| \| Yes \| 84.9 \| 1.18 (1.12-1.24) \| 86.8 \| 1.42 (1.35-1.50) \| \| No \| 83.4 \| 1 [Reference] \| 83.6 \| 1 [Reference] \| \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| \| Yes \| 86.9 \| 1.25 (1.18-1.32) \| 91.5 \| 1.99 (1.87-2.13) \| \| No \| 83.4 \| 1 [Reference] \| 83.3 \| 1 [Reference] \| \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| \| \| 70.5 \| 0.61 (0.54-0.68) \| 74.1 \| 0.62 (0.56-0.70) \| \| Medicaid \| 78.6 \| 0.79 (0.58-1.07) \| 80.8 \| 0.79 (0.57-1.10) \| \| Private, Medicare, military, or other \| 84.9 \| 1 [Reference] \| 86.0 \| 1 [Reference] \| \| Chronic diseases i \| Chronic diseases i \| Chronic diseases i \| Chronic diseases i \| Chronic diseases i \| \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| \| Yes \| 87.0 \| 1.23 (1.16-1.32) \| 87.9 \| 1.18 (1.10-1.26) \| \| No \| 83.7 \| 1 [Reference] \| 84.9 \| 1 [Reference] \| \| Alzheimer disease, Parkinson disease, or dementia \| Alzheimer disease, Parkinson disease, or dementia \| Alzheimer disease, Parkinson disease, or dementia \| Alzheimer disease, Parkinson disease, or dementia \| Alzheimer disease, Parkinson disease, or dementia \| \| Yes \| 84.8 \| — \| 81.2 \| 0.63 (0.56-0.70) \| \| No \| 84.3 \| — \| 85.7 \| 1 [Reference] \| \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| \| Yes \| 86.1 \| 1.13 (1.05-1.21) \| 89.7 \| 1.36 (1.26-1.48) \| \| No \| 84.0 \| 1 [Reference] \| 84.8 \| 1 [Reference] \| \| Diabetes, hypertension, or high cholesterol \| Diabetes, hypertension, or high cholesterol \| Diabetes, hypertension, or high cholesterol \| Diabetes, hypertension, or high cholesterol \| Diabetes, hypertension, or high cholesterol \| \| Yes \| 85.6 \| 1.50 (1.43-1.59) \| 86.6 \| 1.39 (1.32-1.47) \| \| No \| 80.0 \| 1 [Reference] \| 81.9 \| 1 [Reference] \| \| Myocardial infarction, stroke, transient ischemic attack, deep vein thrombosis, or pulmonary embolism \| \| \| \| \| \| Yes \| 85.7 \| 0.98 (0.92-1.06) \| 87.6 \| 1.04 (0.96-1.12) \| \| No \| 84.1 \| 1 [Reference] \| 85.1 \| 1 [Reference] \| \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| \| Yes \| 85.6 \| — \| 88.3 \| 1.04 (0.88-1.24) \| \| No \| 84.3 \| — \| 85.4 \| 1 [Reference] \| Current smokers were less likely than past or never smokers to be vaccinated. Participants who reported ≥1 alcoholic drink per week or exercising ≥2 days per week were more likely than never drinkers and those who exercised ≤1 day per week to be vaccinated. Participants who reported having no health insurance were less likely than those with any form of health insurance to have received the influenza vaccine (OR = 0.61; $95\%$ CI, 0.54-0.68). Having reported a prior pneumonia diagnosis (OR = 1.25; $95\%$ CI, 1.18-1.32), a history of cancer (OR = 1.23; $95\%$ CI, 1.16-1.32), asthma or emphysema (OR = 1.13; $95\%$ CI, 1.05-1.21), or diabetes, hypertension, or high cholesterol (OR = 1.50; $95\%$ CI, 1.43-1.59) were associated with higher odds of vaccination, compared with not having reported these conditions. ## Pneumococcal Vaccination Of the 69 041 participants who responded to the pneumococcal vaccination question, 59 015 ($85.5\%$) reported having received the vaccination (Table 1). In multivariable analysis, predictors associated with pneumococcal vaccination were similar to the observations for influenza vaccination (Table 2), with some notable exceptions. Hispanic participants were less likely than non-Hispanic White participants (OR = 0.74; $95\%$ CI, 0.64-0.85) to be vaccinated, while the test of association for American Indian/Alaska Native participants was not significant. Past pneumonia diagnosis was more strongly associated with higher likelihood of pneumococcal vaccination (OR = 1.99; $95\%$ CI, 1.87-2.13) than was observed for influenza vaccination. While not associated with influenza vaccination, reporting a history of Alzheimer disease, Parkinson disease, or dementia (vs not reporting a history of these conditions) was associated with a lower likelihood of being vaccinated for pneumonia (OR = 0.63; $95\%$ CI, 0.56-0.70). In addition, we did not find significant associations in the comparisons of participants who were never married (vs currently married), by levels of weekly alcohol use or by exercise history. ## Secondary Analyses Participants who responded “don’t know/not sure” to vaccination questions were small subsets of the total study population (2994 [$4.0\%$] for influenza and 5801 [$7.7\%$] for pneumonia). Their predictor distribution more closely resembled the distribution for participants who responded as vaccinated, compared with participants who responded as not vaccinated. Including them in the vaccinated group did not affect overall findings. Among participants who received an influenza vaccination, $91.6\%$ were also vaccinated for pneumonia, compared with $52.3\%$ among those who had not received an influenza vaccination. In multivariable analyses restricted to non-Hispanic Black participants, some associations with vaccination remained similar to those identified in the whole cohort, including age, education, past pneumonia diagnosis, health insurance, cancer history, and several chronic diseases; however, other predictors were more highly associated with vaccination (Table 3). Living in a small rural town (vs in an urban area or large rural city/town) was associated with having 0.22 times the odds of being vaccinated for influenza ($95\%$ CI, 0.07-0.65). The magnitude of this association was nearly one-quarter the size of that among non-Hispanic White participants (OR = 0.86; $95\%$ CI, 0.74-0.99). Reporting less than a high school diploma/General Educational Development (vs reporting a college degree or higher) was associated with being less than half as likely to be vaccinated against influenza (OR = 0.47; $95\%$ CI, 0.32-0.69), while we found a much smaller difference when we compared higher levels of education. In the full cohort, each increase in education level was associated with a decreased risk of not being vaccinated. Some subpopulations had particularly low levels of vaccination; only $43.5\%$ of non-Hispanic Black participants without health insurance and no comorbidities were vaccinated for influenza, compared with $67.8\%$ of non-Hispanic White participants who similarly did not have health insurance or comorbidities, per unadjusted analysis. Table 3. \| Characteristic \| Non-Hispanic Black (n = 3939) b \| Non-Hispanic Black (n = 3939) b.1 \| Non-Hispanic Black (n = 3939) b.2 \| Non-Hispanic White (n = 63 638) b \| Non-Hispanic White (n = 63 638) b.1 \| Non-Hispanic White (n = 63 638) b.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristic \| No. of respondents (% vaccinated) \| Univariate, OR (95% CI) \| Multivariate, OR (95% CI) \| No. of respondents (% vaccinated) \| Univariate, OR (95% CI) \| Multivariate, OR (95% CI) \| \| Age, y \| \| 1.03 (1.01-1.04) \| 1.03 (1.01-1.04) \| \| 1.03 (1.03-1.04) \| 1.03 (1.03-1.04) \| \| Education \| Education \| Education \| Education \| Education \| Education \| Education \| \| Some high school or less \| 203 (61.1) \| 0.53 (0.39-0.71) \| 0.47 (0.32-0.69) \| 1237 (80.8) \| 0.63 (0.54-0.72) \| 0.73 (0.61-0.88) \| \| High school diploma/GED \| 450 (72.0) \| 0.86 (0.68-1.09) \| 0.79 (0.61-1.03) \| 9529 (84.1) \| 0.79 (0.74-0.84) \| 0.81 (0.75-0.87) \| \| School after high school \| 1460 (72.7) \| 0.89 (0.76-1.05) \| 0.85 (0.71-1.02) \| 22 489 (83.8) \| 0.77 (0.74-0.81) \| 0.77 (0.73-0.82) \| \| College degree or higher \| 1785 (74.9) \| 1 [Reference] \| 1 [Reference] \| 29 996 (87.0) \| 1 [Reference] \| 1 [Reference] \| \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| Annual household income, $ \| \| <10 000 \| 225 (70.7) \| 1 [Reference] \| — \| 906 (78.2) \| 1 [Reference] \| 1 [Reference] \| \| 10 000-34 999 \| 1294 (71.0) \| 1.02 (0.75-1.39) \| — \| 16 893 (83.4) \| 1.40 (1.19-1.65) \| 1.01 (0.83-1.22) \| \| 35 000-74 999 \| 1630 (74.1) \| 1.18 (0.87-1.61) \| — \| 27 151 (85.6) \| 1.66 (1.41-1.95) \| 1.12 (0.92-1.37) \| \| ≥75 000 \| 603 (76.6) \| 1.36 (0.96-1.92) \| — \| 15 458 (87.7) \| 2.00 (1.70-2.36) \| 1.32 (1.08-1.63) \| \| Urbanicity c \| Urbanicity c \| Urbanicity c \| Urbanicity c \| Urbanicity c \| Urbanicity c \| Urbanicity c \| \| Urban or large rural city/town \| 3923 (72.9) \| 1 [Reference] \| 1 [Reference] \| 61 986 (85.4) \| 1 [Reference] \| 1 [Reference] \| \| Small rural town \| 16 (50.0) \| 0.37 (0.14-0.99) \| 0.22 (0.07-0.65) \| 1658 (81.3) \| 0.74 (0.65-0.84) \| 0.86 (0.74-0.99) \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Never married \| 242 (71.1) \| 0.80 (0.59-1.07) \| 0.84 (0.60-1.17) \| 2493 (84.0) \| 0.82 (0.74-0.92) \| 0.87 (0.76-0.99) \| \| Divorced or separated \| 1230 (69.0) \| 0.72 (0.61-0.85) \| 0.73 (0.61-0.87) \| 8690 (80.9) \| 0.66 (0.62-0.70) \| 0.74 (0.69-0.80) \| \| Widowed \| 619 (75.3) \| 0.99 (0.80-1.22) \| 0.87 (0.68-1.12) \| 7499 (85.0) \| 0.89 (0.83-0.95) \| 0.83 (0.76-0.90) \| \| Currently married \| 1764 (75.5) \| 1 [Reference] \| 1 [Reference] \| 43 537 (86.5) \| 1 [Reference] \| 1 [Reference] \| \| Marriage-like relationship \| 57 (63.2) \| 0.56 (0.32-0.96) \| 0.59 (0.32-1.09) \| 1232 (80.9) \| 0.67 (0.58-0.77) \| 0.66 (0.56-0.77) \| \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| Smoking status \| \| Current \| 152 (66.5) \| 0.76 (0.54-1.09) \| — \| 1199 (77.2) \| 0.62 (0.54-0.71) \| 0.70 (0.60-0.82) \| \| Past \| 1664 (74.0) \| 1.10 (0.95-1.28) \| — \| 27 991 (86.4) \| 1.16 (1.10-1.21) \| 1.12 (1.07-1.18) \| \| Never \| 1753 (72.2) \| 1 [Reference] \| — \| 30 895 (84.6) \| 1 [Reference] \| 1 [Reference] \| \| Alcohol use, times per week d \| Alcohol use, times per week d \| Alcohol use, times per week d \| Alcohol use, times per week d \| Alcohol use, times per week d \| Alcohol use, times per week d \| Alcohol use, times per week d \| \| Never \| 1753 (71.8) \| 1 [Reference] \| — \| 17 064 (83.6) \| 1 [Reference] \| 1 [Reference] \| \| <1-4 \| 1693 (73.7) \| 1.10 (0.95-1.28) \| — \| 32 385 (85.6) \| 1.16 (1.10-1.22) \| 1.14 (1.07-1.21) \| \| ≥5 \| 145 (73.8) \| 1.10 (0.75-1.62) \| — \| 10 959 (87.2) \| 1.34 (1.25-1.43) \| 1.24 (1.15-1.35) \| \| Exercise, days per week e \| Exercise, days per week e \| Exercise, days per week e \| Exercise, days per week e \| Exercise, days per week e \| Exercise, days per week e \| Exercise, days per week e \| \| 0 or 1 \| 2194 (71.3) \| 1 [Reference] \| 1 [Reference] \| 35 330 (84.6) \| 1 [Reference] \| 1 [Reference] \| \| 2 or 3 \| 989 (75.4) \| 1.23 (1.04-1.47) \| 1.33 (1.11-1.61) \| 16 585 (86.6) \| 1.18 (1.12-1.24) \| 1.17 (1.10-1.24) \| \| ≥4 \| 354 (73.7) \| 1.13 (0.87-1.45) \| 1.30 (0.99-1.72) \| 7808 (85.8) \| 1.34 (1.02-1.17) \| 1.13 (1.05-1.23) \| \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| Self-rated health \| \| Excellent \| 239 (69.0) \| 1 [Reference] \| — \| 8269 (82.3) \| 1 [Reference] \| 1 [Reference] \| \| Very good \| 1256 (72.1) \| 1.15 (0.86-1.56) \| — \| 27 416 (84.6) \| 1.18 (1.11-1.26) \| 1.16 (1.07-1.24) \| \| Good \| 1672 (73.2) \| 1.23 (0.91-1.65) \| — \| 20 167 (87.0) \| 1.45 (1.35-1.55) \| 1.40 (1.29-1.52) \| \| Fair \| 390 (75.4) \| 1.37 (0.96-1.97) \| — \| 4039 (87.9) \| 1.56 (1.40-1.75) \| 1.55 (1.36-1.77) \| \| Poor \| 24 (79.2) \| 1.70 (0.61-4.74) \| — \| 277 (85.9) \| 1.32 (0.93-1.86) \| 1.27 (0.86-1.87) \| \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| Use of internet for health information \| \| Yes \| 2056 (73.2) \| 1.04 (0.90-1.19) \| — \| 37 770 (85.8) \| 1.09 (1.05-1.14) \| 1.18 (1.11-1.24) \| \| No \| 1828 (72.4) \| 1 [Reference] \| — \| 25 180 (84.6) \| 1 [Reference] \| 1 [Reference] \| \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| Past pneumonia diagnosis \| \| Yes \| 744 (79.3) \| 1.55 (1.28-1.88) \| 1.59 (1.27-1.98) \| 16 247 (87.4) \| 1.27 (1.21-1.34) \| 1.24 (1.17-1.32) \| \| No \| 3126 (71.2) \| 1 [Reference] \| 1 [Reference] \| 46 148 (84.5) \| 1 [Reference] \| 1 [Reference] \| \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| Health insurance \| \| \| 273 (65.9) \| 0.68 (0.53-0.89) \| 0.88 (0.64-1.20) \| 1940 (71.4) \| 0.41 (0.37-0.46) \| 0.56 (0.50-0.64) \| \| Medicaid \| 74 (66.2) \| 0.69 (0.42-1.12) \| 0.71 (0.41-1.24) \| 278 (82.0) \| 0.76 (0.56-1.03) \| 0.81 (0.56-1.18) \| \| Private, Medicare, military, or other \| 3523 (73.9) \| 1 [Reference] \| 1 [Reference] \| 61 047 (85.8) \| 1 [Reference] \| 1 [Reference] \| \| Chronic diseases f \| Chronic diseases f \| Chronic diseases f \| Chronic diseases f \| Chronic diseases f \| Chronic diseases f \| Chronic diseases f \| \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| Cancer (any) \| \| Yes \| 504 (76.4) \| 1.24 (1.00-1.54) \| 1.17 (0.92-1.49) \| 10 817 (87.8) \| 1.29 (1.21-1.37) \| 1.25 (1.17-1.34) \| \| No \| 3275 (72.4) \| 1 [Reference] \| 1 [Reference] \| 50 208 (84.8) \| 1 [Reference] \| 1 [Reference] \| \| Alzheimer disease, Parkinson disease, or dementia \| \| \| \| \| \| \| \| Yes \| 212 (75.5) \| 1.16 (0.84-1.59) \| — \| 3413 (86.0) \| 1.06 (0.96-1.17) \| — \| \| No \| 3727 (72.7) \| 1 [Reference] \| — \| 60 225 (85.3) \| 1 [Reference] \| — \| \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| Asthma or emphysema \| \| Yes \| 666 (77.0) \| 1.30 (1.05-1.60) \| 1.23 (0.97-1.56) \| 8190 (87.0) \| 1.18 (1.10-1.26) \| 1.13 (1.04-1.22) \| \| No \| 3373 (72.1) \| 1 [Reference] \| 1 [Reference] \| 55 448 (85.1) \| 1 [Reference] \| 1 [Reference] \| \| Diabetes, hypertension, or high cholesterol \| \| \| \| \| \| \| \| Yes \| 3544 (74.2) \| 1.83 (1.48-2.27) \| 2.01 (1.58-2.56) \| 48 272 (86.8) \| 1.56 (1.48-1.63) \| 1.50 (1.41-1.58) \| \| No \| 395 (61.0) \| 1 [Reference] \| 1 [Reference] \| 15 366 (80.8) \| 1 [Reference] \| 1 [Reference] \| \| Myocardial infarction, stroke, transient ischemic attack, deep vein thrombosis, or pulmonary embolism \| \| \| \| \| \| \| \| Yes \| 589 (75.0) \| 1.14 (0.93-1.40) \| — \| 9687 (86.6) \| 1.13 (1.06-1.20) \| 0.98 (0.91-1.06) \| \| No \| 3350 (72.5) \| 1 [Reference] \| — \| 53 951 (85.1) \| 1 [Reference] \| 1 [Reference] \| \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| History of hip fracture \| \| Yes \| 25 (72.0) \| 0.96 (0.40-2.30) \| — \| 1827 (86.0) \| 1.06 (0.93-1.21) \| — \| \| No \| 3895 (72.8) \| 1 [Reference] \| — \| 61 611 (85.3) \| 1 [Reference] \| — \| ## Discussion This study identified key demographic (race and ethnicity, health insurance type, age, marital status, urbanicity, and education) and health-related (smoking, alcohol use, exercise history, self-rated health, prior pneumonia diagnosis, and chronic conditions) factors that were significantly associated with self-reported vaccination status among a population of women aged ≥65 years with a high rate of vaccination. These findings support the existing literature on predictors of vaccination and provide new insights into additional characteristics that may increase the risk of older women receiving influenza or pneumococcal vaccines. Our stratified analyses prompt interesting considerations regarding cohort design, namely, ensuring representation of key populations in nationwide cohorts. Race was among the strongest predictors of vaccination in our study. A previous systematic review also reported that non-Hispanic Black and Hispanic people were less likely than non-Hispanic White people to be vaccinated, 7 in part because of limited access to health care and lower levels of health insurance. 14 Improving vaccination rates among racial and ethnic minority populations is critical, because US Black populations, compared with non-Black populations, have higher levels of comorbidities associated with more severe pneumococcal disease, longer lengths of stay, and higher costs during pneumococcal hospitalizations. 15 We also found that living in small rural towns (vs urban areas or a large rural city/town) was associated with lower levels of vaccination, likely reflecting more limited health care access. This finding is consistent with a health insurance claim–based study of older adults indicating that older adults living in rural areas were $23\%$ less likely than those in suburban/metropolitan areas to receive pneumococcal vaccination. 16 Increasing access for key populations, such as racial and ethnic minority groups and individuals living in rural settings, could be achieved through developing partnerships with community groups, leaders, or nonprofit organizations. Efforts to improve influenza vaccination rates among hard-to-reach populations have benefited from working with churches in areas with high levels of participation and trust in faith-based centers. 17 Research also has shown mobile health clinics to be highly effective at increasing health services, largely because of their patient-centric approach. 18 Community members attending mobile health clinics cite their convenient locations, informal settings, and familiar environments as key aspects of increasing their engagement. Strategic partnerships with community organizations should be made to most effectively increase vaccination rates among older adults. ## Factors Involving Health Care Utilization or Health History This study highlights that factors that increase the interaction with the health care system are associated with greater likelihood of vaccination. Our finding that participants with chronic diseases were more likely than participants without chronic diseases to be vaccinated is consistent with the existing literature.8,19,20 This finding may simply reflect that people in worse health have more interaction with the health care system than do people with better health; research indicates that people who report seeking medical care 1 or more times per year are more likely than people with no medical care visits in the previous 12 months to receive recommended vaccines. 21 However, it may also show that individuals who are at higher risk of infection and progression to severe disease take proactive preventive measures more commonly than healthier individuals do. Indeed, we found that participants who reported a prior pneumonia diagnosis were substantially more likely than participants without a prior pneumonia diagnosis to report pneumococcal vaccination, potentially reflecting the importance of understanding disease severity and personal risk in vaccination decisions. A meta-analysis 22 of risk perception and health behavior found that individuals who perceived themselves at higher risk of influenza were more likely to be vaccinated than individuals who perceived a lower risk. When exploring reasons for pneumococcal vaccination refusal among older Black patients, researchers found that those who refused vaccination may not have believed they were susceptible to the disease. 23 This finding reinforces the importance of increased knowledge about vaccines and the diseases they protect against. It is important to identify new strategies to promote routine and proactive interaction with the health care system. Direct outreach by primary care providers and pharmacists, through mailings or automated telephone calls, has increased the rates of influenza vaccination 24 and human papillomavirus vaccine initiation and completion. 25 Previous studies suggest that health care provider recommendation is the most influential factor contributing to a patient’s decision to vaccinate.26-28 Thus, it is critical that we maximize health care providers’ ability to recommend vaccination. This maximization can be accomplished by using new technologies such as electronic reminder systems, 29 addressing financial barriers physicians face that limit their recommendations, and providing vaccines in primary care settings.30,31 The results of our race-and-ethnicity–stratified analysis reveal the importance of careful cohort design and participant recruitment. Stratification by race and ethnicity or other predictors can provide insights into factors affecting vaccination or other health outcomes. The presence of statistical significance for 1 subpopulation and absence for another may not necessarily indicate that the association is modified by variables used for stratification but instead that the study has greater statistical power to identify signals (whether clinically relevant or not) in one group over another. The impact of differential statistical power can be observed in the unadjusted associations between a history of myocardial infarction, stroke, transient ischemic attack, deep vein thrombosis, or pulmonary embolism and influenza vaccination. Despite the point estimate of the association among non-Hispanic Black participants being of greater magnitude (OR = 1.15) than that among non-Hispanic White participants (OR = 1.13), only the association among non-Hispanic White participants was significant. Future researchers should consider representation and sample size during cohort design and analysis. ## Potential Implications for SARS-CoV-2 Vaccination Strategies Our data provide insights that could be leveraged to improve uptake of the SARS-CoV-2 vaccine. The original strain of SARS-CoV-2 had a reproductive number of roughly 2.5, corresponding to a herd protection threshold of about $60\%$ vaccinated; however, the emergence and predominance of the Delta variant, which has a reproductive number of nearly 7, requires slightly more than $85\%$ vaccination to achieve herd protection. 32 More recently, the Omicron variant was estimated to be 4 times as infectious as the Delta variant. 33 Recent analyses of patterns of intention to receive the COVID-19 vaccine 34 and real-world data on vaccine administration in the United States 35 are similar to patterns observed for the predictors of influenza and pneumococcal pneumonia vaccination assessed in our analysis. Lifestyle predictors of SARS-CoV-2 vaccine uptake have yet to be explored and would provide valuable information for further developing vaccination promotion programs. Consensus is growing in the scientific community that SARS-CoV-2 is likely to become endemic in the United States. 36 If routine vaccination against SARS-CoV-2 is required in the future, public health officials should leverage lessons learned from influenza to increase the effectiveness and efficiency of programs to promote vaccination. ## Limitations and Strengths Although to our knowledge this research represents the largest long-standing, prospective cohort study to examine vaccination among women aged ≥65 years, our study had some limitations. First, the vaccination data were collected in 2013, and behaviors may have changed since then. Second, we classified predictors by using self-reported information from the nearest time to which vaccination status was assessed. Although we used the most updated information possible, not all data on predictors were collected concurrently with data on vaccination status. Thus, our risk-factor categorization may not have reflected some participants’ true status at the time of questionnaire completion. Reliance on self-reported vaccination status may present an additional limitation, because the accuracy of this information may be associated with race and ethnicity, education, and household income.37,38 Third, our study population was mostly non-Hispanic White, educated, and living in urban settings, which may limit the generalizability of our overall findings. While adult women (aged ≥18 years) in the United States are more likely than adult men to get vaccinated for influenza, the differences by sex become smaller or even disappear completely with age.4,7,39 We could not find sex-stratified analyses of predictors of vaccination in the literature; this information would be helpful to better understand the effect of sex on vaccination and the generalizability of our findings. Nonetheless, the predictors identified here should assist researchers when they develop analysis plans for investigating vaccine uptake among other populations. Fourth, a higher proportion of participants reported having received influenza ($84.3\%$) and pneumococcal ($85.5\%$) vaccinations in comparison with the general population of older adults in the United States ($70.4\%$ and $66.9\%$, respectively). While acknowledging high rates of vaccination, our study reveals key predictors of vaccination among a specific population, which enables more efficient targeting to further increase vaccination levels. Key strengths of our study included the use of a large, prospective cohort of women with detailed health information; leveraging demographic, health, and lifestyle information to complete a robust analysis of predictors of vaccination; and stratification by race and ethnicity to evaluate how predictors of vaccination differ by racial and ethnic groups. Assessing modification of predictors of vaccination by key demographic variables, such as race and ethnicity and urbanicity, in future research would enhance targeted efforts to increase vaccination among specific populations. Future research should also investigate the impact of health-related and lifestyle factors on vaccination among men and younger populations. ## Conclusions Even among a group of women with high vaccination rates, we identified subgroups at higher risk of missed seasonal influenza and/or pneumococcal vaccination. This information adds to the growing body of literature about characteristics associated with vaccine coverage and should be used to craft interventions and programs aimed at improving vaccination coverage among older women. This information is directly applicable to SARS-CoV-2 vaccine distribution, given that we saw similar characteristics among one-time and annual vaccines. Clinicians working with older subpopulations with a higher risk of being unvaccinated should be more vigilant in recommending vaccinations during clinic visits. ## References 1. Estimated flu-related illnesses, medical visits, hospitalizations, and deaths in the United States—2018–2019 flu season 2. 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--- title: 'Effect of different doses of Lacticaseibacillus paracasei K56 on body fat and metabolic parameters in adult individuals with obesity: a pilot study' authors: - Guzailinuer Kadeer - Wanrui Fu - Yaqi He - Ying Feng - Wei-Hsein Liu - Wei-Lian Hung - Haotian Feng - Wen Zhao journal: Nutrition & Metabolism year: 2023 pmcid: PMC10031870 doi: 10.1186/s12986-023-00739-y license: CC BY 4.0 --- # Effect of different doses of Lacticaseibacillus paracasei K56 on body fat and metabolic parameters in adult individuals with obesity: a pilot study ## Abstract ### Background Studies have shown that probiotics have an effect on reducing body fat on a strain-specific and dose–response bases. The purpose of this study was to evaluate the effect of a novel probiotic strain Lacticaseibacillus paracasei K56 on body fat and metabolic biomarkers in adult individuals with obesity. ### Methods 74 adult subjects with obesity (body mass index ≥ 30 kg/m2, or percent body fat > $25\%$ for men, percent body fat > $30\%$ for women) were randomized into 5 groups and supplemented with different doses of K56 (groups VL_K56, L_K56, H_K56, and VH_K56: K56 capsules, 2 × 107 CFU/day, 2 × 109 CFU/day, 2 × 1010 CFU/day, 2 × 1011 CFU/day, respectively) or placebo (group Pla: placebo capsule) for 60 days. Subjects were advised to maintain their original dietary intake and physical activity. Anthropometric measurements, body composition assessment, and metabolic parameters were measured at baseline and after 60 days of intervention. ### Results The results showed that the L_K56 group had significant decreases in percent body fat ($$p \leq 0.004$$), visceral fat area ($$p \leq 0.0007$$), total body fat mass ($$p \leq 0.018$$), trunk body fat mass ($$p \leq 0.003$$), waist circumference ($$p \leq 0.003$$), glycosylated hemoglobin($$p \leq 0.002$$) at the end of the study compared with baseline. There were non-significant reductions in Body weight and BMI in the L_K56, H_K56, VL_K56 groups, whereas increases were observed in the placebo and VH_K56 groups compared with baseline values. In addition, K56 supplementation modulated gut microbiota characteristics and diversity indices in the L-K56 group. However, mean changes in body fat mass, visceral fat area, weight, body mass index, waist circumference and hip circumference were not significantly different between groups. ### Conclusions The results suggest that supplementation with different doses of Lacticaseibacillus paracasei K56 has certain effect on reducing body fat and glycosylated hemoglobin, especially at a dose of 109 CFU/day. Trial registration: clinicaltrials.gov Identifier: NCT04980599. ## Introduction Obesity is a complex chronic disease defined as excessive or abnormal fat accumulation that adversely affects health [1]. In recent years, the increased prevalence of obesity has reached epidemic proportions, and presents a critical public health problem worldwide because of the substantial health risks associated with increased mortality from type 2 diabetes (T2D), hypertension, and cardiovascular diseases, as well as the incidence of some cancers [2]. However, safe and effective treatments for obesity are scarce and new strategies are needed to mitigate its substantial health effects. Obesity is mostly a multifactorial disease due to obesogenic environments, psychosocial factors and genetic variants. In 2004, scientists have first reported that, gut microbiota as an important environmental factor affects energy harvest from the diet and energy storage in the host [3]. Since then, a large number of studies have explored the relationship between obesity and gut microbiota, and revealed that the changes in the gut microbial composition and function contribute to the pathophysiology of obesity [4–6] and that their modulation may aid in the prevention and treatment of this disease [7, 8]. Probiotics are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” [9]. Studies have shown potential therapeutic effects of probiotics on obesity and related metabolic disorders by influencing and maintaining the homeostasis of gut microbiota composition and function through various mechanisms of actions such as antimicrobial activity, enhancement of barrier function, immunomodulation [9, 10]. Lactic acid bacteria, specifically lactobacillus and Bifidobacterium are the most documented probiotics that appear to have beneficial effects of reducing fat mass, regulating glucolipid metabolism. Animal studies have shown that, Lactobacillus gasseri SBT2055 inhibits enlargement of visceral adipocytes, reduces body weight gain, improves glucose tolerance in rodents through anti-inflammatory effects and stimulation of energy expenditure [11, 12]. In another study, supplementation with Lactobacillus plantarum reduced fat mass and serum lipid profile concurrently with downregulation of lipogenic gene expression in the adipocytes, and modulated gut microbiota composition, resulting in reductions in the bodyweight of high fat diet (HFD) fed obese mice [13]. Similar results have been observed in other experiments in which probiotic Bifidobacterium longum supplemented to HFD-fed obese animals [14, 15]. In humans, supplementation with single species [16, 17] or multiple species of probiotics [18, 19] to overweight/obese subjects at various doses reduced abdominal adiposity, waist and hip circumference or improved glucolipid metabolism for varying extents. A recent meta-analysis highlighted a positive trend of probiotics supplementation in improving anthropometric measures of overweight and obese patients with associated metabolic diseases [20]. Interestingly, a strain-specific effect on body weight and metabolism of the probiotics has also been reported; Some clinical trials also suggest that the extent of anti-obesogenic effects of probiotics may depend on both the probiotic dose and viable form used [21, 22]. In a recently published animal study, a novel probiotic strain Lacticaseibacillus paracasei K56, isolated from the intestine of a healthy child, was treated by gavage at various doses to HFD-fed mice for 12 weeks. The results have shown that L. paracasei K56 significantly reduced body and fat mass and improved lipid metabolism [23]. In another animal study, administration of Lacticaseibacillus paracasei K56 effectively attenuated obesity parameters, such as body weight, insulin-resistance, plasma glucose and lipids; The beneficial effects may be related to the restored host gut microbiota [24]. This indicates that, K56 might be a promising probiotic strain for prevention and treatment of obesity and related metabolic disorders. However, the beneficial effects of this novel probiotic strain have not been proved in humans, and the appropriate dosage for human administration needs to be evaluated. In this exploratory study, we aim to evaluate the metabolic effects of K56 and confirm the appropriate administration dose in humans preliminary. ## Test materials The test materials were kindly provided by YILI industrial company Ltd. (China). The probiotic capsules contained different doses of L. paracasei K56 strain (very low dose: 1 × 107 colony forming units/capsule, low dose: 1 × 109 colony forming units/capsule, high dose: 1 × 1010 colony forming units/capsule, very high dose: 1 × 1011 colony forming units/capsule), and was standardized with maltodextrin and microcrystalline cellulose. The ingredients of the placebo capsule were similar to the probiotic capsule but without the addition of K56. The final products looked and tasted identical to each other. Participants were instructed to take two capsules per day before breakfast for 60 days. ## Study participants The participants were recruited for the study at the Huadong hospital affiliated to Fudan University, Shanghai, China. A total of 74 subjects with obesity were initially signed informed consent. The inclusion criteria were as follows: [1] Body mass index (BMI) ≥ 30 kg/m2, or percentage of body fat (PBF) assessed by electrical bioimpedance ≥ $25\%$ formen and ≥ $30\%$ for women; [2] Age >18 and ≤ 60 years. The selected subjects were excluded from the study if they had any of the following conditions: [1] Patients with severe chronic diseases (coronary heart disease, uncontrolled diabetes, hypertension, mental disorders, cancer, hepatic or renal dysfunctions, etc.) and their complications; [2] Patients with severe allergy, gastrointestinal diseases, immunodeficiency; [3] Hyperthyroidism or hypothyroidism, Cushing syndrome, or any other disease affecting the results of the study; [4] History of administration of drugs affecting body fat or functional foods/supplements for obesity improvement in the past two months; [5] Use of any weight control measures (diet, exercise, etc.) in the past month; [6] Participation in any other clinical trials within the previous 3 months; [7] Unable to maintain their current lifestyle during the study period. [ 8] Failure to take the study products as required, or failure to follow up on time. ## Study design This was a randomized, single blind, placebo controlled, pilot study and was approved by the Ethics Board Committee of Huadong Hospital [20200083], the protocol was registered at the U.S. National Institute of Health (clinicaltrials.gov Identifier: NCT04980599). The recruitment was conducted through online enrollment questionnaires and telephone interviews, and subjects who met the inclusion criteria were scheduled for a baseline visit to assess their eligibility. Written informed consent was obtained from all eligible subjects who met the inclusion criteria and did not meet the exclusion criteria before enrollment. The subjects were then randomly assigned to one of the placebo group (Pla), very low dose K56 group (VL), low dose K56 group (L), high dose K56 group (H), and very high dose K56 group (VH) for a 60-day of intervention period. Randomization was performed using computer-generated random numbers by a statistician who had not participated in this study and group allocation was blinded to the participants. The intervention period was lasted for 60 days, subjects were asked to take different doses of K56 or placebo capsules two capsules per day preferably before breakfast with the specific advice to maintain their previous dietary intake and physical activity, current treatments and lifestyles during the study period. At the first and second visit, investigators dispensed one bottle of test material (60capsules/bottle) to every participant, and to prevent any viability or shelf-life issues, capsules were delivered to participants in insulated bags with ice pack, and stored in refrigerator after delivery. During the intervention period, to make sure all participants to take capsules as we suggested, we made illustration about the usage of test material, and created a WeChat group in order to remind the participants to take capsules as we suggested every day. Compliance for the consumption of the test materials was assessed by counting the returned capsules at the second and the last visit. In addition, the investigators reviewed the questionnaires for missed doses submitted by the subjects every two weeks. The subjects also recorded about undesired adverse events and emergencies in the questionnaires. The Semi-quantitative food frequency questionnaire was used to monitor the changes of dietary habits and the daily walking step numbers recorded by motion recorder was used to monitor the changes of physical activity. Anthropometric measurements, body composition assessment and vital sign assessments were conducted at the day 0, day 30 and day 60 of the intervention period. Blood samples and fecal samples were collected for the biochemical and gut microbial analysis at the day 0 and day 60. ## Outcomes The primary outcomes were changes in body fat percentage (PBF) and visceral fat area (VFA) from baseline to day 60. Secondary outcomes were changes in BMI, body weight, waist circumference, muscle mass, and metabolic parameters from the baseline to day 60. Body weight and body compositions, including body fat mass, percent body fat, visceral fat area, regional body fat mass, skeletal muscle mass were assessed using a bioelectrical impedance analysis machines (Inbody770, Biospace, Korea) while the subject was fasting and wearing only light underwear. BMI was calculated as body weight divided by the square of the height. Waist circumference was measured directly on the skin between the lowest rib margin and the iliac crest while the subject was in a standing position using a plastic measuring tape to the nearest 0.1 cm. After 10 min of rest, blood pressure was measured in a sitting position by a trained researcher using automatic BP monitor (U16, Omron,) on the left arm. Blood samples were collected after 10–12 h overnight fasting, and were analyzed for serum total cholesterol (TCH), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), fasting blood glucose (FBG), insulin (INS), glycosylated hemoglobin (HbA1c), hepatic and renal functions, white blood cells by using routine laboratory methods at Hua Dong Hospital affiliated to Fudan University. ## Sample collection and handling Fecal samples were collected for microbiome analysis at the baseline and after 60 days of intervention. Participants were asked to use a fecal collection box and sterile fecal container which were provided by investigators prior to collection. Samples were transported to the laboratory on ice bags, after which they were frozen and stored at − 80 °C until use. Total genomic DNA from each sample was extracted using a Hipure Soil DNA Kit (Magen, Guangzhou, China) according to the manufacturer’s instructions and quantified with a Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA, USA), A260/A280 ratios were measured to confirm the purity of DNA. DNA samples were snap frozen and stored at − 20 °C till used. ## Real-time PCR analysis The amplification, detection and melt curve analysis of DNA were performed on an ABI7900 Sequence Detection System (Applied Biosystems). The reaction mixture (10 μl) contained 5 μl 2 × Master mix, 0.2 μl of each of the forward and reverse primers, 1 μl of ROX, 1 μl of template DNA. The amplification program consisted of 1 cycle of 95 °C for 5 min; 40 cycles of 95 °C for 15 s, 60 °C for 1 min; followed by melting curve cycling. A standard curve from genomic DNA extracted from a pure K56 strain culture was used. Each plate was run with non-template control. ## 16S rDNA gene sequencing and bioinformatics analysis The sequencing library was constructed using a MetaVX Library Preparation Kit (GENEWIZ, Inc., South Plainfield, NJ). Briefly, 20–30 ng of DNA was used to generate amplicons that cover V3 and V4 hypervariable regions of the 16 s rDNA gene of bacteria. The forward primer contains the sequence ‘CCTACGGRRBGCASCAGKVRVGAAT’ and the reverse primers contain the sequence ‘GGACTACNVGGGTWTCTAATCC’. The 25 μl PCR mixture was prepared with 2.5 μl of TransStart buffer, 2 μl of dNTPs, 1ul of each primer, 0.5 μl of TransStart Taq DNA polymerase and 20 ng template DNA. The PCR is performed by the following program: 3 min of denaturation at 94 °C, 24 cycles of 5 s at 95 °C, 90 s of annealing at 57 °C, 10 s of elongation at 72 °C, and a final extension at 72 °C for 5 min. Indexed adapters were added to the ends of the amplicons by limited cycle PCR. Finally, the library is purified with magnetic beads. The concentration is detected by a microplate reader(Tecan, Infinite 200 Pro) and the fragment size is detected by $1.5\%$ agarose gel electrophoresis which is expected at ~ 600 bp. Next generation sequencing was conducted on an Illumina Miseq Platform (Illumina, San Diego, USA). PE300 paired-end sequencing was performed according to the manufacturer’s instructions. After sequencing, Illumina MiSeq raw data were sorted by sample using index sequences, and paired-end FASTQ files were generated for each sample. The sequencing adapter sequence and F/R primer sequence of the target gene region were removed, bases with Phred quality score lower than 20, and sequences less than 200 bp in length were removed using Cutadapt (v1.9.1, https://cutadapt.readthedocs.io/en/stable/). After sequencing, error-corrected paired-end sequences were assembled into one sequence, and sequences containing N and chimeric sequences were removed, resulting effective sequences for OUT clustering (The procedures were conducted by GENEWIZ, Inc., South Plainfield, NJ). VSEARCH (1.9.6) was used for clustering (sequence similarity is set to $97\%$) with reference data base Silva138. Then the representative sequences of OTUs were analyzed by RDP classifier (Ribosomal Database Program) Bayesian algorithm, and the community composition of each sample was counted at different species classification levels. Based on the obtained OTU analysis results, the α diversity information such as ACE, Shannon, Simpson and Chao1 indices were calculated to confirm the species diversity and uniformity of the microbial community in the sample using QIIME 1.9.1. Based on Bray–Curtis distance, beta diversity between samples (information about microbial community diversity between samples in comparison groups) was determined, and relationships between the samples were visualized using principal coordinate analysis (PCoA) plots. Linear discriminant analysis effect size (LEfSe) was performed using LEfSe software (v1.0, https://huttenhower.sph.harvard.edu/galaxy/). ## Statistical analysis Prism 8.0.1 (GraphPad, San Diego, CA, USA) was used for statistical analyses on body composition and blood parameters. For continuous variables, normality tests were performed using Shapiro–Wilk tests. Normally distributed data were expressed as mean ± standard deviation and were analyzed by one-way ANOVA test with multiple comparisons by controlling the false discovery rate (Benjamini, Krieger, & Yekutieli); data with skewed distribution were expressed as median (interquartile range) and were analyzed by Kruskal–Wallis test. To test the differences between the endpoint and baseline values, the paired t-test was conducted if the data were normally distributed or Wilcoxon signed rank test if the data distribution was skewed. Significant differences in the relative abundance of microbial phyla, genera, and alpha diversity were analyzed using R software. Kruskal–Wallis test was used for between group comparisons and Wilcoxon rank-sum test for within group comparisons. A false discovery rate (FDR) based on the Benjamini–Hoch-berg (BH) adjustment was applied for multiple comparisons. A p value < 0.05 was considered to be significant. ## Baseline characteristics of subjects The study populations and reasons for exclusions are shown in Fig. 1. Seventy-four eligible subjects were enrolled in this study and randomized into five groups for 60-day intervention. Two subjects were dropped-out during the intervention period for personal reasons (one in the L_K56 group and one in the H_K56 group). A total of seventy-two subjects completed the 60-day intervention; however, one subject with < $85\%$ treatment compliance, five subjects who administered antibiotics within two weeks before sample collection were excluded from the analysis. Therefore, a total of 66 subjects were included in the data analysis. No adverse events were reported as reasons for dropout. The baseline characteristics of the subjects who completed the study without major protocol violations are summarized in Table 1. The demographics of the subjects were similar among the different groups. There were no significant differences between groups in anthropometric variables, lipid profiles, glycosylated hemoglobin, fasting blood glucose and parameters of liver, renal functions at the baseline. Fig. 1Flow diagram of enrollment, assignment, and follow-up of study participantsTable 1Baseline characteristics of subjectsVariablesPlacebo($$n = 13$$)VL_K56($$n = 14$$)L_K56($$n = 12$$)H_K56($$n = 13$$)VH_K56($$n = 14$$)p valueGender, F/M$\frac{6}{79}$/$\frac{58}{48}$/$\frac{58}{60.85}$Height, m166.70 ± 10.64165.20 ± 9.28165.30 ± 7.18165.90 ± 10.79163.90 ± 8.650.95Age, year40.15 ± 9.7839.00 ± 7.7438.42 ± 9.5944.92 ± 8.0339.14 ± 10.520.38Weight, kg88.84 ± 23.5780.38 ± 17.2686.96 ± 14.3580.92 ± 17.2881.34 ± 18.600.67BFM, kg34.48 ± 12.1830.96 ± 9.7334.34 ± 7.2430.58 ± 6.7431.13 ± 8.920.68BMI, kg/m231.62 ± 5.7029.27 ± 4.7731.72 ± 4.0529.12 ± 3.7729.99 ± 4.710.45PBF, %38.71 ± 7.5238.06 ± 5.6239.42 ± 4.5938.10 ± 5.4538.26 ± 5.540.97VFA, cm2159.90 ± 49.41146.80 ± 47.05159.20 ± 30.52150.00 ± 34.37146.90 ± 38.150.86WC, cm104.20 ± 15.9999.99 ± 14.46102.40 ± 10.28100.60 ± 12.5499.22 ± 13.170.88HbA1c, %5.70 (0.90)5.65 (0.40)5.65 (0.60)5.60 (0.30)5.40 (0.20)0.25GA, %12.68 ± 2.3511.61 ± 1.4312 ± 1.5112.55 ± 1.2712.11 ± 1.500.46FBG, mmol/L5.60 (0.50)5.20 (0.30)5.00 (1.05)5.00 (1.10)4.65 (0.80)0.15TCH, mmol/L5.06 ± 0.825.27 ± 0.685.40 ± 0.664.94 ± 0.764.97 ± 0.710.43TG, mmol/L1.87 (1.41)1.44 (1.30)1.86 (0.86)1.36 (0.64)1.20 (0.44)0.28ALT, U/L23.30 (30.80)22.95 (23.60)28.55 (29.75)35.50 (21.70)20.60 (18.40)0.35AST, U/L16.10 (16.60)18.35 (6.70)18.85 (12.85)24.30 (9.10)16.55 (5.40)0.30Creatinine, μmol/L79.15 ± 14.6575.11 ± 11.0973.97 ± 10.9471.20 ± 11.9275.11 ± 14.710.63Urea, mmol/L5.20 (0.80)5.10 (0.90)5.20 (2.20)4.80 (1.90)5.35 (2.10)0.90Data are expressed as mean ± SD or median (interquartile range). A chi-square test was performed on categorical variables. One-way ANOVA test or Kruskal–Wallis test was performed on continuous variables. BMI, body mass index; BFM, body fat mass; PBF, percent body fat; VFA, visceral fat area; HbA1c, Glycosylated hemoglobin; GA, Glycated albumin; FBG, fasting blood glucose; TCH, total cholesterol; TG, triglycerides; ALT, Alanine transaminase; AST, Aspartate Transaminase ## Food intake and activities Subjects were advised to maintain their original dietary pattern and activity level throughout the intervention period. According to the questionnaires feedback and recorded step counts, most of the subjects were able to maintain the required consistency of dietary and activity habits throughout the intervention period. There were no significant differences between groups in dietary intake and habitual activity at the baseline and end of study. ## Adverse events and safety parameters The adverse events reported by participants during the intervention period included loose stools, feeling of incomplete evacuation, or flatulence, which were potentially product-related. Adverse events are summarized in Table 2. The symptoms were generally mild and of short duration, and there were no any dropouts occurred due to the adverse events. There were no significant abnormal changes in measured safety parameters: vital signs, renal and hepatic function markers (Table 3).Table 2Gastrointestinal symptoms reported by the subjectsSymptomsplaceboVL_K56L_K56H_K56VH_K56TotalLoose stools3 [23]5 [36]1 [8]4 [31]0 [0]13 [20]Flatulence5 [38]10 [71]9 [75]9 [69]7 (0.5)40 [60]Feeling of incomplete evacuation2 [15]2 [14]1 [8]1 [8]5 [36]9 [14]Values are expressed as number (%)Table 3Changes in biomarkers of hepatic function, renal function and vital signsPlaceboVL_K56L_K56H_K56VH_K56p value△ALT, U/L− 1.10 (3.70)− 0.85 (8.30)− 2.85 (5.30)− 1.80 (7.00)2.50 (7.70)0.27△AST, U/L− 1.40 (5.80)− 0.55 (3.80)− 0.75 (6.55)− 4.00 (2.60)0.30 (3.20)0.69△Creatinine, μmol/L− 10.02 ± 7.38− 6.00 ± 3.96− 9.16 ± 5.82− 8.10 ± 3.08− 8.41 ± 5.660.59△Urea, mmol/L− 0.10 (1.40)0 (2.300)− 0.25 (1.20)− 0.10 (0.60)− 0.10 (0.70)0.59△DBP, mmHg− 1.39 ± 9.16− 1.14 ± 10.491.67 ± 8.660.15 ± 8.08− 1.50 ± 9.490.90△SBP, mmHg− 2.39 ± 15.40− 1.14 ± 11.600.33 ± 14.49− 4.46 ± 11.582.86 ± 17.710.74△HR, bpm− 2.54 ± 8.771.42 ± 9.641.00 ± 7.15− 1.62 ± 11.15− 5.43 ± 4.830.23Data are expressed as mean ± SD or median (interquartile range). △ Changes in the mean value from baseline to 60 days. p value obtained from one-way ANOVA test or Kruskal–Wallis test. DBP, diastolic blood pressure; SBP, systolic blood pressure; HR, heart rate; ## K56 controls body and visceral fat, reduces waist circumference The relative change in PBF from baseline to the end of the intervention period was the primary outcome of our study. After 60 days of probiotic intake, the mean value of PBF in L_K56 and H_K56 groups decreased compared with baseline values, especially the change in L_K56 group was statistically significant (− $0.867\%$, $$p \leq 0.004$$). In the placebo and VH_K56 groups, there were non-significant increases in PBF from baseline to 60 days ($0.29\%$, $0.47\%$), resulting in significant differences in the mean value of changes in L_K56, H_K56 groups compared to placebo and VH_K56 groups. The total body fat mass was significantly reduced in the probiotic L_K56 group (− 0.72 kg, $$p \leq 0.018$$) at the end of the study compared with baseline. There were observations of non-significant reductions in BFM in the VL_K56, H_K56 groups, and non-significant increases in placebo and VH_K56 groups. Changes in body fat mass were most pronounced in the trunk area and a similar pattern was observed in the visceral fat area (Fig. 2). Body weight and BMI were not significantly reduced in L_K56, H_K56, VL_K56 groups, whereas there were increases in placebo and VH_K56 groups compared with baseline values, the change was statistically significant in group VH_K56. The results also indicate that, in L_K56 and H_K56 groups, there was a trend towards an increase in skeletal muscle mass. Regarding to waist and hip circumferences, the reduction in waist circumference from baseline (− 1.7 cm, $$p \leq 0.01$$) in L_K56 group and the increase in hip circumference from baseline (0.86 cm, $$p \leq 0.003$$) in VH_K56 group were statistically significant, while the changes in other groups were not significantly different. However, the mean change in BFM, VFA, weight, BMI, waist circumference and hip circumferences were not significantly different between groups. Fig. 2Results of anthropometric and body composition variable measurements. The graphs show a percent body fat, b body fat mass, c body fat mass of trunk, d visceral fat area, e weight, f body mass index, g hip circumferences, h waist circumference, i skeletal muscle mass. The data points correspond to the mean ± SEM. * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, comparison of baseline and 60 days values within groups (paired t-test); #$p \leq 0.05$, ##$p \leq 0.01$, ###$p \leq 0.001$, differences in changes in mean value from baseline to 60 days between groups (one-way ANOVA) ## K56 decreased glycosylated hemoglobin compared to baseline, but did not affect lipid profiles Table 4 shows blood lipid profile, insulin, glycosylated hemoglobin, glycated albumin, fasting blood glucose at baseline and after intervention. Serum total cholesterol, triglyceride, HDL-Cholesterol, LDL-Cholesterol were didn’t change significantly after 60 days intervention compared with baseline in all groups, and there were no significant differences in changes from baseline to 60 days between groups. A statistically significant reduction in glycosylated hemoglobin of L_K56 group was observed at the end of intervention compared with baseline, while the changes in other groups were not significant. In addition, the glycated albumin levels in placebo and VH_K56 groups were elevated from baseline with statistically significance, and the changes in other groups were not significant. However, there were not significant differences in changes of the abovementioned variables among all groups. Insulin concentration and C-peptides were not change significantly within groups, and did not differ significantly between groups. Table 4Biochemical measurementsVariablesPlaceboVL_K56L_K56H_K56VH_K56p valueHbA1c, % Baseline5.70 (0.90)5.65 (0.40)5.65 (0.60)5.60 (0.30)5.40 (0.20) 60 days5.70 (0.60)5.55 (0.40)5.45 (0.50)5.40 (0.20)5.30 (0.20) Change− 0.10 (0.40)− 0.10 (0.10)− 0.20 (0.20)− 0.10 (0.20)0.00 (0.20)0.12GA, % Baseline11.90 (2.40)11.95 (2.80)12.05 (1.90)12.50 (1.30)11.65 (2.00) 60 days11.90 (2.70)11.95 (2.60)12.45 (2.30)12.60 (1.40)12.15 (2.30) Change0.40 (0.60)0.30 (0.30)0.30 (0.50)0.10 (0.20)0.20 (0.70)0.69FBG, mmol/L Baseline5.60 (0.50)5.20 (0.30)5.00 (1.05)5.00 (1.10)4.65 (0.80) 60 days5.30 (1.10)5.15 (0.80)5.25 (1.15)4.80 (1.40)4.60 (1.00) Change− 0.30 (0.80)0.050 (0.80)0.00 (0.40)0.00 (0.50)− 0.05 (0.10)0.73HDL-C, mmol/L Baseline1.39 ± 0.331.48 ± 0.281.41 ± 0.391.42 ± 0.311.43 ± 0.35 60 days1.289 ± 0.331.47 ± 0.291.36 ± 0.321.43 ± 0.341.43 ± 0.31 Change− 0.11 ± 0.18− 0.01 ± 0.12− 0.05 ± 0.110.01 ± 0.16− 0.002 ± 0.100.19LDL-C, mmol/L Baseline3.05 ± 0.863.28 ± 0.623.41 ± 0.463.03 ± 0.653.02 ± 0.61 60 days3.14 ± 0.793.62 ± 0.963.33 ± 0.583.27 ± 0.553.16 ± 0.65 Change0.09 ± 0.510.34 ± 0.66− 0.08 ± 0.420.25 ± 0.380.14 ± 0.330.23TCH, mmol/L Baseline5.06 ± 0.825.27 ± 0.685.40 ± 0.664.94 ± 0.764.97 ± 0.71 60 days5.08 ± 0.935.45 ± 1.055.20 ± 0.725.09 ± 0.634.97 ± 0.75 Change0.02 ± 0.690.18 ± 0.69− 0.20 ± 0.460.15 ± 0.44− 0.00 ± 0.360.44TG, mmol/L Baseline1.87 (1.41)1.44 (1.30)1.86 (0.86)1.36 (0.64)1.20 (0.44) 60 days2.50 (1.66)1.37 (0.81)1.69 (1.08)1.31 (0.65)1.32 (0.57) Change0.23 (1.05)− 0.08 (0.26)0.05 (0.75)0.10 (0.33)0.03 (0.44)0.44Peptide C, ng/mL Baseline2.80 (1.60)2.80 (1.10)3.35 (2.00)2.60 (0.90)2.85 (1.20) 60 days2.70 (1.70)3.10 (2.10)3.00 (1.75)2.90 (1.20)2.80 (1.10) Change0.10 (0.70)− 0.10 (0.80)0.00 (0.95)0.10 (0.30)− 0.10 (0.40)0.70Insulin, μU/mL Baseline14.30 (7.30)13.50 (5.90)18.30 (20.70)13.70 (5.00)13.55 (10.30) 60 days13 (10.10)13.70 (12.50)16.55 (13.75)15.10 (5.30)16.60 (8.50) Change3.30 (8.30)1.95 (5.10)0.25 (6.25)1.50 (4.20)0.95 (5.00)0.80Data are expressed as mean ± SD or median (interquartile range). Differences in changes in mean values from baseline to 60 day between groups, p value obtained from One-way ANOVA test or Kruskal–Wallis test. $p \leq 0.05$, *$p \leq 0.01$, obtained from paired t-test or Wilcoxon signed rank testHbA1c, glycosylated hemoglobin; GA, glycated albumin; FBG, fasting blood glucose; HDL-c, high density lipoprotein cholesterol; LDL-c, low density lipoprotein cholesterol; TCH, total cholesterol; TG, triglycerides ## 60 days supplementation with Lacticaseibacillus paracasei K56 increased fecal K56 levels determined by qPCR. Table 5 shows the number of detected positive samples and average quantity of fecal K56 determined by qPCR at the baseline and after 60-day intervention. There were no significant differences in number of positive samples and average quantity of K56 between all groups at baseline. After 60-day of intervention, the number of samples with increased fecal K56 levels in all probiotic supplemented groups were significantly higher compared with the placebo group, where there were no elevated K56 levels in any subject. Table 5Quantitative analysis of K56 in feces at the baseline and day 60 by qPCRGroupBaseline60 daysIncreased fecal K56 levelbDetected positiveaAverage quantity in positive samples [logconcentration/ul original DNA]Detected positiveAverage quantity in positive samples [logconcentration/ul original DNA]Placebo$\frac{4}{144.331}$ ± $\frac{0.2703}{144.887}$ ± $\frac{0.6720}{14}$VL_K$\frac{566}{154.411}$ ± $\frac{1.48111}{153.526}$ ± $\frac{1.1669}{15}$L_K$\frac{564}{144.098}$ ± $\frac{1.68314}{142.990}$ ± $\frac{0.99112}{14}$H_K$\frac{564}{144.797}$ ± $\frac{1.00313}{141.930}$ ± $\frac{1.33312}{14}$VH_K$\frac{567}{154.836}$ ± $\frac{1.04915}{151.685}$ ± $\frac{1.35615}{15}$aFeces samples were analyzed from all participants who had returned both baseline and after intervention samplesbParticipants were considered have increased fecal K56 levels when the K56 concentration was at least one log higher at the end-of-study compared to baseline, while when the concentration was undetectable or remained within one log compared to baseline they were considered didn’t have increases ## 60 days supplementation with Lacticaseibacillus paracasei K56 modulated gut microbial diversity and composition Based on the results of the 16 rDNA sequences (V3–V4 region) using MiSeq performed in all groups, the bacterial group was dominated by the phyla Bacteroidota, Firmicutes, Proteobacteria, Actinobacteriota and Fusobacteriota (Fig. 3A).Fig. 3Bacterial abundance at phylum and genus level, alpha diversity, beta diversity. A Bacterial abundance at the phylum level at baseline and end of study in placebo and probiotic groups. B bacterial abundance at the genus level at baseline and end of study in placebo and probiotic groups. Boxplots show the alpha diversity of bacterial communities at baseline and after intervention in placebo and probiotic groups for C Shannon, D Simpson, E ACE, F Chao1 indices. G Principal coordinate analysis (PCoA) showing the microbial community distance between baseline and end of study in each group A small portion of the phyla Desulfobacterota, Verrucomicrobiota, Cyanobacteria, Synergistetes, Patescibacteria Patescibacteria and campilobacterota appeared in probiotic and control groups. At baseline, bacterial phylum did not differ significantly between groups, and there were no significant changes in the abundance of bacterial phylum from baseline to end of intervention in all groups. Considering the Firmicutes to Bacteroides ratio, the H_K56 group had higher F/B ratio than other groups at the baseline, but there were no significant differences among all groups (median for Pla, VL_K56, L_k56, H_K56, VH_K56 groups were 0.65, 0.90, 0.75, 1.43, 0.99 respectively, $p \leq 0.05$). The changes of B/F ratio from baseline to end-of-study were not differed significantly among all groups (median of changes − 0.05, 0.01, 0.17, − 0.20, − 0.17 for Pla, VL_K56, L_K56, H_K56, VH_K56 respectively). At the genus level (Fig. 3B), the abundance of genus Bacteroides at baseline was lower and the abundance of genus Prevotella was higher in L_K56 group than other groups, but they didn’t differ significantly between groups. After 60 days of intervention, the abundance of genus Parabacteroides in L_K56 group increased significantly from baseline ($$p \leq 0.01$$, Wilcoxon test). The genus Bacteroides, Alistipes, Parasutterella in L_K56 group experienced increases in abundance and genus Prevotella in L_K56 group experienced decrease in abundance through the intervention period. The genus Bacteroides was increased slightly in VH_K56 group. Agathobacter in H_k56 group was decreased significantly ($$p \leq 0.035$$). Alpha diversity indices (Fig. 3C–F), including ACE index, Chao1 index, Shannon index and Simpson index, indicate the richness and evenness of gut microbial community. At the end of the intervention, there were significant increases in ACE, Shannon, Chao1 and Simpson indices from baseline in the L_K56 group; And significant decreases in ACE and Chao1 indices in the H_K56 group. The increases in ACE and Shannon indices in the L_K56 group were differed significantly from other groups. The β diversity of each group was examined by principal coordinate analysis (PCoA), the results showed that there were no significant differences from baseline to end of study in each group except for a slight separation in L_K56 group (Fig. 3G). The differentially abundant taxa between baseline and end of study in each group was identified by Linear discriminant analysis Effect Size (Fig. 4). At phylum level, there were no significantly differentiated bacteria among top ten phyla in all groups. The abundance of class Coriobacteria and its members (order Coriobacterials, family Coriobacteriaceae, genus Collinsella) decreased significantly at the end of study compared to baseline in each group. The abundance of Parabacteroids distasonis* in L_K56 group, Clostridium scindens in placebo group and Veillonella atypica in VH_K56 group were significantly increased than baseline. Fig. 4Linear discriminant analysis (LDA) effect size (LEfSe) was used to calculate the taxa that best discriminated between the baseline and end of study in each group. Taxa that reached a linear discriminant analysis score (log10) > 2.0 are highlighted and labelled at taxonomic levels from phylum to species ## Discussion This study presents a comprehensive analysis of the effects of supplementation with a novel probiotic strain on obesity, metabolic parameters and gut microbiota in free-living adults with obesity. After 60 days of intervention period, we found that supplementation with Lacticaseibacillus paracasei K56 at a daily dose of 109 CFU determined beneficial effects on obesity and glucose metabolism by reducing body fat mass, body fat percentage, trunk fat mass and visceral fat area, waist circumferences and glycosylated hemoglobin. Since central fat has a greater negative impact on the metabolic risk associated with obesity, the reduction of fat compartment may represent a beneficial effect of probiotics, even though there was no significant reduction in body weight. However, the effects of K56 were no longer significant when the dose of supplementation was higher (1010 CFU/day) or lower (107 CFU/day), and at very high doses(1011 CFU/day), there was a trend toward opposite effects. Such dose–response effects of probiotics have been observed in a previous clinical study in which, after obese adults were randomized to receive low-dose lactobacillus gasseri BNR17 (BNR-L, 109 CFU/day), or high-dose BNR17 (BNR-H, 1010 CFU/day) for 12 weeks, reduced visceral adipose tissue was only observed in high dose of L. gasseri BNR17 group [21]. In a randomized clinical trial, obese subjects received 200 g/d fermented milk contains 108 CFU/g Lactobacillus gasseri SBT2055 for 12 weeks, the abdominal visceral and subcutaneous fat area reduced from baseline significantly by an average of $4.6\%$ and $3.3\%$ respectively [16]. However, when the concentrations of SBT2055 were 106 or 107 CFU/g, significant reductions were didn’t present, suggesting a possible diminution of effectiveness at lower doses [17]. However, in a recent study, researchers observed that there were no unequivocal relationships between the effect of probiotics and the dose [25]. In a previously reported animal study, high fat diet induced obese mice were treated by gavage five times a week with freshly prepared K56 (106 CFU/day or 108 CFU/day) alone or in combination with α- galactooligosaccharides for 12 weeks. After treatment, all probiotic groups significantly decreased body weight gain and visceral fat than high fat diet (HFD) group, especially at the dosage of 108CUF/day alone or combined with α-GOS had lower body weight and fat gain than 106 CFU/day group [23]. In another animal study, HFD-fed mice were administered K56 suspension of 107 CFU/day, 109 CFU/day, and 1011 CFU/day. After 10 weeks of intervention, the three K56 groups did lowered the weight gain and abdominal fat than HFD group, and there were no significant differences between the three k56 groups. However, the abdominal fat by MRI scanning in 107group was significantly higher than normal diet (ND) group, whereas no significant increases or comparable to ND group in 109 and 1011 groups. Moreover, regarding the impact to glucose metabolism, the AUC of oral glucose tolerance test was significantly reduced in 109 and 1011 groups than HFD group, especially in the 109group [24]. Lactobacillus johnsonni 3121 and Lactobacillus. rhamnosus 86 were also evaluated for their anti-obesity effects using a high-fat diet-induced obese mouse model. Daily oral administration of L. johnsonni 3121 and L. rhamnosus 86 for 12 weeks (1010 CFU/day) significantly improved serum lipid profile and downregulated the expression of genes related to adipogenesis and lipogenesis in epididymal white adipose tissue of high-fat diet fed obese mice ($p \leq 0.05$) [26]. Fat mass expansion of ketonic diet induced diabetic mice was ameliorated by treatment with Bifidobacterium animalis ssp. Lactis 420 at a dose of 1010 CFU/day ($$p \leq 0.020$$), and there was a marked trend of fat mass reduction by 109 CFU/day ($$p \leq 0.066$$) [27]. These findings suggest that, the dose probiotics need varies greatly depending on the strain. Although the recommended intake of probiotics is mainly between 107 and 1011 CFU/day, some strains have been shown to be efficacious at lower levels, while some requires substantially more [28]. In this study, obesity parameters such as PBF, VFA, BFM, WC were reduced significantly after treatment with K56 in L_K56 group, and trend to improvements were observed in VL_K56 and H_K56 groups. Although the results were not as robust as animal experiments, but generally consistent with the results of above-mentioned animal experiments in which the anti-obesity effects of K56 were evaluated. An accumulating body of evidence has suggested that the gut microbiota of obese individuals is characterized by a decrease of α diversity, an alteration of β diversity, an increased abundance of phylum Firmicutes and Firmicutes-to-Bacteroidetes ratio, while some other studies have suggest that no significant difference existed in obese and lean individuals concerning Firmicutes/Bacteroidetes ratio and the abundance of Bacteroidetes [29]. Numerous mechanisms of action for probiotic-mediated weight loss have been proposed. These include the modification of the gut microbiota, reduction of intestinal permeability, and modulation of the immune system [10, 30]. In our study, concerning the changes in abundance of bacterial phyla after intervention period, there were no statistically significant changes in each group. This is in accordance with a previously reported clinical trial in which multi-species probiotic includes nine strains of Bifidobacterium and Lactobacillus altered the influence of microbiota on biochemical, physiological and immunological parameters, but it didn’t affect overall composition of gut microbiota after 12-weeks administration to obese, postmenopausal women. It is noteworthy that, low-dose K56 supplementation increased the abundance of genus Parabacteroides and species Parabacteroides distasonis significantly. According to previous papers, the gut microbial community of obese patients exhibited a significant decrease in the relative abundance of several Bacteroidetes taxa including Parabacteroides spp., Bacteroides spp. when compared to normal weight subjects and negatively correlated with body fat and waist circumferences [31, 32]. Besides, researchers recently have found that Parabacteroides distasonis could affect the proportion of secondary non-12α-hydroxylated bile acids and metabolism of glucose and lipid, ameliorate weight regain via increased thermogenesis [33]. Although bile acids were not analyzed in this study, it is possible that treatment with K56 induces weight loss in subjects with obesity by increasing the abundance of Parabacteroides distasonis species, followed by increased secondary non-12α-hydroxylated bile acids and increased thermogenesis. In addition, genus Bacteroides in L_K56 and VH_K56 groups, Alistipes and Parasutterella in L_K56 group each trended towards increased abundance in the gut after intervention. In an animal study, it has been reported that Bacteroides has protective effects against weight gain [34]. Alistipes, a genus belongs to Bacteroidetes phylum, has been reported to inversely correlated to adiposity, lipid, and glucose homeostasis parameters [35], and may have protective effects against some diseases, including liver fibrosis, colitis, cancer immunotherapy, and cardiovascular disease [36]. Parasutterella was reported to have potential role in bile acid maintenance and cholesterol metabolism [37]. After administration of K56, we also noted a trend of reduction in the abundance of Prevotella in L_K56 group. In a previous clinical trial, it has been reported that high abundance of Prevotellaceae and Veillonellaceae associated with obesity and impaired glucose metabolism [38]. Recently, researchers have proposed that high abundance of Prevotella, especially P. copri in the gut may be associated with excessive energy uptake and increase fat accumulation [39]. In addition, Individuals with reduced microbial gene richness present more pronounced dys-metabolism and low-grade inflammation that were the main characteristics of obesity, suggesting that reduced gut microbial diversity accompanied changes in key species is the decisive factor in obesity [40]. According to the ACE, Shannon, Simpson and Chao1 indices, there were significant changes in alpha diversity of the intestinal microbial community in L_k56 and H_K56 groups from baseline to end of study and the changes in alpha diversity in L_K56 group differed significantly from other groups. This in agreement with a previous RCT that also reported significant differences in alpha diversity after supplementation with probiotic Lactobacillus curvatus HY7601 and Lactobacillus plantarum KY1032 [41]. However, the PCoA scatter plot for baseline and after intervention didn’t differed in each group except for a slight trend to separation in L_K56 group. Taken together, the results indicate that K56 administration is expected to enrich the microbial community, modulate the gut microbiota associated with obesity. This is in consistent with previous preclinical study in which K56 supplementation restored the gut microbiota of HFD fed mouse and ameliorated HFD induced obesity and associated metabolic parameters such as blood glucose and lipid profile [24]. But we didn’t observe significant changes in plasma lipid profile and fasting blood glucose in present study, except for a statistically significant reduction in HbA1c in L_K56 group. However, the average levels of plasma lipid and glucose were within normal range at baseline, and after 60 days of K56 supplementation in all groups. This result warrants further investigations in patients with hyperlipidemia and prediabetes to evaluate a metabolic benefit of K56. Also, this exploratory study enrolled a small number of individuals, which affects statistical power, especially when the effects of an intervention on clinical features were investigated. As a result, the study was not powered to deliver definitive conclusions on the end points related to energy balance. However, all the groups were randomized and investigated blindly. We may argue that any confounding factors were probably equally distributed between different groups. And we didn’t observe any improvements in Placebo group over the intervention period. Based on this exploratory study and preclinical animal studies, we could suggest that administration of K56 in adequate amount, may help improve obesity and related metabolic parameters, and the dosage as high as 1011 CFU/day is safe. If we take efficiency and economy into account, the dose of 109 CFU/day could probably be a better option. Meanwhile, this study was a promising start for future clinical trials with propriate design to confirm and extend our study results. ## Conclusion This was the first randomized single-blind placebo controlled exploratory study to investigate the effects of supplementation with a novel probiotic strain K56 in obese free-living adults. The results suggest that, under the condition of maintaining original dietary intake and physical activity, supplementation with different doses of Lacticaseibacillus paracasei K56 has certain effect on reducing body fat, improving glucose metabolism and modulating the gut microbiota to favor anti-obesity characteristics, especially at a dose of 109 CFU/day. ## References 1. 1.World Health Organization. Obesity. https://www.who.int/health-topics/obesity. Accessed 20 Nov 2022. 2. Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. 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--- title: Time trends and geographical patterns in suicide among Greenland Inuit authors: - Ivalu Katajavaara Seidler - Janne Schurmann Tolstrup - Peter Bjerregaard - Allison Crawford - Christina Viskum Lytken Larsen journal: BMC Psychiatry year: 2023 pmcid: PMC10031872 doi: 10.1186/s12888-023-04675-2 license: CC BY 4.0 --- # Time trends and geographical patterns in suicide among Greenland Inuit ## Abstract ### Background Between 1980 and 2018 Greenland has had one of the highest suicide rates in the world with an average rate of 96 suicides per 100,000 people annually. The aim of this study is to investigate suicide rates in Greenland according to age, birth cohort, period, sex, place of residence and suicide method from 1970 until 2018. ### Methods Suicide rates were examined using register and census data from 1970–2018 among Greenland Inuit. Rates were calculated by Poisson regression in Stata and by use of Excel. In analyses of the period trends, rates were standardized according to the World Standard Population 2000–2025. ### Results The suicide rate has been declining since a peak at 120 suicides per 100,000 people annually in the 1980s but remained high at a rate of 81.3 suicides per 100,000 people annually from 2015–2018. Descriptive analyses point to the decrease in male suicides as the primary factor for the overall decreasing rates while the rate among women has been increasing. Simultaneously, the proportion of women who used a violent suicide method increased from $60\%$ in 1970–1979 to $90\%$ in 2010–2018. The highest rates are seen among young people, especially young men aged 20–24 years and youth suicide rates increased with later birth cohorts. When the rates started to increase in the 1980s both the capital Nuuk and East Greenland had the highest rates. Since then, the rate in Nuuk has declined while the rate in East Greenland was three times the national rate from 2015–2018. ### Conclusions From 1970 to 1989 the suicide rate increased from 28.7 to 120.5 per 100,000 people mirroring a rapid societal transition in the post-colonial period. The rate has slowly declined from the peak in the 1980s but remains at a very high level. Young people in general are at risk, but the steady increase in the rate among women is worrying and there is a need to investigate underlying causes for this development. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12888-023-04675-2. ## Introduction Greenland is the least densely populated country in the world with 56,000 people living scattered along the coast of the 2 million km2 island. The majority of the population are Inuit making up $90\%$ of the population [1]. Inuit in Greenland are related to Inuit populations living across the Arctic sharing similar cultural heritages, language, but also challenges. Indigenous populations across the Arctic experience very high suicide rates compared to non-indigenous populations [2, 3]. These high rates are linked to colonization, rapid sociocultural and economic transformations leading to significant changes in lifestyles and livelihoods across a historically short span of 50 years [4–6]. This has been linked to the onset of events causing social problems, intergenerational traumas, high prevalence of adverse childhood experiences, and loss of cultural affiliation especially impacting youth mental health [5, 7, 8]. With one of the highest suicide rates in the world it is to great public health and societal value to continuously monitor suicide rates in regard of specific trends and risk factors [9]. Today Greenland experiences between 60 to 40 suicides every year and from 2000 to 2018 the average annual rate was 87.3 per 100,000 people [6, 10]. ## A century of monitoring suicide in Greenland The World Health Organization has stated that registration and ongoing monitoring of suicides are essential for national prevention strategies [9]. Registrations of suicides from 1901–1960 reported that suicide was a rare event in Greenland mainly related to psychiatric disease with rates varying from 0–3.5 suicides per 100,000 people annually [6, 11, 12]. After the initiation of an intensive modernization plan for Greenland in the 1950s the suicide rate increased dramatically from the 1960s and stabilized at a high level from 1980 and onwards with an average rate of 96 suicides per 100,000 people annually [6]. From 1968 until 2018, suicide has accounted for $10\%$ of all deaths in Greenland contributing to a significantly lower life expectancy when comparing with Western countries and in the period of 2005–2009 suicide was the third leading single cause of death in Greenland [1, 5]. Methods of suicide are categorized as either violent or non-violent. Distinguishing the method of suicide is important because violent methods (e.g., hanging, shooting, or drowning) are associated with a higher lethality than non-violent methods [13]. The differences in lethality were investigated in a systematic review and meta-analyses that reported case fatality rates varying from 84.6 to $89.7\%$ for violent methods such as hanging or suffocation and shooting to $8\%$ for poisoning (non-violent method) [14]. There are several ways of defining which methods are violent or non-violent but a simple approach is to define non-violent methods as poisoning and all other methods as violent [13]. The type of method is associated with sex and the general pattern is that men use violent methods more often than women and thereby increasing the lethality [13, 14]. In Greenland research has found the same association between method and sex which may in turn explain some of the sex-related differences in suicide rates [15, 16]. ## Sex, age, birth cohort and geography Most suicides in Greenland are youth suicides with the highest rates among young men [6]. Regional differences have shifted across time and more recent research has found the highest rates in East and North Greenland as opposed to the capital Nuuk where rates have decreased from more than 150 suicides per 100,000 people annually in the 1980s to around 60 since the 2000s [6]. An inspection of birth cohorts show a pattern of higher rates of youth suicides in younger birth cohorts where the youngest birth cohorts include the age group 10–14 years [6]. Besides age, period and birth cohort are key indicators/variables to include when analyzing temporal trends in suicide rates because they reflect both historical context and the rapid sociocultural developments of the postcolonial period in Greenland dating from 1953 and forward [5–7, 17, 18]. ## Objective The study objective was to investigate suicide rates in Greenland according to age, birth cohort, period, sex, and place of residence from 1970 until 2018. To get a more detailed understanding of the main objective potential patterns in suicide methods according to sex during the study period were investigated. Results may offer important knowledge for future strategies and interventions to prevent suicides in Greenland. ## Methods This study is a register-based study on the Greenland Inuit population from 1970 to 2018. Greenland Inuit here defined as both being born and living in Greenland. The exclusion of other ethnicities is because the majority of non-Greenlanders are Danes who have come to Greenland to work and who often stay for a limited time. These are assumed to have a significantly different risk profile for suicide, given that the suicide rate in Denmark is around seven times less than the rate Greenland based on rates from 2007 until 2018 [19]. ## Data sources We examined suicide rates using register and census data for Greenland Inuit. The Central Population Register covers all Greenlandic citizens each assigned with a unique personal identification number allowing for register-linkage. Information on suicide was obtained from the nationwide Greenland register of causes of death dating back to 1968. The register is updated regularly, and deaths are validated against the Central Population Register. From 1970 until 2018 1,952 suicides were registered among 19,766 deaths using the International Classification of Diseases ICD-8 and ICD-10 codes developed by the World Health Organization. Codes included for suicide were E950, E951, E953-E959 for ICD-8 and X60-X65, X70-X75, X78, X80, X83-X84 for ICD-10. All suicide deaths are verified by a physician who then completes a medical death certificate. The Danish Health Data Authority code deaths to ICD codes and the information is recorded in the Greenland register of causes of death. All deaths due to injuries require that the police are involved [20]. The Greenland causes of death register has been validated and compared to police records with good agreement on suicides. When comparing the death certificates with police records from 1977 to 1986, 370 of the 420 suicides from the death certificates were registered in the police records [21]. A more recent study evaluated the usability of the register to be of medium quality based on The Vital Statistics Performance Index for Quality [20]. Population size was based on information from the Central Population Register and obtained from the statistical databank of Statistics Greenland for 1977–2018 and combined with census data for 1970 and 1976 [10, 22, 23]. The data included 2,265,676 people annually from 1970 until 2018. We followed the RECORD applying for routinely collected data for reporting of the study [24] Since data are not publicly availably no specific coding of outcome has been provided in this paper. ## Method of suicide Based on the ICD-8 and ICD-10 codes method of suicide was categorized into four categories: poisoning; hanging, strangulation or suffocation; firearms or use of explosives and other. The category other includes methods that were less prevalent such as drowning, cutting or piecing instruments, jumping from high places, other and unspecified means, and late effects of self-inflicted injury. Method-specific suicide rates were calculated on hanging, strangulation or suffocation, firearms, or use of explosives and other. ## Stratification variables Sex was included to distinguish different rates in men and women. To display different risk at different ages across cohorts, ten-year birth cohorts were included from 1940 until 1999. To investigate geographical differences Greenland was divided into five regions based on socioeconomic conditions and remoteness from the capital (Table 1). Nuuk, the capital, is the largest and wealthiest community with the greatest variety of jobs, shops, and cultural institutions. The largest communities on the west coast, Maniitsoq and Sisimiut, are centrally located and in many ways intermediate between Nuuk and the more remote communities on the west coast, the latter offering fewer jobs, shops, and cultural institutions. The 50 smaller communities (often referred to as villages) on the west coast of Greenland offer few jobs, often only a single shop and rarely any cultural institutions. East Greenland is remote and suffers from a high unemployment rate and many social problems. In East Greenland there are 3,110 inhabitants living in two larger and five smaller communities. Smaller communities are generally defined as having less than 500 inhabitants [10].Table 1The five geographical regions used for stratification of suicide rates based on information from Statistics Greenland 2022 [10]RegionCommunitiesPopulation sizeNuukThe capital of Greenland18,800 inhabitantsCentral large communities onThe largest communities on the west coast, Maniitsoq and Sisimiut8,108 inhabitants in totalPeripheral large communities in West GreenlandLarger communities often having more than 500 inhabitants in East Greenland20,156 inhabitants in totalSmall communities in West GreenlandCommunities often having less than 500 inhabitants located in West Greenland6,179 inhabitants in totalEast GreenlandAll communities in East Greenland3,110 inhabitants in total ## Statistical analyses Analyses were performed using the statistical software Stata version 17. Rates were calculated by Poisson regression in Stata and by use of Excel [25, 26]. The observations were assumed to be independent. To make rates comparable internationally the analyses of the period trends were standardized to the average of five year periods according to the World Standard Population 2000–2025 [27]. Differences in age distributions across time were taken into account in the analyses of suicide rates according to sex and period where rates were age adjusted according to the average age distribution of the Greenland population in period 2015–2018. ## Results Table 2 shows the development of the suicide rate from 1970–2018. From 1970–1974 the rate was 29 per 100,000 people annually. Over a 15-year period the rate more than quadrupled peaking at 121 per 100,000 people annually in 1985–1989. The rate started declining from 1990 and in 2015–2018 the rate was 81 per 100,000 people annually. Among the 1,952 suicides registered from 1970–2018, 41 suicides were registered in children aged 11–14 years. Table 2Suicide and suicide rates from 1970 to 2018PeriodSuicidesSuicides/yearPopulation (risk time)Suicide rate per 100,000 people annuallyPeriod specific percent changeStandardized suicide rates per 100,000 people annually a1970–19745611.219540228.7-33.31975–19799519.020242746.963.847.81980–198421042.0210082100.0113.088.81985–198926853.6222348120.520.699.71990–199424048.0236118101.6-15.796.91995–199925150.2245000102.40.8106.12000–200423647.224912394.7-7.5101.92005–200921142.225199083.7-11.684.62010–201422244.425174488.25.386.72015–201816332.620046881.3-7.880.4Total195240.72,26470086.2-86.2aStandardized according to World Standard Population 2000–2025 The highest rate was seen in young men aged 20–24 years with 387 suicides per 100,000 people annually followed by a decline with increasing age. Among women, young women aged 15–19 years had the highest rate of 96 suicides per 100,000 person years (Fig. 1).Fig. 1Age specific suicide rates by sex. Analysis of mortality data from 1970 to 2018 The rates for both men and women increased until 1985–1989 whereafter the male rates declined while the female rates increased slowly until 2018 thus reducing the gender gap (Fig. 2). Figure 3 shows that birth cohorts born after 1960 had the highest suicide rates among the youngest age groups and that youth suicide rates continued to increase with later birth cohorts. The pattern was similar in the youngest birth cohort born in 1990–1999 but at a lower level compared to previous cohorts born between 1960 and 1989. Regional temporal trends are shown in Fig. 4. East Greenland experienced the highest rate of around 300 suicides per 100,000 people annually in 1995–1999. In the capital Nuuk, the rate was at its highest in the 1980s followed by a large decrease further increasing the differences in rates between West and East Greenland. Fig. 2Period specific crude and age adjusted suicide rates by sex. Age adjusted rates were standardized according to the age distribution of 2015–2018. Analysis of mortality data from 1970 to 2018Fig. 3Age specific suicide rates by 10-year birth cohorts. Analysis of mortality data from 1970 to 2018Fig. 4Period specific suicide rates by community size and geographical location. Analysis of mortality data from 1970 to 2018 Figure 5 show that men mainly used violent methods for suicide with shooting or use of explosives as the most common methods in 1970–1979 ($70\%$). Across time, suicide by hanging has become the most prevalent method in both sexes accounting for $73\%$ of suicides in men and $76\%$ in women from 2010–2018. Women had a high proportion of non-violent suicides (poisoning) at the beginning of the study period whereas the more violent methods, predominantly hanging, strangulation or suffocation became more prevalent during the study period increasing by $30\%$.Fig. 5Method of suicide according to year in men and women. The category other includes drowning, cutting or piecing instruments, jumping from high places, other and unspecified means, and late effects of self-inflicted injury. Analysis of mortality data from 1970 to 2018 The method-specific suicide rate for hanging for both sexes increased from 7.3 per 100,000 people annually in the 1970s to 63 during 2010 to 2018. For shooting as method, the rate decreased from a peak of 49.7 per 100,000 people annually in the 1980s to 14.6 during 2010 to 2018. The rate of poisoning was 3.8 per 100,000 persons annually in the 1970s and after an increase to 5.1 in the 1980s it declined to 3.1 in the period from 2010 to 2018 (Table 3).Table 3Method-specific suicide rates. Analysis of mortality data from 1970 to 2018PeriodOtherShooting or use of explosivesHanging, strangulation or suffocationPoisoning1970–19793.023.97.33.81980–19899.949.745.85.11990–19996.932.660.22.32000–20095.222.758.23.02010–20184.414.663.03.1 ## Discussion The suicide rate in Greenland from 1970 until 2018 differed markedly according to age, birth cohort, period, sex, and place of residence. The suicide rate has been declining since a peak in the 1980s but remains high today as is the case among many other Indigenous populations compared to non-Indigenous populations [2]. Descriptive analyses point to the decrease in male suicides as the primary factor for the overall decreasing rates but the tendency of increasing rates in women is worrying. Simultaneously, the proportion of women who used a violent suicide method increased from $60\%$ in 1970–1979 to $90\%$ in 2010–2018 which in part explains the increase in the suicide rate among women. Overall hanging as a suicide specific method increased across the study period. This is the first study to identify a reducing gender gap in suicide rates among Greenlandic Inuit. As is the case across many Arctic Indigenous peoples the young men have had and continue to have the highest rates [6, 28, 29]. However, the authors have no clear explanation of why rates are decreasing in men and increasing in women. ## The epidemiology of suicide in Greenland The findings of this paper are in line with previous epidemiological studies of suicides in Greenland. A systematic review that investigated epidemiological factors of suicide among young men in Greenland found the same increase in suicide rates as were shown in the analyses of period rates in the current study [30]. The results on suicide rates according to age and sex are in accordance with previous findings where youth aged 15 to 24 years have the highest rates across all age groups while young men have the highest rates overall [6, 31]. The same sex-related patterns are found among Inuit in Canada and Alaska and similar trends of a decrease in the rates have been shown [3]. ## Suicide methods The documented high prevalence of hanging as suicide method is consistent with findings in a European context, where a high prevalence of hanging as suicide method has been found in both men ($54.3\%$) and women ($35.6\%$) [32]. Japan also struggle with high rates of youth suicides and the same pattern appears with hanging as the most prevalent method among adolescents, a prevalence that has increased since the 90 s in both sexes [33]. ## Adverse childhood experiences Research has pointed to the association of adverse childhood experiences (ACEs) and suicidality in Arctic Regions [28, 29, 34]. In Greenland ACEs have been linked to both suicides and suicidal thoughts [5–7, 35–38]. Experiencing alcohol problems often in the childhood home and sexual abuse was associated with up to four times higher odds of past year suicidal thoughts according to data from the population health surveys in Greenland [5, 17]. The latest survey data among youth aged 15–34 years reported that $41\%$ of the young women were victims of sexual abuse before the age of 18, whereas the corresponding proportion was $12\%$ in young men. The same survey found a cumulative effect between the number of experienced ACEs and prevalence of suicidal thoughts and attempts [39]. The high prevalence and the type of ACEs in Greenland may contribute to the pathogenesis of suicides: The high proportion of young women exposed to sexual abuse could be a contributing factor of the detected increase in female suicide rates. However, the population health surveys have shown a decrease in ACEs in the youngest birth cohorts compared to birth cohorts born in 1970–1989 where the proportion who experienced ACEs was very high [17]. The latter grew up during the period where the alcohol consumption was at its highest, which is paralleled in the high suicide rates of the birth cohorts. According to the figure, youth suicide began to increase in the generations born during the 1950s and more than quadrupled in the following 10-year birth cohorts after which youth suicide became the most prevalent across all age groups. Based on these observations there is a potential link between alcohol consumption, ACEs, and youth suicide. ## Intergenerational trauma The trends in suicide rates of birth cohorts are also seen in other Arctic Indigenous peoples and has been interpreted as a sign of cultural cohort effects caused by cultural discontinuities related to colonialization and rapid societal changes [34, 40]. Problems with alcohol and ACEs does not only severely affect the individuals exposed to them but may have cumulative and long lasting consequences across generations [41]. Often these are referred to as intergenerational trauma. In the case of many Indigenous Peoples’, colonization of Indigenous land and lives, and rapid societal developments caused the onset of trauma and consequently intergenerational trauma [5, 7, 8, 42]. Intergenerational traumas are mirrored in the prevalence of both alcohol-related problems and ACEs. The prevalence has shifted across time and so has the impact on the generations exposed. Connection to culture has been identified as an important factor fostering resilience and improved mental health among those affected by intergenerational trauma [43–46]. The association between culture and resilience differs across generations: Older generations feel more connected to their cultural heritage enhancing the protective effect, whereas younger generations experience a more distorted cultural connection making them more vulnerable to the ongoing rapid societal transitions compared to the older generations [43]. ## Geographical patterns of suicide Whereas the rate in Nuuk started to decrease during the 1980s the rate in East Greenland continued to increase and stabilized at a very high level of three times the national rate. East Greenland is the most remote populated area of Greenland with less access to education, social- and health services, welfare, employment, and cultural institutions. A study on suicide in Greenland identified lack of access to education and employment as risk factors for suicide offering part of an explanation of the regional disparities in suicide rates [38]. Growing up in either a smaller community in West Greenland or in East Greenland is associated with a smaller likelihood of obtaining longer education which in turn affects the individual opportunities to fit into a modern society built on Western ideals [40, 47]. Living in smaller communities in West, East or North Greenland is further associated with lower income compared to the income of people living in the capital. The high suicide rate in East Greenland could further relate to the high prevalence of social problems and ACEs. In 2014 East Greenland had the highest prevalence of sexual abuse ($46\%$), while violence and problems with alcohol consumption was most prevalent in Nuuk and other larger communities [47]. The Greenland Police releases a yearly report every year with statistics on ‘sexual relations with children under the age of 15’, and here East Greenland and larger peripheral communities have the highest numbers [48]. Survey data from 2018 among the Greenlandic youth aged 15–34 years support this clear geographical clustering of ACEs in the Eastern, Northern, and Southern municipalities. Nuuk (Sermersooq West) have the lowest prevalence but overall prevalence of alcohol in childhood home, violence in childhood home, and experience of sexual abuse before the age of 18 are high [39]. The geographical clustering of high suicide rates and related risk factors point to the need for different approaches in the preventive effort of suicide. ## Strengths and limitations The included registries are regarded as complete and are updated annually. Using nationwide registers reduces the risk of selection bias while there are potential problems with information bias [49]. Problems with selection may occur due to migration, which is rather common in Greenland both with-in Greenland and often to Denmark where many people chose to settle down permanently or periodically as part of their educational path [1]. Determining whether this selection inherits bias will require more in-depth investigation of who migrates and their risk of suicide. Potential problems with misclassification of suicides as accidents and vice versa cannot be ruled out [21]. Since there are no knowledge on the direction or size of such it is not possible to determine the effect on the results of the study. ## Implications for prevention and health promotion Temporal trends in the suicide rates are paralleled by trends in ACEs and can be linked to rapid societal developments in the post-colonial area [17, 50]. Research have found an association between ACEs and suicide attempts and ACEs are often reported as part of the background in suicide victims in Greenland [35–38, 51]. Based on this the prevention of ACEs would be an important factor in the preventive effort to reduce the number of suicides in Greenland. The regional differences in the suicide rate and prevalence of ACEs should be taken into consideration in the preventive efforts. Relevant preventive efforts and interventions to reduce ACEs should be focused on the capacity in the community. Community capacity cover empowerment of communities to provide healthy environments and to prevent and handle crises [52]. The findings of this paper underline the need to focus on suicide prevention among youth. Research have shown that there is a particular need to include a broad focus of not only the individual but also their surrounding community and relations [46]. Culture and relations have been found to enhance mental health by providing knowledge and connection to the culture, intergenerational relationships, and role models [45]. Suicide prevention interventions among Indigenous youth incorporating cultural aspects have been found to enhance the level of mental health [53, 54]. Arctic research is developing a more holistic strength-based approach to suicide prevention focusing on community strengths and protective factors fostering thriving communities [44, 45, 55–57]. Prevention should be culturally relevant, community-based, and include individual, family, social, historical, cultural, and environmental factors that constitute the base of the ‘iceberg’ of which suicide would figure as part of the visible tip above water [58]. In Greenland, land-based and intergenerational interventions for mental health among youth are being conducted and monitored. The Greenlandic Government is currently working on a new suicide prevention strategy expected to launch in 2023. The strategy includes perspectives from youth collected by the National Advocacy Center working for Childrens Rights, MIO [59]. ## Conclusion Across a 50-year period the suicide rate has increased drastically in Greenland mirroring a rapid societal transition in the post-colonial period. The rate has slowly declined from the peak in the 1980s but remains at a very high level. Young people, and especially young men, are at risk but the steady increase in the rate in women is worrying. This can in part be explained by the fact that there has been an increase in the proportion of violent suicide methods among women increasing the lethality. Research points to the importance of ACEs in relation to suicide risk and the high prevalences of ACEs among women may contribute to the decreasing gender gap but there is a need to investigate this further. Youth suicide increased with later birth cohorts born after 1949. The youngest birth cohort still exhibited the pattern of high youth suicide rates but at a lower level compared to older generations. These findings coincide with a decrease in ACEs and urbanization which authors believe is reflected in the rates overall and the development of geographical clustering of the highest rates. These factors must be taken into consideration in the preventive work along with a strong focus on the community and protective factors that increases intergenerational bonds and cultural connectedness. Factors which may heal intergenerational trauma and enhance a feeling of cultural identity creating a strong foundation for youth and foster thriving communities. ## Supplementary Information Additional file 1. ## Authors’ information The main author, Ivalu Katajavaara Seidler, is of Greenland Inuit heritage and has been working with Greenlandic health research since 2018. More specifically her research topics include youth, mental health and alcohol consumption. Ivalu has a master’s degree in Public Health Science and is part of the Centre for Public Health in Greenland. The study in question is the first part of her Ph.D. project entitled ‘Patterns for suicides in Greenland – Risk and protective factors with a particular focus on Adverse Childhood Experiences’. Janne Tolstrup is a professor of epidemiology and is working in the field of public health especially focusing on the significance of alcohol intake and alcohol problems for somatic and psychiatric diseases. The preferred choice of methodology is to use a combination of data from large population-based studies and national registers. She is also running several intervention studies and is the author of more than 140 scientific papers. Peter Bjerregaard is a Professor of Arctic Health and has authored of more than 330 articles in peer reviewed journals, monographs, books, book chapters and research reports, predominantly on circumpolar epidemiology and community health. He is part of the Centre for Public Health in Greenland. Dr. Allisson Crawford is a psychiatrist and Associate Professor in the Department of Psychiatry, University of Toronto, where she is the Medical Director of the Northern Psychiatric Outreach Program and Telepsychiatry at the Centre for Addiction and Mental Health. 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--- title: PHQ-9, CES-D, health insurance data—who is identified with depression? A Population-based study in persons with diabetes authors: - Ute Linnenkamp - Veronika Gontscharuk - Katherine Ogurtsova - Manuela Brüne - Nadezda Chernyak - Tatjana Kvitkina - Werner Arend - Imke Schmitz-Losem - Johannes Kruse - Norbert Hermanns - Bernd Kulzer - Silvia M. A. A. Evers - Mickaël Hiligsmann - Barbara Hoffmann - Andrea Icks - Silke Andrich journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC10031874 doi: 10.1186/s13098-023-01028-7 license: CC BY 4.0 --- # PHQ-9, CES-D, health insurance data—who is identified with depression? A Population-based study in persons with diabetes ## Abstract ### Aims Several instruments are used to identify depression among patients with diabetes and have been compared for their test criteria, but, not for the overlaps and differences, for example, in the sociodemographic and clinical characteristics of the individuals identified with different instruments. ### Methods We conducted a cross-sectional survey among a random sample of a statutory health insurance (SHI) ($$n = 1$$,579) with diabetes and linked it with longitudinal SHI data. Depression symptoms were identified using either the Centre for Epidemiological Studies Depression (CES-D) scale or the Patient Health Questionnaire-9 (PHQ-9), and a depressive disorder was identified with a diagnosis in SHI data, resulting in 8 possible groups. Groups were compared using a multinomial logistic model. ### Results In total 33·$0\%$ of our analysis sample were identified with depression by at least one method. 5·$0\%$ were identified with depression by all methods. Multinomial logistic analysis showed that identification through SHI data only compared to the group with no depression was associated with gender (women). Identification through at least SHI data was associated with taking antidepressants and previous depression. Health related quality of life, especially the mental summary score was associated with depression but not when identified through SHI data only. ### Conclusion The methods overlapped less than expected. We did not find a clear pattern between methods used and characteristics of individuals identified. However, we found first indications that the choice of method is related to specific underlying characteristics in the identified population. These findings need to be confirmed by further studies with larger study samples. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13098-023-01028-7. The online version contains supplementary material available at 10.1186/s13098-023-01028-7. ## Key points Patients with diabetes often have comorbid depression. Those patients are struggling to meet their treatment goals. Thus, they have a higher risk of getting diabetes related complications as for example coronary heart diseases. A lot of different tools and instruments are available to diagnose depression, to screen for depression among patients with diabetes or to identify depression symptoms or depressive disorder in clinical or epidemiological studies, including interview, questionnaires or claims data. It would be helpful to know if the tools that are used identify the same people or, if this is not the case, whether people identified by different tools have different characteristics or health outcomes. We found that different methods do not identify the same people with depression. There was no clear pattern of differences between the identified groups, however, we found some initial indications that the method chosen is related to particular underlying characteristics in the population identified. Further research with larger data sets is necessary to see if there are differences among the persons that are identified by different tools to give recommendations which screening tool to use for what purpose. ## Introduction Patients with diabetes have an increased prevalence of depression compared to the general population [1]. Although it remains controversial if diabetes leads to depression or vice versa or if there is a bidirectional association, there is sufficient evidence that depression can have a serious impact on a person’s wellbeing and their ability to self-manage their diabetes [2–5]. Individuals with diabetes and comorbid depression are found to have unfavorable diabetes related outcomes such as a reduced adherence to their diabetes treatment, higher HbA1c levels, increased diabetes symptoms, or unfavorable micro-, and macrovascular outcomes [2–6]. Beyond unfavorable health outcomes, Brüne et al. [ 2021] found that people with diabetes and depression had almost two times higher total health care cost compared to people with diabetes without depression [7]. Despite the relevance of comorbid depression in people with diabetes, it is assumed that only $50\%$ are recognized and an even smaller amount is appropriately treated [2]. Several methods are used to identify depression or to estimate the prevalence of it. Prevalence estimates of depression among people with diabetes differ, which is also due to the fact that a range of different methods are used to assess depression [1, 8]. Three systematic reviews found, that in studies where a questionnaire was used to assess depression, the prevalence was about two to three times higher than in those that used a diagnostic interview [8–10]. The method used to assess the presence of depression depends on several factors. For example, it may depend on study design, time constraints, personal preferences of the researchers, availability or the aim of the assessment. Furthermore, there are a variety of questionnaires, each with a different objective and somewhat different background or focus [11–14]. Knowledge of the different methods and instruments to assess depression is therefore important. Up to now, there are a number of studies available that validate these questionnaires in general [15, 16]. Very few studies have compared the different instruments for identifying depression among patients with diabetes. These studies either intended to validate a certain instrument against another in a specific population or wanted to compare psychometric properties or internal reliability [17–20]. A method other than questionnaires is the use of diagnosis in statutory health insurance (SHI) data to identify persons with depressive disorder. Up to now, there is no study, in which SHI data was used for comparison purposes. In our study, we used two of the most common instruments in addition to SHI data to investigate whether the different methods identify - more or less - the same individuals or whether they identify different individuals. In particular - if the identified individuals differ - we were interested in possible patterns of characteristics of the identified groups. Thus, in contrast to existing validation studies, the aim of this study was to assess and describe in detail the overlap and the differences between groups identified by different methods to find persons with depression (symptoms or disorders), as well as potential associations between individual and clinical characteristics and the method used to identify a person. Specifically, three methods to identify depression were used and compared: the Centre for Epidemiological Studies Depression (CES-D) scale, the Patient Health Questionnaire-9 (PHQ-9) - the two most frequently evaluated questionnaires among people with diabetes [20] - or a diagnosis in SHI data. In this way, we aimed to gain basic insights and better understand the issues associated with the use of different methods. ## Study design The study design and recruitment of participants have been described elsewhere [21]. In brief, a cross-sectional survey was conducted in a random sample of individuals with diabetes ($$n = 4$$,053) insured by one SHI covering 673,366 persons in Germany. Individuals with diabetes type 1 or 2 were identified using an algorithm taking into account diagnosis based on the 10th International Classification of Diseases (ICD-10) for ‘diabetes’ (E10–E14), prescription of antihyperglycemic drugs (Anatomical-Therapeutic-Chemical [ATC] classification A10), and documentation of blood glucose, or a HbA1c measurements. This algorithm has been validated and used in previous studies [22]. We linked data of the survey to longitudinal SHI data on an individual level. The initial aim of the study was to assess differences in people with diabetes and with and without depression regarding costs and health related quality of life. The presented analyses are secondary analyses that were developed in the course of the study. ## Data source The baseline survey was a 9-page postal questionnaire conducted in 2013. It assessed information on sociodemographic characteristics such as age, sex, and years of education, duration of diabetes, and type of diabetes. PHQ-9 and the German version of the CES-D were used to assess depression symptoms. SHI data on health care utilization patterns and health care costs for all in- and outpatient treatments were available for the period covering four quarters before and after the quarter of the baseline survey. ## Study population Of 46,566 individuals with diabetes in the SHI 3,642 persons were randomly selected and contacted to participate in the study. In total 1,860 persons sent back their questionnaire (response rate: $51\%$) and gave written informed consent to use their SHI data. Responders did not differ from the non-responders in having a history of depression diagnosis [23]. For 201 of these persons, a lack of data over the complete observation period existed, e.g. because the person switched health insurance during that time. In total 1,659 persons were considered for the analysis. Further 80 persons were excluded as they provided incomplete information in the questionnaire. Thus, a total of 1,579 persons were included in our analysis (Appendix Fig. 1). Ethical approval was obtained from the ethics committee of the Heinrich Heine University Düsseldorf and is available under the study reference 3762. ## CES-D The CES-D and the German version of it (Allgemeine Depressionsskala) are brief self-report measures, designed to assess symptoms of depression in the general population in epidemiological studies among nine signs and symptoms of depression defined by the American Psychiatric Association Diagnostic and Statistical Manual, fourth edition [11]. Several studies have assessed the validity of the CES-D in different populations [24, 25]. We used the short form of the German version of the CES-D in our study (allgemeine Depressionsskala Kurzform (ADS-K)) [25]. The instrument comprises 15 statements regarding depression. Based on a four-point scale (ranging from “rarely or never” (0 point) to “frequently, all the time” (3 points)), the frequency of depressive symptoms occurring during the last week can be assessed. A score that can range from 0 to 45 is built by adding up the points from each statement. We used a cut-off value of ≥ 17 to define clinically meaningful depressive symptoms as suggested by validation studies [25]. ## PHQ-9 The PHQ-9 is a multipurpose instrument used to screen, monitor and measure the severity of depression symptoms. The PHQ-9 can be assessed using different methods: as a diagnostic algorithm to make a probable diagnosis of major depressive disorder using the nine criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or to test for other depressive disorders and a cut-off based on summed-item scores to assess the severity of depression symptoms [12]. The algorithm is the scoring method that was originally proposed to screen for depression. Within this study we focused on the PHQ-9 as a screening instrument. According to Kroenke et al. [ 2001] we defined depression when two or more of the nine symptoms were present at least “more than half the days” in the past two weeks, and one of the symptoms was depressed mood or anhedonia. If the thought of suicide was present, it is considered to be present, regardless of the reported duration [12]. Several studies have used the PHQ-9 to assess depression among individuals with diabetes and used a similar approach [2, 26]. ## Depression in SHI data For a diagnosis in SHI data a ICD-10 code for the diagnosis of unipolar depression during the study period of nine quarters was required. Diagnosis of unipolar depression included the following codes: F32.0-F32.9 Depressive episode, F33.0-F33.9 Recurrent depressive disorder, F34.1 Dysthymia, F38.1. Other recurrent mood [affective] disorders and. F41.2 Mixed anxiety and depressive disorder. ## Group composition based on depression measurement We classified the participants into eight groups after linking SHI data with survey data. Group 1 reported depression symptoms in the CES-D and PHQ-9 and had a diagnosis in SHI data. Group 2 reported depression symptoms in the CES-D and PHQ-9 but had no diagnosis in SHI data. Group 3 had symptoms according to the PHQ-9 but not according to the CES-D and had a diagnosis in SHI data. For group 4 no symptoms were reported with the PHQ-9 but with the CES-D and they had a diagnosis in SHI data. Group 5 was only identified with the CES-D, Group 6 only with the PHQ-9 and group 7 only with a diagnosis in SHI data. Group 8 had no depression symptoms or diagnosis and was considered as a reference group (Appendix Table 1). ## Possible associated variables and covariates All potentially associated variables and covariates considered as potential predictors were recorded during the baseline survey, except information on clinical and disease related measures (based on SHI data). Based on a literature review and clinical expertise, we considered socio-demographic variables, patient-reported measures on health-related quality of life (HRQoL) and diabetes related distress as well as clinical and disease-specific variables. The following variables were included as sociodemographic factors: age, gender, marital status (married, single, divorced or separated, widowed), relationship status (with/without partner), origin (resident in Germany since birth/not residing in Germany since birth) as well as employment (yes/no), and retirement status (yes/no). The International Standard Classification of Education (ISCED) was used to categorize participants according to the duration of their education (< 10 years, 10–14 years, > 14 years) [27]. Furthermore, type and duration of diabetes were also assessed in the baseline survey as well as information on a previous diagnosis of depression by a health professional. HRQoL was investigated using the 12-item Short Form health survey (SF-12), a multipurpose generic measure of health status [28]. The SF-12 can be used to compose a physical health and a mental health summary score (PCS-12 and MCS-12). We also assessed diabetes-specific distress using the Problem Areas in Diabetes Scale (PAID), a 20-item scale consisting of emotional problems commonly reported among patients with diabetes [19]. SHI data was used to assess clinical and disease related measures. Comorbidities were measured using diagnostic groupings, which are necessary for the morbidity-oriented risk structure adjustment by SHIs in Germany. We used the number of coded morbidity groups in the year prior to the baseline survey [2012] to assess the number of comorbidities [29]. Healthcare costs were calculated from the perspective of a SHI including all costs imposed to the SHI. We took net costs into consideration without taking discounts into account. Costs were analyzed for every person individually, covering the survey quarter plus the four quarters before and after, a total of nine quarters. The adapted Diabetes Complications Severity Index (aDCSI) was used to assess diabetes complications thereon to determine diabetes severity [30]. Treatment of diabetes was assessed by looking for prescription of insulin or oral antihyperglycemic drugs (OADs) in the SHI data for each participant during the course of the study. Additional it was checked whether persons took antidepressants during the course of the study. These were defined by the ATC Code N06A. ## Statistical analyses We described the study population by using mean, standard deviation and median for quantitative variables as well as frequency and percentage for categorical variables. We used the Mann–Whitney U test for comparison of quantitative variables in two groups and Kruskal-Wallis test for three and more groups. Pearson’s chi-square test was conducted to assess if differences for categorical variables were significant. P-values related to the aforementioned tests show the probability to observe the actual value of the related test statistic or even more extreme values of it assuming the null hypothesis that there are no differences between groups. Smaller p-values indicate against the null hypothesis. To compare the eight groups, we handled the missing data (cf. description of the study population and Table 1) with the machine learning based R-algorithm missForest to impute. To assess the quality of the imputation we calculated out-of-bag (OOB) imputation errors as the proportion of false classified cases (PFC) for categorical and as normalized root of mean squared error (NRMSE) for quantitative variables. Since the comparison of all eight groups to each other (the so called many-to-many problem) requires 28 pairwise comparisons, each with respect to a variety of characteristics, one should expect a considerable number of false rejections/effects. In order to be able on the one hand to control the type I error (i.e., rejection of at least one true null hypothesis, also known as family-wise error rate) and on the other hand to see any effects after multiple adjustment (done by the Bonferroni correction), we focused on the comparison of seven groups with depressive disorder to the group with no depression or depressive symptoms (i.e., group 8) as the reference group (the so called many-to-one problem). We used a multinomial logistic regression to model the group membership, whereby the log odds of being in one group relative to being in the reference group is modelled as a linear combination of predictor variables. Thus, an indirect comparison of seven groups with depressive disorder to each other may be done by comparing those differences to the reference group. Gender, age, marital status, employment status, type of diabetes and diabetes duration, insulin and OAD usage, aDCSI score, previous depression and intake of antidepressant medication, number of comorbidities, HRQoL, PAID score, and total health care costs were used as potential candidates for independent variables in the multinomial model. We selected the finale multinomial model by keeping important variables (age, sex, comorbidities, MCS-12 and PCS-12), removing collinear variables as well as minimizing Akaike information criterion (AIC). The final model includes the independent variables: age, gender, taking insulin, previous depression, taking antidepressant, the number of comorbidities, HRQoL, and the PAID score. P-values related to the estimates of the multinomial regression odds ratios (OR) for being in a group with depressive disorder compared to the reference group, are the probabilities to observe the actual value of the OR or more extreme values and under the null hypothesis that there is no effect (OR = 1). Smaller p-values are an indication, that null hypothesis may be wrong and there is an effect. The significance level (also for multiple comparisons) was set to α = 0·05. ## Description of the study population Table 1 describes the 1,579 participants and their characteristics. For 271 subjects in the total sample (17·$2\%$) data of at least one variable in the baseline survey were missing while 1,308 persons had complete data. Participants had a mean age of 67 years and almost $40\%$ were female. About $90\%$ were German and $84\%$ were in a relationship. About one in five had more than 14 years of education. Almost $70\%$ of the participants were retired. More than $75\%$ were married, around $7\%$ were divorced or separated and 12·$4\%$ were widowed. On average participants had diabetes for 11 years, the majority had T2DM (85·$9\%$). About one-third of the participants were treated with insulin, around $67\%$ took OAD. 17·$5\%$ took antidepressants. The mean healthcare costs in our sample were 10,123€. Participants had on average 41·7 points on the physical component summary scale (PCS) of the SF-12 and 50·1 on the mental component summary scale (MCS). The average PAID Score in the sample was 19·4. $14\%$ of people in the sample reported that they had previously been diagnosed with depression. Table 1Baseline characteristics of the DiaDec samplen (%), mean ± SD, medianSample size, n1,579Age, years, $$n = 157967$$·0 ± 9·9,69·0Sex, female, $$n = 1579597$$ (37·8)Origin, Germany $$n = 15771$$,397 (88·6)Family status, in a relationship, $$n = 15561$$,306 (83·9) Marital status, $$n = 1573$$ Married1,188 (75·5) Divorce/separated112 (7·1) Widowed195 (12·4)Employment status, employed, $$n = 1547402$$ (26·0)Retirement status, retired, $$n = 15641$$,089 (69·6)Level of education, ISCED ≥ 14 years, $$n = 1570337$$ (21·5)Diabetes duration in years, $$n = 153311$$·0 ± 8·3,9·0 Type of Diabetes, $$n = 1566$$ Type 1 Diabetes128 (8·2) Type 2 Diabetes1,345 (85·9) Type unknown/other93 (5·9)Diabetes severity aDCSI, $$n = 15793$$·0 ± 2·2,3·0Number of comorbidities, $$n = 15793$$·7 ± 2·1,3·0 Treatment, $$n = 1579$$ Taking insulin486 (30·8) Taking oral antihyperglycemic drugs1,071 (67·8) Taking antidepressants276 (17·5)Health care costs for 2 years, €, $$n = 157910$$,123·0 ± 13,188·2,6,112·7Health related Quality of Life$, $$n = 1544$$ physical component summary scale of the SF-12 (PCS-12)41·7 ± 10·9,43·5 mental component summary scale of the SF-12 (MCS-12)50·1 ± 10·5,53·3Problem Areas in Diabetes Scale (PAID)∞, $$n = 151219$$·4 ± 17·6,14.0 Previous depression, n (%) (self-reported), $$n = 1575$$ Yes225 (14·3) No1,041 (66·1) unknown309 (19·6)Percentages of categorical variables computed with respect to the total number of subjects within the sampleSD = standard deviation$ range from 0 to 100, zero indicates the lowest level of health measured by the scales and 100 indicates the highest level of health∞ Possible score can range from 0 to 100, with higher scores indicating greater diabetes-related emotional distress ## Prevalence of depression according to the different methods Figure 1 displays overlaps between the different methods and reports the overall prevalence within the sample. In total 33·$0\%$ of our analysis sample [521] were identified with some form of depression by at least one method. The prevalence of depression in our sample ranged from 11·$6\%$ (PHQ-9) up to 22·$4\%$ (SHI data). Fig. 1Venn diagram showing the persons identified by different methods to assess depressive disorder and intersections between the different methods The different groups and their characteristics are described in Table 2. Group 8 – the reference group - was the largest group with 1,058 persons and group 3 identified through the PHQ-9 and a diagnosis in SHI data the smallest with 22 persons. With respect to sociodemographic variables the percentage of females was highest in group 7 (51·$7\%$) while it was lowest in group 6 (33·$3\%$). Group 7 (only identified by a diagnosis in SHI data) was the group with most persons being German of origin (92·$7\%$) and group 2 (identified by both instruments) the one with the smallest number of persons with German origin (76·$1\%$). In group 8 most people were in a relationship (87·$7\%$) and group 1 (identified by all methods) was the group where the smallest number of persons was in a relationship (70·$1\%$). One third of group 6 (identified through PHQ-9 only) were retired but only about $52\%$ of the persons in group 1. A duration of education for more than 14 years was highest in the group 8 (23·$2\%$) and in group 1 (20·$3\%$) and lowest in group 2 (15·$2\%$). Group 1 had also the highest share of persons with type 1 diabetes (12·$6\%$). With regard to diabetes specific and health care related outcomes, the highest number of persons with type 1 diabetes was found in group 1(12·$8\%$) and the lowest amount was found in group 7 (4·$9\%$). Group 2 and 3 had the highest share of persons taking insulin (43·5 and $50\%$) whereas in all other groups the share was around $30\%$. For OAD in all groups the share of persons taking them was between 60 and $70\%$. Average health care costs were highest in group 3 with a median of more than 13,900 € and lowest in group 8 (median 5,283 €). Table 2Comparison of groups according to depression statusGroup 1n(%) / mean ± SD, medianGroup 2n(%) / mean ± SD, medianGroup 3n(%) / mean ± SD, medianGroup 4n(%) / mean ± SD, medianGroup 5n(%) / mean ± SD, medianGroup 6n(%) / mean ± SD, medianGroup 7n(%) / mean ± SD, medianGroup 8n(%) / mean ± SD, medianp-value P + C + S+ P + C + S-P + C-S+P-C + S+P-C + S-P + C-S-P-C-S+P-C-S-Sample size, n7946224885362051,058Age, years61·3 ± 10·8,62·064·9 ± 10·9,65·062·4 ± 11·6,61·063·7 ± 10·1,65·066·5 ± 9·3,68·069·7 ± 9·1,72·567·2 ± 9·9,69·067·7 ± 9·7,70·0 < 0·001 Sex, female38 (48·1)20 (43·5)8 (36·4)24 (50·0)33 (38·8)12 (33·3)106 (51·7)356 (33·7) < 0·001 Origin, Germany63 (79·8)35 (76·1)19 (86·4)37 (77·1)73 (85·9)28 (77·8)190 (92·7)952 (90·2) 0·001 Family status, in a relationship54 (70·1)35 (79·6)17 (77·3)38 (79·2)62 (73·8)29 (82·9)155 (77·1)916 (87·7) < 0·001 Marital status Married48 (60·8)28 (60·9)15 (68·2)31 (64·6)58 (69·1)28 (77·8)148 (72·$6\%$)832 (78·$9\%$) < 0·001 Divorce/separated16 (20·3)7 (15·2)3 (13·6)3 (6·3)8 (9·5)1 (2·8)16 (7·8)58 (5·5) Widowed10 (12·7)10 (21·7)2 (9·1)9 (18·8)15 (17·9)5 (13·9)32 (15·7)112 (10·6)Employment status, employed20 (26·0)16 (35·6)5 (23·8)16 (34·8)25 (30·1)8 (22·2)44 (21·8)268 (25·8)0·44Retirement status, retired41 (51·9)31 (67·4)11 (50·0)26 (54·2)51 (61·5)27 (75·0)142 (69·6)760 (72·7) < 0·001 Level of education, ISCED ≥ 14 years16 (20·3)7 (15·2)5 (22·7)11 (22·9)14 (16·7)6 (16·7)34 (16·7)244 (23·2) 0·05 Diabetes duration, years11·4 ± 8·98·511·8 ± 7·61014·0 ± 10·41012·7 ± 9·21011·5 ± 8·01011·2 ± 7·11010·7 ± 8·88·510·9 ± 8·190·88 Type of Diabetes Type 1 Diabetes10 (12·8)3 (6·7)3 (13·6)6 (12·8)8 (9·5)2 (5·7)10 (4·9)86 (8·2)0·07 Type 2 Diabetes66 (84·6)39 (86·7)17 (77·3)38 (80·9)70 (83·3)31 (88·6)171 (83·8)913 (86·9) Type unknown/other2 (2·6)3 (6·7)2 (9·1)3 (6·4)6 (7·1)2 (5·7)23 (11·3)52 (5·0)Diabetes severity aDCSI2·9 ± 2·0,3·03·3 ± 2·3,3·03·2 ± 2·8,2·53·6 ± 2·1,3·03·0 ± 2·3,2·04·1 ± 2·3,4·03·6 ± 2·4,3·02·9 ± 2·1,3·0 < 0·001 Number of comorbidities4·3 ± 2·0,4·03·6 ± 1·63·04·6 ± 2·74·05·6 ± 2·8,5·03·4 ± 1·9,3·04·4 ± 2·5,4·04·6 ± 2·5,4·03·3 ± 1·9,3·0 < 0·001 Treatment Taking insulin27 (34·2)20 (43·5)11 (50·0)17 (35·4)29 (34·1)12 (33·3)57 (27·8)313 (29·6)0·17 Taking oral antihyperglycemic drugs50 (63·3)31 (67·4)15 (68·2)29 (60·4)53 (62·4)25 (69·4)139 (67·$8\%$)729 (68·9)0·81 Taking antidepressants57 (72·2)16 (34·8)15 (68·2)29 (60·4)9 (10·$6\%$)4 (11·1)89 (43·4)57 (5·4) < 0·001 Health care costs in € for 2 years12,021·8 ± 9,631·2,8,941·114,755·5 ± 22,196·0,7,624·621,071·6 ± 23,499·2,13,930·016,133·5 ± 16,576·6,9,418·88,841·8 ± 8,848·2,6,952·512,869·1 ± 12,829·9,7,255·613,598·3 ± 15,942·2,7,414·78,617·3 ± 11,730·2,5,283·50·48 Health related Quality of Life physical component summary scale of the SF-12 (PCS-12)$32·4 ± 9·3,30·628·6 ± 7·6,26·731·8 ± 10·2,30·333·2 ± 9·3,34·135·2 ± 9·6,35·531·1 ± 7·8,32·340·6 ± 10·5,42·344·5 ± 9·8,47·3 < 0·001 mental component summary scale of the SF-12 (MCS-12) $30·6 ± 7·8,28·533·5 ± 6·8,32·137·3 ± 9·1,37·839·1 ± 7·7,37·939·8 ± 7·6,38·142·3 ± 7·3,43·149·8 ± 10·252·254·1 ± 7·3,56·0 < 0·001 Problem Areas in Diabetes Scale (PAID)∞45·0 ± 22·5,47·048·9 ± 19·8,51·024·7 ± 20·3,23·038·0 ± 19·7,38·036·9 ± 16·7,39·029·5 ± 18·4,30·016·9 ± 13·5,15·014·2 ± 12·7,10·0 < 0·001 Previous depression Yes53 (67·1)15 (32·6)11 (50·0)28 (58·3)4 (4·7)2 (5·6)60 (29·4)52 (4·9) < 0·001 No8 (10·1)13 (28·3)5 (22·7)9 (18·8)42 (49·4)22 (61·1)98 (48·0)844 (80·0) unknown18 (22·8)18 (39·1)6 (27·3)11 (22·9)39 (45·9)12 (33·3)46 (22·6)159 (15·1)SD = standard deviation$ range from 0 to 100, zero indicates the lowest level of health measured by the scales and 100 indicates the highest level of health∞ Possible score can range from 0 to100, with higher scores indicating greater diabetes-related emotional istressPHQ-9, Patient Health Questionnaire-9; C = CES-D, Center for Epidemiological Studies Depression Scale; S = SHI data, statutory health insurance dataGroup 1 = identified with all 3 instruments, Group 2 = identified by PHQ-9 and CES-D, Group 3 = identified by PHQ-9 and health insurance data, Group 4 = identified by CES-D and health insurance data, Group 5 = identified by CES-D, Group 6 = identified by PHQ-9, Group 7 = identified by health insurance data, Group 8 = no depressive disorder Looking at HRQoL, the average score on the PCS12 was highest in group 8 (median 47·3) and group 7 (median 42·3) and lowest in group 1 (median 30·6). These findings were similar for the MCS12. The average PAID score was highest in group 1 (median 45·0) and lowest in group 8 (median 10·0) and group 7 (median 15·0). In group 1 was the highest share of persons reporting a previous depression (67·$1\%$) and in group 8 the lowest share (4·$9\%$). ## Results of the multinomial model Table 3 reports the results of the multinomial logistic regression model with imputed data (the OOB imputation errors are reasonably small ranging from 0·086 to 0·71), comparing the seven groups with depressive disorder with the reference group with no depressive disorder (i.e., group 8). Overall, several differences in associations with the independent variables and the groups identified by the three methods were identified (even Bonferroni adjusted). We did not find a clear pattern between methods used and characteristics of individuals identified. However, we found some remarkable points. First, we observed that a person who took antidepressants compared to a person who did not take antidepressants was 12 times (or for that matter about 9, 8 and 7 times) more likely to be in group 3 (group 1, 7 or 4, respectively) than in the reference group, i.e., OR = 12·00 (8·94, 8·31 and 7·25, respectively). These four groups are characterized by a diagnosis in SHI data. Contrastingly, in groups not identified through a diagnosis in SHI data, i.e., groups 2, 5 and 6, the estimated effects of taking antidepressants were considerably smaller and even not significant for groups 5 and 6 (depression symptoms according to CES-D and PHQ-9 only). A quite similar pattern was noticed for reporting previous depression and comorbidities: Persons reporting a previous depression where significantly more likely to be in one of the groups identified through a diagnosis in SHI data (group 1, 3, 4 and 7) compared to the reference group and people with more comorbidities were more likely to be groups 4 and 7 (both identified through SHI diagnosis). Second, women were almost twice more likely to be in the group with an SHI data-based diagnosis only (group 7) than in the reference group (OR = 1.86). But there were no further significant associations related to other groups. Third, age was a significant factor for group membership probability. With each year of life, it is less likely to be in any group with depressive disorder than in the reference group (all OR’s are less than one), however, not significant for groups without SHI-based depression diagnosis. Low HRQoL values and especially low MCS-12 values were associated with belonging to any group but not the one identified by SHI data only, each in comparison to the reference group. We observed that a person with low MCS-12 is significantly more likely to be in a group with both symptoms according to PHQ-9- and CES-D (i.e., group 1 and group 2) than in any other group. Furthermore, the results regarding the PAID Score point in the same direction, values were associated with belonging to any group (except the smallest group) but not the one identified by SHI data only. Table 3Results of the multinomial model reporting odds ratio (OR) and $95\%$ confidence intervals ($95\%$ CI) for belonging to the different groups compared to belonging to the group with no depression (group 8)Group 1($$n = 79$$)Group 2($$n = 46$$)Group 3($$n = 22$$)Group 4($$n = 48$$)Group 5($$n = 85$$)Group 6($$n = 36$$)Group 7($$n = 205$$) Outcome P + C + S+ P + C + S-P + C-S+P-C + S+P-C + S-P + C-S-P-C-S+OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value)OR§[$95\%$ CI](p-value) Age (years) 0·94[0·90, 0·98] (0.0019) 0·96[0·92, 1·00](0.0559)0·93[0·89, 0·98] (0.0059) 0·94[0·91, 0·98] (0·0024) 0·97[0·94, 1·01](0·1025)0·99[0·95, 1·04](0·7203)0·98[0·96, 1·00] (0·0151) Sex (female vs. male $) 1·80[0·86, 3·76](0·1990)1·09[0·49, 2·54](0·8317)0·77[0·28, 2·11](0·6087)1·91[0·94, 3·91](0·0756)1·01[0·57, 1·77](0·9804)0·67[0·31, 1·45](0·3084)1·86[1·31, 2·65] (0·0005) Comorbidities (number) 1·12[0·94, 1·35](0·2052)0·82[0·65, 1·03](0·0924)1·09[0·87, 1·37](0·4627)1·44[1·23, 1·68] (< 0·0001) 0·91[0·78, 1·06](0·2183)1·08[0·90, 1·28](0·4134)1·31[1·20, 1·44] (< 0·001) taking insulin (yes vs.no $) 0·52[0·24, 1·16](0·1094)0·86[0·37, 1·97](0·7157)1·43[0·52, 3·92](0·4882)0·48[0·22, 1·06](0·0678)0·72[0·40, 1·32](0·2915)0·58[0·26, 1·29](0·1814)0·57[0·37, 0·86] (0·0075) taking antidepressants (yes vs. no $) 8·94[3·87, 20·63] (< 0·0001) 2·99[1·16, 7·74] (0·0235) 12·00[4·16, 34·65] (< 0·0001) 7·25[3·25, 16·15] (< 0·0001) 1·24[0·52, 2·93](0·6244)1·25[0·40, 3·92](0·7019)8·31[5·43, 12·73] (< 0·0001) Health related Quality of Life physical component summary scale of the SF-12 (PCS-12 (score) 0·90[0·86, 0·95] (< 0·0001) 0·84[0·79, 0·89] (< 0·0001) 0·89[0·84, 0·94] (0·0001) 0·94[0·90, 0·99] (0·0106)0·94[0·91, 0·97] (< 0·0001) 0·90[0·86, 0.94] (< 0·0001)0.99[0.97, 1.01] (0·3628) mental component summary scale of the SF-12 (MCS-12) (score) 0·73[0·69, 0·78] (< 0·0001) 0·75[0·71, 0·80] (< 0·0001)0·86[0·81, 0·91] (< 0·0001)0·88[0·84, 0·92] (< 0·0001)0·85[0·82, 0·88] (< 0·0001)0·88[0·84, 0·92] (< 0·0001)0·98[0·95, 1·00] (0·0396) Problem Areas in Diabetes Scale (PAID) (score) 1·07[1·05, 1·10] (< 0·0001) 1·08[1·06, 1·11] (< 0·0001)1·00[0·97, 1·03] (0·8948)1·06[1·04, 1·08] (< 0·0001)1·06[1·04, 1·08] (< 0·0001)1·03[1·01, 1·06] (0·0062)1·00[0·98, 1·01] (0·7238) Previous depression (yes vs. no $) 8·32[2·83, 24·48] (< 0·0001) 2·07[0·66·6·42](0·2099)6·86[1·86, 25·29] (0·0038) 9·11[3·43, 24·20] (< 0·0001) 0·37[0·11, 1·23](0·1041)0·52[0·11, 2·52](0·4188)5·19[3·10, 8·68] (< 0·0001) (Do not know vs. no $) 2·76[0·97, 7·85](0·0569)1·74[0·68, 4·42](0·2465)2·47[0·68, 8·96](0·1691)2·13[0·80, 5·70](0·1317)1·49[0·84, 2·66](0·1752)1·15[0·52, 2·56](0·7308)1·73[1·11, 2·71] (0·0158) P = PHQ-9, Patient Health Questionnaire-9; C = CES-D, Center for Epidemiological Studies Depression Scale; S = SHI data, statutory health insurance data, MCS-12 = Mental summary score of the SF-12, PCS-12 = Physical summary score of the SF-12, PAID = Problem Areas in Diabetes scaleGroup 1 = identified with all 3 methods, Group 2 = identified by PHQ-9 and CES-D, Group 3 = identified by PHQ-9 and health insurance data, Group 4 = identified by CES-D and health insurance data, Group 5 = identified by CES-D, Group 6 = identified by PHQ-9, Group 7 = identified by health insurance data, Group 8 = no depressive disorder$Reference group, OR = odds ratio (corresponding to one unite change in case of age, comorbidities, PCS-12, MCS-12 and PAID)CI = confidence interval, p-values under 0·05 are bold, p-values significant at multiple level α/70Nagelkerke’s R2 = 0.677 ## Discussion National and international guidelines recommend screening people with diabetes for depression to identify patients in need of psychological treatment [31, 32]. However, neither of these guidelines give detailed instructions on which screening instrument to use or describe the differences for the identified groups. A recent meta-analysis of diagnostic accuracy of depression questionnaires in adult patients with diabetes by de Joode et al. [ 2019] showed, that the CES-D and the PHQ-9 are the most frequently evaluated depression questionnaires among patients with diabetes [20]. They differed in terms of sensitivity and specificity, however none of the two instruments was found to be superior over the other. The results of our study show that between 14·$6\%$ and 22·$4\%$ of individuals with diabetes had depression depending on the method used to assess it. High prevalence estimates can be expected, since on the one hand, there is evidence that depression is a risk factor for diabetes and, on the other hand, studies show that the distress caused by diabetes contributes to the development of depression [1, 8, 33–36]. The results of our study are within the range of findings from the two most recent meta-analyses on depression among persons with diabetes where prevalence ranged from 1·$8\%$ up to 88·$0\%$ [1, 8]. One could assume, that both instruments used would identify more or less the same persons since they both measure depression symptoms within the last or the last two weeks. One could also assume an overlap between the two instruments and the persons identified through SHI data, however this overlap would be expected to be a little less pronounced as SHI data covers diagnosis from two years. We indeed found some overlap between the methods; however, surprisingly the majority of persons was identified by one instrument only (20·$7\%$ of the total sample), 7·$3\%$ of the whole sample were identified using two methods and 5·$6\%$ were identified with all three methods. The largest number of persons was identified through SHI data only (group 7). In total 68·$0\%$ of those identified with depression in our sample were identified through SHI data of which 42·$1\%$ were also identified through one of the two instruments. The characteristics of individuals identified by either of the two instruments were quite similar. Within our sample women were more likely to belong to the group identified through SHI data only (group 7). This is in line with results of an analysis of routine German SHI data that found women are diagnosed more frequently than men in all age groups [37]. It seems that persons who have a diagnosis of depression in SHI data but do not show symptoms in either of the questionnaires (group 7) do not noticeably differ in their HRQoL when compared with the group with no depression. Neither were the reported scores for diabetes related distress high in this group. Screening for depression among individuals with diabetes seems to be necessary since all groups identified through at least one questionnaire (groups 1–7) had more unfavorable outcomes compared to the group with no depression. Our findings show that, even though the same disease should be measured, the degree of variability in persons identified across the methods is substantial. If we would have used the PHQ-9 only we would have missed 133 patients who have depressive symptoms according to the CES-D but not according to the PHQ-9. Similarly, if we would have used only the CES-D we would have missed 58 persons who had symptoms according to the PHQ-9 but not according to the CES-D. Unfortunately, the differences between the groups were not pronounced enough to draw conclusions on which method is to be preferred. To keep in mind: We found some indication that the method chosen to identify persons with a depressive disorder might be related to particular underlying characteristics in the population identified. To our knowledge, there is no study, which has used a similar many-to-one approach. It will be interesting to compare findings of future studies with larger samples. ## Strength and limitations To our knowledge, this is the first study which analyses groups identified by different instruments to assess depression, and includes also SHI data. The linkage of survey data with SHI data allows a more detailed description of the identified persons which would not be possible with using only one of the two data sources. The analyzed data set is rather large allowing for robust estimates. Moreover, the response rate was with 51·$0\%$ reasonably high for a survey-based study. An nonresponse analysis did not reveal any major differences between responders and nonresponders especially with respect to depression [23]. Thus, nonresponse bias should be small. However only persons of one SHI could participate in the study which might influence the results since, for example, the prevalence of diabetes varies among the different SHIs in Germany [38]. Survey data was only assessed at one point during the study period whereas the SHI data covers the whole study period thus the prevalence observed in SHI data might be, among other reasons, higher as the time frame during which it is assessed is longer. Moreover, it has to be kept in mind that a diagnosis in SHI data is not valid as a screening measure for depression since people with a diagnosis have most likely received some form of therapy. Furthermore, within SHI data we find clinical diagnosis whereas the results of the CES-D and the PHQ-9 are not clinical diagnosis but results of screening measures for depression. Additionally, it might be the case that once a person has received a depression diagnosis it will not be removed from the track record even though the person does not have depression anymore. Likewise, we could not get a full history of diagnosed depression but only data on depression diagnosis 12 month before and after the baseline assessment. Our focus is on acute depression, in line with the two instruments used during the baseline survey, which is not covered by a lifetime history of depression. Since we include a considerable time frame before and after the baseline assessment misclassification is assumed to be low. ## Conclusion Our study is the first study that describes the overlap and differences between individuals identified with different methods to detect depression. Although several characteristics were found to be associated with belonging to the different groups; we did not find a clear pattern among those characteristics. However, we have found some initial indications that the method chosen is related to particular underlying characteristics in the population identified. The methods have a relatively low overlap. The majority of persons were identified using a diagnosis in SHI data. Those identified through SHI data only did not differ in their HRQoL when compared to those with no depression. This could be either due to a successful therapy or due to a spontaneous relapse. Our study shows, that there might be similarities but also differences in characteristics of identified persons depending on the method used. By using either of the three methods, one should be aware that certain persons are missed. Therefore, further research with a comprehensive data set, that is sufficiently large in terms of case numbers, is needed to address the implications of using either of the methods. Especially prospective studies investigating clinical outcomes would be important. This knowledge is crucial to enable clinicians to make an informed decision about the usage of either of the two instruments in every day practice, taking into account setting, time constraints and other relevant circumstances. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 ## References 1. Harding KA, Pushpanathan ME, Whitworth SR, Nanthakumar S, Bucks RS, Skinner TC. Depression prevalence in type 2 diabetes is not related to diabetes–depression symptom overlap but is related to symptom dimensions within patient self-report measures: a meta‐analysis. Diabet Med (2019.0) 36 1600-11. 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--- title: 'Adverse childhood experiences and health risk behaviours among adolescents and young adults: evidence from India' authors: - Chanda Maurya - Priya Maurya journal: BMC Public Health year: 2023 pmcid: PMC10031876 doi: 10.1186/s12889-023-15416-1 license: CC BY 4.0 --- # Adverse childhood experiences and health risk behaviours among adolescents and young adults: evidence from India ## Abstract ### Background Adverse childhood experiences (ACEs) are traumatic and stressful events that occur in childhood. These experiences at home, school, or in the community may damage the cognitive health and emotional skills of children and adolescents. ### Objective The present study examines the association between Adverse childhood experiences and risky health behaviour indicators while controlling other background characteristics among boys and girls. This study also assesses outcomes in the aggregate to estimate the impact of cumulative adversity on various risky health behavioural factors among boys and girls among adolescents and young adults (age group 13–23) in India. ### Data and methods Data were drawn from the second wave of the “Understanding the lives of adolescents and young adults (2018–2019)” survey. Bivariate and logistic regression analysis were conducted to fulfill the objective. ### Results The findings show that nearly $30\%$ of boys and $10\%$ of girls had violent behaviour. Substance use prevalence was much higher among boys ($34.11\%$) than girls ($6.65\%$). More boys had negative gender attitudes. The majority of the study participants had multiple ACEs. Boys who experienced more than three or more childhood adversity had two times higher odds (OR: 2.04; CI: 1.01–4.16) of the early sexual debut, while the same figure for girls was thirteen times (OR: 13.13; CI: 3.95–43.69) than their male counterparts. ### Conclusion The study findings underlined the need for implementing outcome-oriented approaches to adolescents’ health care and behavioural risks. Therefore, identifying and intervening with adolescents and young adults who are at the highest risk of engaging in risky behaviors early in life may reduce the risk of these behaviors persisting into adulthood. In order to avoid health risk behavior in later stages among adolescents and young adults, policymakers need to focus on ACEs as risk factors and take action to reduce this burden. A potential model could be to create awareness among family members, caregivers, and communities to be more empathetic toward the children. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15416-1. ## Introduction The childhood years, from prenatal to late adolescence and early adulthood, are “building block” years for the basis of intelligence and skill development, self-motivation, social behavior, health and adult relationships, which extend into adulthood [1, 2]. Some level of stress and adversity is a normal part of healthy human development. However, exposure to frequent stressful events without protective factors can result in negative health outcomes [1]. Adverse childhood experiences (ACEs) are traumatic and stressful events that occur in childhood before a child reaches the age of 18. It includes all types of direct and indirect abuse, neglect such as experiencing or witnessing violence, growing up with substance-abusive family members, incarceration of parents, parental separation, sibling or other family members, and suicidal incidence in the household as well as in the community [1, 3, 4]. Evidence also suggests that adverse experiences at home, school, or in the community may damage the cognitive health and emotional skills of children and adolescents [1, 5]. These childhood experiences also undermine their stability, sense of safety, and bonding among children [6]. According to the report of Centers for Disease Control and Prevention (CDC), nearly $62\%$ of adults from the United States of America experienced atleast one type of ACE before the age of 18, and about one in six reported that they had experienced more than three types of ACEs [1]. A study from India done by Fernandes et al. [ 2021] reported that one in two young people has child mistreatment ACEs and family-level ACEs [2]. ACEs can burden economic costs in the form of healthcare spending, loss of employee productivity, social services, and judicial expenditure [5, 7]. According to a recent estimate, global cost for the burden of violence against children is $2\%$ of the global GDP at the lowest level and it goes up to $8\%$ of the global GDP at the highest level in the year 2013 [5]. ACEs and health risk behaviours (HRBs) are also associated with increased comorbid conditions, early mortality, premature death and increased prevalence of the leading causes of death in adulthood [8, 9]. Many studies have found links between ACEs and long-term health outcomes, including cancer and cardiovascular diseases [8–10]. A shred of literature has also identified that adolescents who are the victim of adverse events are at greater risk of health risk behavior such as engaging in substance abuse, drug use, suicide attempts, sexually transmitted infections, risky sexual behavior, poor mental and physical health outcome, which leads to health disparity over the lifespan [11–15]. Research demonstrates that those who grew up experiencing inter-parental violence are more likely to have externalizing and internalizing behavior problems, trauma symptoms, own perpetration and victimization of violence, dating violence, hopelessness, psychological adjustment problems, and low self-esteem [16–20]. Further, the belief in gender norms and conceptions affects boys and girls differently. For instance, girls are more prone to domestic violence, which leads to internalizing disorders such as depression and anxiety, whereas it affects boys from violent behavior to perpetrating violence [21, 22]. Earlier studies focused on single ACEs mentioned that single predictors of ACEs did not account for a large amount of variance in health outcomes [9, 10]. Moreover, youth exposed to multiple types of maltreatment had a significantly higher chance of depressive disorders [23], substances use problems [24], and poor physical health [25] in comparison to those exposed to a single type. Cumulative Risk Theory also postulates that greater levels of adversity were associated with outcomes in a dose-dependent manner, such as multiple adverse exposures will result in poorer outcomes than single-event exposures [26, 27]. Research has shown that ACEs increase the risk of poor health-related outcomes in later life and most studies discussed the effect of ACEs on mental health, depression, and physical health. Also, the early onset of HRBs envisages their persistence into the later year of life [43]. Evidence demonstrates that ACEs are more common in low and middle-income countries due to lack of limited resources and fewer social and healthcare services [10]. Moreover, owing to the paucity of data, less is known about how ACEs are associated with HRBs in adolescence and early adulthood in the Indian context when many risky health behaviour problems often emerge. Identifying and treating the risk factors that are central to the development of health risk behaviours is pivotal to intervening with vulnerable populations such as adolescents and young adults who have adverse childhood experiences. Therefore, to fill the gaps in the literature in the Indian context, the current research has two objectives. First, to examine the association between adverse childhood experiences and risky health behaviour indicators while controlling with other background characteristics among boys and girls. Second, we assess outcomes in the aggregate to estimate the impact of cumulative adversity on various risky health behavioural factors among boys and girls in the age-group 13–23 years in India. All the analysis is segregated by gender as boys and girls have different kinds of exposure to different risk factors. ## Data The present study utilized data from the second wave of the “Understanding the lives of adolescents and young adults (UDAYA)” survey conducted by the Population Council under the supervision of the Ministry of Health and Family Welfare, Government of India [28]. The survey is longitudinal in nature and was conducted in two Indian states, namely, Uttar Pradesh and Bihar. The wave-1 of survey was conducted in 2015-16 and a follow-up survey was conducted three years later in 2018-19. The survey collected detailed information on family, community environment, media, assets acquired in adolescence, and quality of transitions to young adulthood indicators. The UDAYA survey adopted a multi-stage systematic sampling design to provide the estimates for states and urban and rural areas. For each sub-group of the adolescents, the required samples were determined at 920 younger boys, 2,350 older boys, 630 younger girls, 3,750 unmarried older girls, and 2,700 married older girls in each state. Information related to biomarkers was gathered from all younger adolescents and a sub-sample of older adolescents. To achieve the required samples, approximately 36,000 households were covered in each state [28]. A total of 150 (PSUs) visited each state to conduct interviews in the required number of households. As rural and urban areas are treated as independent sampling domains, therefore, drew sample areas independently for each of these domains. The 150 PSUs were divided equally into rural and urban areas. Within each sampling domain, a multi-stage systematic sampling design was adopted [28]. The 2011 census list of villages and wards served as the sampling frame for selection of the villages and wards in rural and urban areas, respectively. This list was stratified using four variables: region, village/ward size, the proportion of the population belonging to scheduled castes and tribes as well as female literacy. For household selection in rural areas, three stages and in urban areas four stages sampling design was adopted. In rural areas, villages were selected by using probability proportional to size (PPS) sampling. In urban areas, firstly 75 wards were selected systematically with probability proportional to size, and then from each wards, after arranging CEBs according to their administrative number, one CEB was selected randomly. To ensure the size of the CEBs, CEBs with less than 500 households merged with the nearest one. A complete mapping and household listing operation were carried out in each selected PSUs. Based on the list of the household list, first the PSUs were divided into two nearly equal segments and one segment was randomly chosen for performing interviews of females and the other for interviews of males. The number of household interviews to be conducted was fixed at 90 in the male segment and 150 in the female segment in each PSU in order to achieve our targeted sample of unmarried boys and girls. Households to be interviewed were selected with equal probability from the list using systematic sampling. The details of sampling are provided in the report [28]. The effective sample size for Uttar Pradesh and Bihar in the first wave was 10,350 and 10,350 adolescents aged 10–19 years, respectively [29]. Moreover, in wave-2 (2018–2019), the study interviewed the participants who were successfully interviewed in 2015–2016 and consented to be re-interviewed. After excluding the respondents who gave an inconsistent response to age and education in the follow-up survey ($3\%$), the final follow-up sample covered 4428 boys and 11,864 girls, with a rate of follow-up $74\%$ for boys and $81\%$ for girls [29]. The substantial sample size for this study was adolescents and young adults aged 13–23 years (boys- 4,221 and girls- 5,987) and was unmarried at both time of the survey. ## Outcome variables: The present study has five outcome variables namely violent behaviour, substances use, negative gender attitude, early sexual debut, and suicidal thoughts. ## Key explanatory variables: The present study has five key explanatory variables, namely substances use by family members, inter-parental violence, physical abuse, sexual abuse and gender discrimination. Details of the study variable were presented in Supplementary Table 1. ## Other explanatory variables: On the basis of previous evidence which has an impact on ACEs and HRB, individual and household level factors were considered as other covariates in the present study. Age group was recoded as 13–19 years and 20–23 years. Current schooling was recoded as no and yes. Co-reside with both parents was recoded as no and yes. Mother’s education was coded as illiterate and literate. Caste was recoded as Schedule caste/Schedule Tribes (SC/ST) and non-SC/ST (including other backward castes and general castes). Religion was recoded as Hindu and Non- Hindu. Wealth Index was divided as poor, middle and rich. Place of residence was recoded as urban and rural. State was recoded as Uttar Pradesh and Bihar. ## Statistical analysis Descriptive statistics (weighted percentage and unweighted sample) were used to assess the characteristics of the adolescents and young adults included in the study. Bivariate analysis looked at the unadjusted association between outcome variables (violent behaviour, substances use, negative gender attitude, early sexual debut and suicidal thoughts) and explanatory variables. Multivariate logistic regression models were run to calculate adjusted odds ratios that indicated whether certain subgroups of adolescents and younger adults were more or less likely to have adverse childhood experiences and whether or not the experiences predicted the likelihood that adolescents and younger adults would have violent behaviour, substances use, negative gender attitude, early sexual debut, suicidal thoughts. Further, logistic regression analysis was used for the association between multiple ACEs and violent behavior, substance use, negative gender attitude, early sexual debut, and suicidal thoughts. All models were adjusted for all other individual and household-level characteristics and segregated by gender of the respondents. Results were presented as an adjusted odds ratio (AOR) with $95\%$ confidence interval (CI). All the statistical analysis was performed using STATA 14 and MS Excel. ## Characteristics of the study population Characteristics of the study population are presented in Table 1. Almost $71\%$ of boys were adolescents, while the same prevalence for girls was $64\%$. Nearly $37.5\%$ of boys and $49.2\%$ of girls were currently not in school. Around one-third of boys ($30.8\%$) and girls ($33.7\%$) had a literate mothers. Nearly $16.9\%$ of the respondents were living with both their parents. Nearly a third-fourth of respondents belonged to non-SC/ST social groups. The majority were from the Hindu religion. About $30.39\%$ of boys and $26.58\%$ of girls were from poor wealth quantile households. The majority of the respondents were rural residents. Table 1Socio-demographic characteristics of respondents, 2018-19CharacteristicsBoysGirlsUnweighted sampleWeighted percentageUnweighted sampleWeighted percentage Age Group (in years) 13–192,98771.513,85864.120–231,23428.492,12935.9 Current schooling No1,53337.512,64749.24Yes2,68862.49334050.76 Mother’s education Illiterate2,71169.233,72566.3literate1,51030.772,26233.7 Co-residence with both parents No74016.971,08617.15Yes3,48183.034,90182.85 Caste SC/ST1,01226.441,20322.81Non-SC/ST3,20973.564,78477.19 Religion Hindu3,53784.424,39075.79Non-Hindu68415.581,59724.21 Wealth Index Poor1,03230.391,24026.58Middle86222.391,10620.48Rich2,32747.223,64152.95 Place of residence Urban1,93317.392,90119.44Rural2,28882.613,08680.56 State Uttar Pradesh2,18567.833,47675.61Bihar2,03632.172,51124.39 Total 4,221 5,987 ## Adverse childhood experiences and health risk behaviour among adolescents and young adults The percentage of different types of childhood adversity experienced and health risk behaviours among adolescents and young adults are presented in Table 2. About a third-fourth of respondents reported that at least one member in their family was substances users. One-fourth of the girls and one-fifth of the boys experienced interparental violence. Physical abuse prevalence was higher among boys ($58.94\%$) than girls ($35.91\%$). About $7\%$ of boys and $13\%$ of girls experienced gender discrimination. About $6.2\%$ of girls were victims of sexual violence, whereas the same prevalence for boys was $1.67\%$. Further, nearly $30.22\%$ of the boys and $9.62\%$ of the girls had violent behaviour. Substance use prevalence was much higher among boys ($34.11\%$) than girls ($6.65\%$). More boys (84.79) had negative gender attitudes compared to girls (68.02). About $4.55\%$ of the boys were sexually active before age eighteen, while the same prevalence for girls was $1.37\%$. Suicidal thoughts prevalence was higher among girls ($5.05\%$) than boys ($2.19\%$). Table 2Percentage distribution of adolescents and young adults by adverse childhood experiences [2015-16] and health risk behaviour, 2018-19VariablesBoysGirlsSamplePercentSamplePercent Adverse childhood experiences Substance use by family member2,99773.144,14873.57Inter-parental violence74619.251,38924.7Physical abuse2,44358.942,01435.91Gender discrimination2937.2969612.45Sexual abuse641.674426.2 Health risk behaviours Violent behavior1,38830.226219.62Substances use1,47734.114396.65Negative gender attitude3,46284.793,73868.02Early sexual debut1554.55791.37Suicidal Thoughts1172.193535.05 Total (N) 4,221 5,987 ## Prevalence of health risk behaviours by background characteristics among adolescents and young adults Table 3 represents the prevalence of health risk behaviours among adolescents and young adults by background characteristics. Boys and girls whose family members were substances users reported a higher prevalence of violent behaviour (boys: $30.3\%$; girls: $10.2\%$), substances use (boys: $37\%$; girls: $7.1\%$), negative gender attitudes (boys: $85.9\%$, girls: $71.2\%$), early sexual debut (boys: $5.2\%$; girls: $1.6\%$) as well as having thoughts about suicide (boys: $2.1\%$; girls: $5.3\%$). Risky health behaviour was more prevalent among those who witnessed interparental violence. Victims of physical abuse had a higher prevalence of violent behaviour (boys: $33.7\%$; girls: $11.5\%$), substances use (boys: $33.9\%$; girls: $6.5\%$), negative gender attitudes (boys: $88.9\%$; girls: $73.9\%$), sexually active before eighteen years (boys: $4.4\%$; girls: $2.2\%$) and suicidal thoughts (boys: $2.2\%$; girls: $5.6\%$). Table 3Prevalence of selected health risk behaviours by type of adverse childhood experiences and other background characteristics among adolescents and young adults, 2018-19VariablesViolent behaviourSubstances UseNegative gender attitudeEarly sexual debutSuicidal ThoughtsBoysGirlsBoysGirlsBoysGirlsBoysGirlsBoysGirlsN (%)N (%)N (%)N (%)N (%)N (%)N (%)N (%)N (%)N(%) Substances used by family members No383(30.01)161(8.05)350(26.34)128(5.37)949(81.83)981(59.24)32(2.85)12(0.62)32(2.46)95(4.44)Yes1,005(30.29)460(10.18)1,127(36.97)311(7.11)2,513(85.88)2,757(71.18)123(5.17)67(1.64)85(2.09)258(5.27) Inter-parental violence No1,125(29.29)452(9.21)1,165(32.65)343(6.62)2,824(84.37)2,770(65.77)118(4.08)36(0.75)94(2.16)244(4.56)Yes263(34.11)169(10.86)312(40.27)96(6.77)638(86.57)968(74.89)37(6.5)43(3.27)23(2.31)109(6.53) Physical abuse No505(25.16)358(8.55)625(34.49)289(6.74)1,427(83.22)2,343(64.71)66(0.75)35(0.75)51(2.18)222(4.73)Yes883(33.74)263(11.52)852(33.85)150(6.5)2,035(85.89)1,395(73.94)89(3.27)44(3.27)66(2.2)131(5.61) Sexual Abuse No25(30.91)50(9.36)28(40.59)41(6.95)57(88.45)267(64.93)8(15.17)32(9.5)5(7.34)39(6.55)Yes1,363(30.21)571(9.63)1,449[34]398(6.63)3,405(84.73)3,471(68.23)147(4.37)47(0.83)112(2.1)314(4.95) Gender discrimination No1,296(29.97)548(9.81)1,361(33.66)402(6.81)3,221(84.75)3,233(66.85)136(4.3)66(1.23)110(2.27)306(4.78)Yes92(33.41)73(8.29)116(39.91)37(5.58)241(85.41)505(76.29)19(7.73)13(2.36)7(1.17)47(6.95) Age group (in years) 13–191,116(34.43)453(10.71)850(27.95)288(6.38)2,496(85.97)2,550(72.25)55(1.9)37(0.93)76(1.77)230(5.22)20–23272(19.65)168(7.66)627(49.58)151(7.15)966(81.83)1,188(60.47)100(11.18)42(2.15)41(3.25)123(4.74) Current schooling No424(25.12)256(9.62)868(56.51)190(6.86)1,362(90.44)1,962(78.31)109(8.99)54(1.93)63(3.00)199(7.01)Yes964(33.28)365(9.61)609(20.67)249(6.45)2,100(81.41)1,776(58.05)46(1.88)25(0.83)54(1.71)154(3.15) Co-reside with both parents No258(33.26)120(10.63)299(38.47)98(7.03)615(87.15)698(70.01)30(4.38)23(2.42)38(3.71)70(5.55)Yes1130(29.6)501(9.41)1178(33.22)341(6.58)2847(84.31)3,040(67.61)125(4.58)56(1.15)79(1.88)283(4.94) Mother’s education Illiterate895(29.33)420(10.71)1,022(35.82)269(6.71)2368(88.37)2703(75.92)111(4.94)66(1.84)72(2.06)229(5.01)literate493(32.22)201(7.47)455(30.27)170(6.54)1094(76.74)1,035(52.48)44(3.65)13(0.45)45(2.48)124(5.13) Caste SC/ST310(27.03)152(11.49)430(58.11)79(41.89)852(5.39)803(86.78)58(6.52)34(3.05)40(2.61)88(6.34)NON-SC/ST1078(31.36)469(9.06)1,047(68.68)360(31.32)2610(7.03)2,935(84.08)97(3.84)45(0.87)77(2.04)265(4.67) Religion Hindu1,176(30.73)474(9.45)1,223(33.54)295(6.01)2,881(84.97)2,588(64.67)128(4.61)66(1.66)103(2.36)270(5.16)Non-Hindu212(27.46)147(10.14)254(37.25)144(8.66)581(83.84)1,150(78.53)27(4.19)13(0.47)14(1.28)83(4.69) Place of residence Urban675(32.77)298(9.68)708(34.74)244[8]1484(76.2)1,490(50.6)51(3.14)24(0.8)67(3.14)193(5.77)Rural713(29.68)323(9.6)769(33.98)195(6.33)1978(86.6)2,248(72.23)104(4.84)55(1.51)50(1.99)160(4.87) Wealth Index Poor340(31.59)173(11.84)399(36.53)76(6.2)938(91.64)1,020(84.07)43(4.94)30(2.19)22(1.6)74(5.37)Middle281(29.99)116(9.67)332(38.12)74(6.17)777(89.33)836(78.77)35(4.84)19(1.62)25(2.22)80(6.17)Rich767(29.44)332(8.48)746(30.66)289(7.07)1747(78.24)1882(55.81)77(4.15)30(0.86)70(2.56)199(4.45) State Uttar Pradesh639(28.35)295(8.54)750(33.14)248(6.81)1,791(83.59)2,201(66.9)103(5.02)46(1.37)61(2.26)184(4.58)Bihar749(34.16)326(12.95)727(36.17)191(6.18)1,671(87.33)1,537(71.49)52(3.55)33(1.38)56(2.05)169(6.49) Total 1,388(30.22) 621(9.62) 1,477(34.11) 439(6.65) 3,462(84.79) 1,882(68.02) 155(4.55) 79(1.37) 117(2.19) 353(5.05) ## Determinants of health risk behaviours among adolescents and young adults Table 4 depicts the multivariate logistic regression analysis estimate for health risk behavior among adolescents and young adults. Substances used by family members were significantly associated with increased odds of violent behaviour [boys- AOR: 1.19, CI: 1.02–1.38; girls- AOR: 1.28, CI: 1.05–1.57], substances use [boys- AOR: 1.38, CI: 1.17–1.62; girls- AOR: 1.21, CI: 0.97–1.52] and negative gender attitudes [boys- AOR: 1.18, CI: 0.98–1.41; girls- AOR: 1.28, CI: 1.12–1.45] than their counterparts. On considering familial ACEs, boys who reported witnessing interparental violence had higher odds for substance use behaviors [AOR: 1.29, CI: 1.08–1.55] and girls had greater odds of early sexual debut [AOR: 2.21, CI: 1.31–3.72] than their counterparts. Girls who experienced interparental violence were $35\%$ more likely to have suicidal thoughts than those who did not experience interparental violence. Boys [AOR: 1.34, CI: 1.17–1.54] and girls [AOR: 1.41, CI: 1.17–1.69] who had been a victim of physical abuse were significantly at greater risk of violent behavior than those who had not been a victim of physical abuse. Further, Girls who experienced physical violence were more likely to have negative gender attitudes [AOR: 1.28, CI: 1.12–1.46] and an early sexual debut [AOR: 1.68, CI: 1.00-2.82]. Victimization of sexual abuse was significantly associated with early sexual debut and suicidal thoughts among both boys and girls. Girls who experienced gender discrimination in their childhood were more likely to have negative gender attitudes [AOR: 1.22, CI: 1.00-1.48] than those who did not experience gender discrimination. The probability of involvement in the early sexual debuts was $72\%$ higher among boys who experienced gender discrimination. Table 4Logistic regression estimates on association between selected health risk behaviour and adverse childhood experiences among adolescents and young adults, 2018-19VariablesViolent behaviorSubstances UseNegative gender attitudeEarly sexual debutSuicidal thoughtsAOR ($95\%$ CI)AOR ($95\%$ CI)AOR ($95\%$ CI)AOR ($95\%$ CI)AOR ($95\%$ CI)BoysGirlsBoysGirlsBoysGirlsBoysGirlsBoysGirls Substances used by family members No®Yes1.19(1.02 1.38)1.28(1.05 1.57)1.38**(1.17 1.62)1.21(0.97 1.52)1.18(0.98 1.41)1.28*(1.12 1.45)1.24 (0.82 1.9)1.91(0.99 3.71)1.26 (0.81 1.95)1.14 (0.88 1.48) Inter-parental violence No®Yes1.09 (0.92 1.31)1.01 (0.82 1.24)1.29*(1.08 1.55)0.94 (0.73 1.22)0.98 (0.77 1.24)1 (0.86 1.17)1.22 (0.81 1.84)2.21(1.31 3.72)1.19 (0.73 1.94)1.35(1.03 1.75) Physical abuse No®Yes1.34(1.17 1.54)1.41(1.17 1.69)1.02 (0.88 1.18)1.1 (0.88 1.38)1.03 (0.87 1.23)1.28(1.12 1.46)1.07 (0.76 1.53)1.68(1 2.82)0.99 (0.67 1.47)1.05 (0.82 1.34) Sexual Abuse No®Yes1.31 (0.78 2.21)1.04 (0.76 1.43)1.59 (0.91 2.77)1.31 (0.93 1.84)1.74 (0.78 3.89)1.01 (0.81 1.25)3.69*(1.62 8.39)7.14(4.37 11.68)3.26(1.24 8.61)1.36(0.95 1.95) Gender discrimination No®Yes0.9 (0.7 1.17)0.89 (0.68 1.16)1.21 (0.93 1.58)0.71(0.5 1.00)0.85 (0.61 1.17)1.22(1.00 1.48)1.72(1.01 2.9)0.82 (0.43 1.55)0.87 (0.39 1.91)1.03 (0.74 1.44) Age group (in years) 13–19®20–230.53(0.45 0.62)0.78(0.64 0.95)2.05*(1.76 2.39)0.91 (0.74 1.13)0.64(0.53 0.77)0.65(0.58 0.74)3.71(2.58 5.33)2.67(1.62 4.41)0.96 (0.63 1.45)0.91 (0.72 1.16) Current schooling No®Yes1.29(1.11 1.49)1.14(0.95 1.38)0.27(0.24 0.50)1.01(0.82 1.26)0.48(0.39 0.59)0.53(0.46 0.60)0.34(0.23 0.50)0.58(0.34 0.98)0.45*(0.31 0.68)0.54(0.43 0.69) Co-reside with both parents No®Yes0.81(0.68 0.96)0.88 (0.71 1.10)0.69(0.57 0.83)0.72(0.56 0.91)1 (0.8 1.26)0.99 (0.85 1.15)0.84 (0.55 1.31)0.43(0.25 0.73)0.37(0.25 0.57)0.85 (0.64 1.12) Mother’s education Illiterate®literate0.93 (0.8 1.08)0.86 (0.7 1.05)0.97 (0.83 1.13)1.04 (0.83 1.3)0.58(0.49 0.69)0.6(0.53 0.68)0.97 (0.66 1.43)0.57(0.30 1.10)1.25 (0.83 1.90)1.03 (0.80 1.33) Caste SC/ST®NON-SC/ST1.15(0.98 1.36)0.85 (0.68 1.05)0.69(0.59 0.82)1 (0.76 1.31)1.12 (0.91 1.39)1.02 (0.87 1.19)0.55(0.38 0.81)0.51(0.30 0.85)0.58(0.38 0.88)0.85 (0.64 1.11) Religion Hindu®Non-Hindu0.91 (0.75 1.10)0.93 (0.75 1.16)0.98 (0.8 1.19)1.34(1.06 1.70)1.25(0.98 1.60)1.77**(1.52 2.05)1.1 (0.68 1.78)0.8 (0.40 1.57)0.62 (0.34 1.13)0.78(0.58 1.03) Place of residence Urban®Rural0.99 (0.81 1.20)0.74(0.57 0.95)1.04 (0.85 1.28)1.05 (0.75 1.46)1.07 (0.78 1.46)0.76(0.62 0.93)1.11 (0.69 1.79)0.72 (0.39 1.33)1.52 (0.84 2.77)1.26 (0.91 1.76) Wealth Index Poor®Middle1.01 (0.84 1.21)0.69(0.55 0.87)0.86 (0.71 1.04)1.19 (0.88 1.6)0.46(0.35 0.59)0.43(0.36 0.51)1.17 (0.75 1.83)0.53(0.29 0.98) 1.65(0.95 2.88) 0.96(0.70 1.32)Rich0.77(0.67 0.9)0.85 (0.71 1.03)0.78(0.67 0.91)0.81(0.65 1.01)1.29*(1.07 1.55)1.85(1.63 2.09)1.83(1.25 2.69)1.45 (0.84 2.52)0.63(0.41 0.95)0.64*(0.5 0.81) State Uttar Pradesh®Bihar1.3(1.14 1.49)1.56(1.31 1.85)1.19(1.04 1.37)1.08 (0.88 1.32)0.99 (0.84 1.17)1.06 (0.94 1.2)0.7(0.49 1)1.23 (0.75 2.02)0.96 (0.65 1.41)1.31(1.04 1.64) Constant 0.52*(0.38 0.70)0.15(0.11 0.22)0.38(0.28 0.52)0.08(0.05 0.13)6.01(4.04 8.92)1.6(1.22 2.10)0.01(0.00 0.02)0.01(0.00 0.02)0.04(0.02 0.10)0.06(0.03 0.09)Note: AOR: Adjusted odds ratio; CI: Confidence Interval; ®: Reference category; SC/ST: Scheduled Caste/Scheduled Tribe; if $p \leq 0.05$, if $p \leq 0.01$, * if $p \leq 0.001$ ## Prevalence and effect of cumulative adverse childhood experiences on risky health behaviours The majority of the study participants had multiple ACEs. Around one in five girls ($18.81\%$) had three or more ACEs, whereas the same prevalence for boys was $16.26\%$ (Fig. 1). Adolescents and younger adults who experienced three or more ACEs had significantly higher odds of risky health behaviors than those with no childhood adversity experience. Gender differences were observed in the magnitude of odds for health risk behaviour. Boys who experienced more than three or more childhood adversity were twice [AOR: 2.04; CI: 1.01–4.16] odds of the early sexual debut, while the same figure for girls was thirteen times [AOR: 13.13; CI: 3.95–43.69] than their counterparts (Fig. 2). Fig. 1Exposure to multiple adverse childhood experiences among adolescents and young adults, 2018-19 Fig. 2Logistic regression estimates on association between selected health risk behaviour and multiple adverse childhood experiences among adolescents and young adults, 2018-19Note: OR: Odds ratio; CI: Confidence Interval; ®: Reference category; if $p \leq 0.05$, if $p \leq 0.01$, ** if $p \leq 0.001$All the other variables were controlled. ## Discussion Health risk behaviors, including violent behavior, substance use, early sexual debut and suicidal thoughts, are the leading cause of morbidity and mortality among adolescents and young adults. Adolescents who experience adverse childhood are at higher risk of adopting negative health behavior. ACEs are stressful and traumatic, leading to immediate health hazards and affecting health across the lifespan [1, 9]. Social learning theory also suggests that social behaviour is learned through observation, imitation and modeling [30]. Therefore, understanding the developmental consequences of ACEs on health is important for developing a strength-based model. The current study expands the evidence by demonstrating how ACEs are associated with HRBs among adolescents. Consistent with our hypothesis, single and multiple ACEs have partially related to adverse health risk behavior. However, the strength of association was not consistent across all health risk domains among girls and boys. The present study findings indicate that substance use by family members and physical violence was the most common type of adverse childhood experience. This is not unusual since India is the second-largest tobacco consumer after China [31]. Physical abuse of children by family members is considered as a normal part of life and quite acceptable in the Indian traditional family system. The conceivable reason for such kind of activity is that it helps improve performance in academics and good behaviour and becomes well-mannered [32, 33]. A study on college students in South India mentioned that around $43\%$ of respondents considered themselves believed that some sort of punishment is necessary to develop good behaviour among children [32]. Sexual abuse generates deep concern for public health worldwide and has also been considered the most severe form of abuse among children [13]. In the present study, $1.67\%$ of boys and $6.2\%$ of girls experienced sexual abuse in childhood. Previous studies from India also reported similar prevalence of different forms of sexual abuse ranging from 2.6 to $14.3\%$ [32]. A systematic review and meta-analysis of 55 studies from 24 countries conducted in 2013 found that the prevalence of child sexual abuse ranges from 8 to $31\%$ among girls and 3–$17\%$ among boys [34]. Its traumatic impact leads to substance use, mental illness, suicide, abusive behaviour, teenage pregnancies, and sexually transmitted diseases that deteriorate the physical health of victims [9, 10, 13, 35]. In our study, sexual abuse was higher among girls ($6.2\%$) than boys ($1.67\%$), moreover, physical abuse prevalence was higher among boys than girls. A meta-analytic review also stated that boys are at higher risk of experiencing severe physical abuse, psychological abuse and neglect, whereas girls are more likely to be victims of sexual abuse [15]. Earlier evidence have also mentioned that male victims are less likely to report sexual abuse [2, 36], so the observed gender differences might be related to reporting bias. Therefore, it requires special attention. Further, this study findings indicate that at least one adverse childhood event was reported by more than one-third of adolescents and was more prevalent among females ($40.8\%$) than males ($36.79\%$). Moreover, overall, ACEs were higher among boys than girls. Similar findings were observed in a cohort study among the minority in the United States [24]. Exposure to different ACEs showed a range of 73.57–$1.67\%$. This prevalence is lower when compared with other studies from India [2, 37]. Kacker et al. [ 2007] reported that $68.9\%$ of children were exposed to physical abuse; $53\%$ experienced sexual abuse; $48.4\%$ suffered emotional abuse and $70.6\%$ experienced neglect [37]. Similarly, data from “Consortium on Vulnerability to Externalising Disorders and Addictions (cVEDA)” found that more than half of the participants reported child maltreatment and family-level ACEs such as domestic violence [2]. Nevertheless, these differences in prevalence must be explained by the measures taken consider in ACE, sample size and age group of the study population. The present study yields supportive evidence for the significant association between childhood adversity and poor risky lifestyle habits in later life. In general, the more adverse experience one has faced in childhood, the higher the probability for those individuals to engage in risky lifestyle behaviour, consequently suffer from negative health habits such as violent behaviour, smoking behaviour, early sexual debut and having mental disorders in later age [3, 38, 39]. Our findings suggested that substance use by family members was a significant risk factor for HRB, except for suicidal thoughts among adolescents and young adults. The social learning model also postulated that tobacco, alcohol or drug consumption are learned behaviour from the individuals and surroundings [30, 40]. Sexual abuse was positively associated with suicidal thoughts. This association may be elucidated by the fact that childhood trauma can negatively impact one’s ability to maintain cognitive health, resulting in risky health behaviours [39, 41]. Results indicated that gender discrimination experiences were positively associated with negative gender attitudes among girls. Previous evidence on discrimination asserted that adolescents who have gone through cultural-based stress or discrimination may experience negative gender attitudes and depressive symptoms. Also, these discrimination experiences could lead to long-term psychological maladjustment, worsening their health [38]. A recent study on youth with childhood adversity experiences stated that those with engaged in prosocial peer groups were less likely to indulge in risky behaviour. On the other hand, those who socialized with antisocial peer groups were at higher risk of risky health behaviours [42]. The current study also finds an unclear association between ACEs and selected health risk behaviour such as sexual abuse with violent behaviour, substances use and negative gender attitude. Therefore, these findings may imply that other individual and household level factors have impact on health outcome other than ACEs. Further, co-occurring maltreatment is very common than single maltreatment [43, 44]. Individuals with history of multiple types of maltreatment were at greater risk of violent behaviour, substance use, early sexual debut and suicidal thoughts and it appears to be a relatively strong dose-response relationship [39, 44]. The cumulative theory posits that if individual experiences more adverse events, health outcomes will be poorer than single event exposure [44–46]. Frequent or co-occurring childhood adversity may increase the harmful consequences of these adverse events to a greater extent [40, 47]. Adverse childhood experiences hamper cognitive development which leads to psychobiological vulnerability and developmental delays. Harmful health behaviour such as substances use maladaptive as a way of coping strategies with external and internal psychological and other challenges that are difficult for the person to manage. Individuals with higher ACEs had greater substance dependency [2, 47]. This study has several limitations. First, the UDAYA data were used for the study which was conducted in two states of the country, which limits the representativeness of our results. Therefore, the findings can’t be generalized at the country level. Second, the ACEs and HRBs were self-reported. Therefore, it is challenging to validate the extent of self-report and might be subject to recall bias. Third, though the study used a number of outcome variables and explanatory variables based on previous literature, however, all potential confounders were not available in the dataset, and for that reason, we were not able to consider them in the study. Fourth, in the present study, only a few ACEs have been studied. Other ACEs such as cyberbullying, harassment, aggressive behaviour, and fighting with peer groups in school which was available in the dataset, were not considered in the present study. Therefore, further research is required for the standardizing evaluation of ACEs and HRBs at the population level. ## Conclusion Adverse childhood experiences are common and have a massive impact on health and social outcomes. Thus, it has public health challenges with implications for the entire lifespan and every health and well-being domain. Also, multiple risk behaviour and condition often exist together in the same individual, adding cumulative risk for poor health outcomes in later stages. The study findings underlined the need for implementing outcome-oriented approaches to adolescents’ health care and behavioural risks. Therefore, identifying and intervening with adolescents and young adults who are at greater risk of engaging in risky behaviors early in life may reduce the risk of these behaviors persisting into adulthood. In order to avoid health risk behavior in later stages among adolescents and young adults, policymakers need to focus on ACEs as risk factors and take action to reduce this burden. A potential model could be to create awareness among family members, caregivers and communities to be more empathetic toward the children. Also, the decision-maker needs to work towards ensuring the protection of their rights and preventing their exploitation by formulating guidelines and strict laws. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. 1.Centers for Disease Control and Prevention. Adverse Childhood Experiences - Prevention Strategies. Atlanta, 2021. 2. Fernandes GS, Spiers A, Vaidya N. 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--- title: Microglia sustain anterior cingulate cortex neuronal hyperactivity in nicotine-induced pain authors: - Dan-dan Long - Yu-zhuo Zhang - An Liu - Liang Shen - Hong-rui Wei - Qian-qian Lou - Shan-shan Hu - Dan-yang Chen - Xiao-qing Chai - Di Wang journal: Journal of Neuroinflammation year: 2023 pmcid: PMC10031886 doi: 10.1186/s12974-023-02767-0 license: CC BY 4.0 --- # Microglia sustain anterior cingulate cortex neuronal hyperactivity in nicotine-induced pain ## Abstract ### Background Long-term smoking is a risk factor for chronic pain, and chronic nicotine exposure induces pain-like effects in rodents. The anterior cingulate cortex (ACC) has been demonstrated to be associated with pain and substance abuse. This study aims to investigate whether ACC microglia are altered in response to chronic nicotine exposure and their interaction with ACC neurons and subsequent nicotine-induced allodynia in mice. ### Methods We utilized a mouse model that was fed nicotine water for 28 days. Brain slices of the ACC were collected for morphological analysis to evaluate the impacts of chronic nicotine on microglia. In vivo calcium imaging and whole-cell patch clamp were used to record the excitability of ACC glutamatergic neurons. ### Results Compared to the vehicle control, the branch endpoints and the length of ACC microglial processes decreased in nicotine-treated mice, coinciding with the hyperactivity of glutamatergic neurons in the ACC. Inhibition of ACC glutamatergic neurons alleviated nicotine-induced allodynia and reduced microglial activation. On the other hand, reactive microglia sustain ACC neuronal excitability in response to chronic nicotine, and pharmacological inhibition of microglia by minocycline or liposome-clodronate reduces nicotine-induced allodynia. The neuron-microglia interaction in chronic nicotine-induced allodynia is mediated by increased expression of neuronal CX3CL1, which activates microglia by acting on CX3CR1 receptors on microglial cells. ### Conclusion Together, these findings underlie a critical role of ACC microglia in the maintenance of ACC neuronal hyperactivity and resulting nociceptive hypersensitivity in chronic nicotine-treated mice. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12974-023-02767-0. ## Background Tobacco smoking serves as an important vehicle for nicotine delivery in humans, and nicotine is the primary reinforcing component driving tobacco addiction. Chronic nicotine exerts a reinforcing effect through repeated activation of nicotinic acetylcholine receptors (nAChRs), which are mainly expressed in dopamine neurons in the ventral tegmental region of the midbrain [1, 2]. Concurrently, chronic exposure to nicotine produces profound changes in human physiology. In addition to studies showing that the risks of respiratory and cardiovascular diseases are higher in smokers than in nonsmokers, the epidemiological and clinical evidence strongly suggests an association between cigarette smoking and the incidence and severity of chronic painful conditions [3, 4]. In the laboratory, several animal studies have demonstrated that chronic nicotine elicits stable, persistent mechanical allodynia in rodents that further exacerbates coexisting painful conditions [5]. However, directly linking the cellular effects of chronic nicotine to modifications of the pain perception-related neural system has been thought to be multifaceted and elusive. Microglia are highly specialized resident macrophage-like cells that act as homeostatic sensors to monitor and sustain the balance of the microenvironment within the central nervous system (CNS) [6]. Their constant surveillance of the brain microenvironment enables microglia, characterized by extremely low thresholds, to be highly active in their continued response to any type of brain homeostasis disorder [7, 8]. Once ramified microglia transform into reactive states, reactive microglia exhibit substantial changes by altering their own morphology and number in diverse ways depending on the type of stimuli they sense. Microglia in the nucleus accumbens (NAc), which is a midbrain limbic region involved in the rewarding effects of nicotine, have been demonstrated to experience a substantial morphological change during nicotine dependence and withdrawal [9–11]. On the other hand, despite some disagreements in the literature [12], the majority of available studies provide compelling evidence that microglia are involved in pain modulation [13–15]. However, the central mechanisms that support a role for microglia in the development of chronic nicotine-induced pain have not been investigated. The anterior cingulate cortex (ACC) is a critical area for the integration of nociceptive perception and emotional responses in chronic pain [16, 17]. In human studies, magnetic resonance imaging (MRI) has shown that the ACC is the most consistently activated region in patients with chronic pain [18]. Consistent with brain imaging data, the inhibition of hyperexcitable pyramidal neurons in the ACC has been reported to produce analgesia in experimental rodent models of inflammatory and neuropathic pain [19, 20]. The central mechanism underlying chronic pain is usually linked to microglial activation at both spinal and supraspinal sites (including the ACC). Our [19] and other works [21–23] indicated that reactive microglia in the ACC, in line with neuronal hyperexcitability, contributed to central neuroadaptation in chronic painful conditions, although the mechanistic details of the reciprocal interactions between neurons and microglia are not well elucidated. In particular, the ACC is also involved in reward processing and addiction. Compulsive use of the addictive substances is related to increased neural activity and structural abnormalities in the ACC [24, 25]. Therefore, this study aims to determine whether and how chronic nicotine elicits nociceptive hypersensitivity in mice through altering ACC microglial morphology and function as well as neuronal activity. ## Animals All animal protocols were approved by the Animal Use and Ethics Committee, University of Science and Technology of China. Male C57BL/6J mice were purchased from Jackson Laboratory. In these experiments, all mice were used at 8–10 weeks of age, weighing approximately 25 g. All mice were kept on a 12-h light–dark cycle (lights on at 7 am) at a comfortable temperature (23–25 ℃) with free access to water and food. The animals were acclimatized to the environment for a week before the experiment. ## Animal models The mice were randomly divided into 2 groups: the vehicle group and the nicotine group. Mice in the nicotine group were administered nicotine with L-tartaric acid (HuaXia, Chengdu, China) in their drinking water. Mice in the vehicle group drank tap water (pH 7.0) with l-tartaric acid. The following treatment schedule was used for nicotine and tartaric acid (in μg/mL), respectively: Day 1–2 [50,75], Day 3–4 [100,150], Day 5 and beyond [200, 300] [26]. The subsequent doses were maintained at 300 μg/mL, and the water was changed every 2–3 days. Saccharin sodium (3 mg/mL, Macklin, China) was dissolved in tap water to mask the bitter taste of nicotine. Mice were treated for 28 days prior to experimentation. ## Von Frey filament test The paw withdrawal threshold (PWT) of the left hind plantar surface of mice was measured using von Frey filaments (von Frey filaments, Stoelting Inc., USA). Before the test, the mice were placed alone in clear plastic boxes and placed on a wire mesh elevated platform to acclimate for at least 2 days, 30 min a day. Before modelling, all mice were tested for the basic threshold of nociceptive behaviour, and mice with abnormal basic nociceptive thresholds were excluded. On the testing day, after acclimatization for 30 min, mechanical allodynia was tested by von Frey filaments in ascending order (0.02, 0.04, 0.07. 0.16, 0.4, 0.6, and 1.0 g) to stimulate the plantar surface of the left hind paw [27]. The minimal force filament that induced the mice to present a brisk paw withdrawal, flinching, or licking was taken as the mechanical response threshold. If there was no positive nociceptive response, a filament with a greater force was applied, and the measurement was repeated three times to obtain an average threshold. Each measurement was spaced at least 5 min apart to prevent aversion from frequent stimuli. ## Hargreaves test Mice were placed on a glass plate at a constant temperature of 30 ℃ and separated with a transparent plastic chamber. The mice were acclimatized for at least two days for 30 min each before testing. The Hargreaves test was used to assess the thermal nociceptive threshold. On the testing day, after acclimatization for 30 min, the thermal nociceptive threshold of the left hind paw was measured by focusing a beam of light on the plantar surface using the Hargreaves apparatus (IITC Life Science, USA). The basal paw withdrawal latency (PWL) was adjusted to 8–15 s and the thermal laser stimulation on the paw lasted for only 20 s to avoid potential tissue damage. The heat stimulation was repeated three times at an interval of at least 5 min for each paw and the mean was calculated. ## Immunohistochemistry and imaging Mice were deeply anaesthetized by intraperitoneal injection of sodium pentobarbital (50 mg/kg) and then perfused with $0.9\%$ saline followed by $4\%$ (w/v) paraformaldehyde. After perfusion, the brain was carefully removed and postfixed in $4\%$ PFA at 4 °C for at least 24 h and then immersed in $20\%$ and $30\%$ sucrose solution at 4 ℃ for 2‒3 days for dehydration until isotonic. Coronal sections (40 μm) were prepared using a cryostat microtome system at – 20 ℃, and the sections were immersed in antifreeze solution and stored at − 20 ℃. For immunohistochemistry, the ACC brain slices were first incubated in $0.3\%$ (v/v) Triton X-100 for 30 min and then incubated with $10\%$ donkey serum for 1 h at room temperature to block nonspecific reactions, followed by incubation with primary antibodies diluted in blocking solution ($0.3\%$ Triton X-100, $10\%$ donkey serum in PBS) at 4 °C for 24 h. The primary antibodies included anti-ionized calcium-binding adapter molecule 1 (Iba-1) (1:500, rabbit, Wako and 1:500, goat, Abcam), anti-Arginase (1:200, mouse, Abcam), anti-iNOS (1:100, rabbit, Abcam), anti-c-Fos (1:500, rabbit, Santa Cruz), anti-glutamate (1:500, rabbit, Sigma; 1:250, mouse, Sigma), anti-GABA (1:500, mouse, Sigma). After washing with PBS (3 × 5 min), the corresponding fluorophore-conjugated secondary antibodies (1:500, Invitrogen) were incubated with brain slices for 2 h at room temperature. At the last stage, the slices were incubated with 4,6-diamidino-2-phenylindole (DAPI; 1:2000, Sigma) for 5 min washed with PBS three times and mounted for imaging. The fluorescence signals were visualized using a Leica DM2500 camera and a Zeiss LSM980 microscope. All the experimental details are described elsewhere [19]. ## In vivo fibre-optic calcium recording Calcium signals were recorded by using fibre photometry. As indicated also by previous paper [28], mice were treated with $5\%$ (w/v, i.p.) chloral hydrate and fixed in a stereotactic frame (RWD, Shenzhen, China). rAAV-CaMKIIα- GCaMP6m-EGFP-WPRE-pA (AAV $\frac{2}{9}$, 5.04 × 1012 vg/mL, BrainVTA) was injected into the ACC (anterior posterior [AP] from bregma: 0.98 mm, medial lateral [ML] from the midline: 0.34 mm, dorsal ventral [DV] from the brain surface: − 1.50 mm) at a volume of 200 nL. An optical fibre (Inper, Hangzhou, China) was implanted in the same place and fixed with dental cement and glue. After 3 weeks, the virus was successfully expressed and the signal was recorded. To record fluorescence signals from GCaMP6m, light from a 470-nm LED was bandpass filtered ($\frac{470}{10}$ nm), collimated, reflected by dichroic mirrors, focused using a 20 × objective, and then delivered at a power of 25–40 μW at the tip of the fibre optic cannula. The fluorescence emitted by GCaMP6m was filtered by bandpass ($\frac{525}{40}$ nm) and focused on the sensor of a CMOS camera. The end of the fibre was imaged at a frame rate of 60 fps with InperSignal, and the mean value of the ROI at the end-face of the fibre was calculated using InperPlot software. To serve as an isosbestic control channel, LED light at 410 nm was bandpass filtered ($\frac{410}{10}$ nm) and transmitted alternately with LED light at 470 nm. GCaMP6m fluorescence intensity before and during punctate mechanical stimulation (von Frey filaments) was then recorded. We used a video camera to record behaviour. The fluorescence change value (ΔF/F) was obtained by calculating (F − F0)/F0, and the signal before the stimulus presentation 5 s was defined as the baseline. Data were analysed by InperPlot software (Inper Technology, Hangzhou). ## Brain slice preparation For acute brain slice preparation, mice were deeply anaesthetized with pentobarbital sodium ($2\%$ w/v, i.p.) and subsequently intracardially perfused with ice-cold oxygenated N-methyl-d-glucamine artificial cerebrospinal fluid (NMDG ACSF) that contained 93 mM N-methyl-d-glucamine (NMDG), 1.2 mM NaH2PO4, 2.5 mM KCl, 20 mM n-2-hydroxyethylpiperazine-N-2-ethanesulfonic acid (HEPES), 30 mM NaHCO3, 2 mM thiourea, 25 mM glucose, 3 mM Na-pyruvate, 5 mM Na-ascorbate, 10 mM MgSO4, 0.5 mM CaCl2, and 3 mM glutathione (GSH). Then, the brain was quickly removed from the skull. Coronal sections containing ACC (300 μm) were sectioned with a vibratome microtome (VT1200s, Leica) at 0.18 mm/s, incubated in NMDG ACSF at 33 ℃ for 10–12 min, and then transferred to HEPES artificial cerebrospinal fluid (ACSF) that contained 2.5 mM KCl, 92 mM NaCl, 30 mM NaHCO3, 20 mM HEPES, 1.2 mM NaH2PO4, 2 mM thiourea, 25 mM glucose, 3 mM Na-pyruvate, 5 mM Na-ascorbate, 2 mM MgSO4, 2 mM CaCl2, and 3 mM GSH at 25 °C for at least 1 h. The brain slices were transferred to a slice chamber (Warner Instruments) for whole-cell recording and were continuously perfused at a rate of 3 ml/min with oxygenated standard ACSF solution (32 °C) that contained 2.4 mM CaCl2, 3 mM KCl, 129 mM NaCl, 20 mM NaHCO3, 1.3 mM MgSO4, 1.2 mM KH2PO4, and 10 mM glucose. An in-line solution heater was used to maintain the temperature of the standard ACSF (TC-344B, Warner Instruments, USA). ## Whole-cell patch-clamp recordings Neurons in the ACC were visualized with a 40 × water immersion objective on an upright microscope (BX51WI, Olympus, Japan) equipped with interference contrast (IR/DIC) and an infrared camera connected to the video monitor. CaMKIIα-Cre::Ai14 mice in which tdTomato specifically labels glutamatergic neurons were used. We used a patch-clamp amplifier (MultiClamp 700B Amplifier, Digidata 1440Aanalog-to-digital converter, USA) and pClamp 10.7 software (Axon Instruments/Molecular Devices, USA) for whole-cell patch-clamp recording. Patch pipettes were pulled from borosilicate glass capillaries (VitalSense Scientific Instruments Co., Ltd., Wuhan, China) with an outer diameter of 1.5 mm on a four-stage horizontal puller (P1000, Sutter Instruments, USA). Voltage-clamp recording was performed with glass pipette filling containing 10 mM HEPES, 130 mM k-gluconate, 5 mM KCl, 2 mM MgCl2, 0.6 mM EGTA, and 0.3 mM Na-GTP (osmolarity: 285–290 mOsm/kg, pH: 7.2). The threshold current for firing was defined as the minimum strength of current injection required to elicit at least one or two spikes. All recordings were Bessel-filtered at 2.8 kHz and sampled at 100 kHz. Throughout the recording, only neurons with series resistance < 30 MΩ were used for analysis. ## Chemogenetic manipulation Mice were anaesthetized with $5\%$ (w/v, i.p.) chloral hydrate and fixed in a stereotactic frame (RWD, Shenzhen, China). A 200 nL volume of virus (rAAV-CaMKIIα-hM4Di-mCherry [AAV $\frac{2}{9}$, 5.85 × 1012 vg/mL, BrainVTA] or rAAV-CaMKIIα-mCherry [AAV $\frac{2}{9}$, 2.69 × 1012 vg/mL, BrainVTA]) was injected into the ACC (AP: 0.98 mm; ML: 0.34 mm; DV: − 1.50 mm) using calibrated glass microelectrodes connected to an infusion pump (RWD, Shenzhen, China) at a rate of 30 nL/min. Behavioural tests were performed at least 3 weeks after viral injection. For chemogenetic manipulation, the chemical ligand clozapine-N-oxide (CNO) (5 mg/kg, MCE, Monmouth Junction, NJ, USA) was intraperitoneally injected into these mice under isoflurane anaesthesia. Behaviour tests were then carried out at least 30 min later [19]. ## Morphological analysis In agreement with other published work [29], for morphometric analysis, confocal images of Iba1-positive cells were visualized and acquired by a confocal laser-scanning microscope (Carl Zeiss LSM880, Germany) with a 40 × objective at 30-μm intervals along the z-axis. The confocal Z-stack image file was analysed using the function ''Calculate Diameter of Filaments from Image''. The diameter of counted events was set between 0.25 μm and 15 μm. We modulated the “Starting Point” and “Seed Point Thresholds” of dendrites according to the actual size, then we selected “Remove Seed Points Around Starting Points” and set the diameter of “Sphere Regions” as 30 μm. The number of events was the number of myeloid cells in the image counted per mm3 according to the volume of the image. Subsequently, the threshold of the dendrites was adjusted using the spine “Points Diameter” step, and then the “Detect Spines” option was selected. Furthermore, after using the ''Dendrite length'' function from the statistics tab, the data was saved and used as a measure of microglial morphology, such as the number of endpoints and process length. Three images were randomly picked from each mouse, and the mean result was used for morphological analysis. Three-dimensional reconstruction of microglia was performed using IMARIS software. ## Fluorescence-activated cell sorting As previously published work [30], mice were anaesthetized with an intraperitoneal injection of pentobarbital ($2\%$ w/v, i.p). Subsequently, mice were perfused intracardially with 20 mL of 0.1 M cold Hanks’ balanced salt solution (HBSS), followed by a rapid collection of the ACC (6 pooled animals per N), which was washed with cold HBSS and chopped into small pieces on ice. Small tissue was mechanically homogenized using a 23G needle to produce single-cell suspension, which was filtered through a 70 μm cell strainer. After 70–$30\%$ Percoll gradient (Sigma, USA) separation, the single-cell suspension was isolated from the interface and filtered with a 200 μm nylon mesh prior to antibody staining. Microglia were labelled with CD11b-PC5.5 (1:20, Biolegend) and sorted by BD FACSAria III (BD, USA) for subsequent qPCR and immunohistochemistry experiments. ## Real-time PCR analysis TRIzol reagent (Vazyme, Nanjing, China) was used to extract total RNA from isolated microglia of the ACC, which was quantified using NanoVue plus (GE, USA). Purity was assessed by the quotient $\frac{260}{280}$ nm. Approximately 500 ng of total RNA was reverse transcribed using StarScript II First-strand cDNA Synthesis Mix with gDNA Remover (GenStar, Beijing, China) in 20 μL reactions following the manufacturer’s protocol. Quantitative real-time PCR (qPCR) for TNF-α, IL-1β, IL-6, CX3CL1, Arg-1 and iNOS was performed on an Applied Biosystems StepOneTM Real-Time PCR System using 2 × TSINGKE Master qPCR Mix (SYBR Green I) (Tsingke Biotechnology, Beijing, China) [19]. Gene-specific primers were purchased from Tsingke Biotechnology. The primer sequences were as follows: TNF-α (forward 5′-CCTGTAGCCCACGTCGTAG-3′,reverse 5′-GGGAGTAGACAAGGTACAACCC-3′); IL-1β (forward 5′-TTCAGGCAGGCAGTATCACTC-3′,reverse 5′-GAAGGTCCACGGGAAAGACAC-3′); IL-6 (forward 5′-GCCAACATTTTATTTCCGGGA-3′,reverse 5′-CCACTGAGCATATTTCTCGGG-3′); CX3CL1 (forward 5′-ACGAAATGCGAAATCATGTGC-3′,reverse 5′-CTGTGTCGTCTCCAGGACAA-3′); Arg-1 (forward 5′-AAGAAAAGGCCGATTCACCT-3′,reverse 5′-CACCTCCTCTGCTGTCTTCC-3′);or iNOS (forward 5′-GACATTACGACCCCTCCCAC-3′,reverse 5′-ACTCTGAGGGCTGACACAAG-3′). ## Drug administration into the ACC Mice were deeply anaesthetized with pentobarbital sodium ($2\%$ w/v, i.p.). We used a dental drill for craniotomy and then implanted a guide cannula (O.D.0.41 mm-27G/M3.5, RWD, Shenzhen, China) above the unilateral ACC (AP: 0.98 mm; ML: 0.34 mm; DV: − 1.30 mm). Modelling was started 21 days before catheterization. An injection cannula (O. D.0.20 mm-30G/M3.5, RWD, Shenzhen, China) with a PE tube was inserted into the guide cannula and drug or standard ACSF was injected for 1 min using a microinjector pump (RWD, Shenzhen, China) on Day 21 postmodelling. As previously described, a dosage of 10 mg/mL minocycline (500 nL, Aladdin, Shanghai, China) [19], a dosage of 5 mg/mL liposomes-clodronate (500 nL, Target Technology, Beijing, China) [31], a dosage of 0.5 ng/nL JMS-17-2 (100 nL, MedChemExpress, US) [32] were employed in the present study. ## Statistical analysis The parametric data are expressed as the mean ± SEM, and nonparametric data are presented as the median (IQR). Histograms or QQ plots was used to assess whether the data conformed to a normal distribution. If the distribution was normal, GraphPad Prism version 8.0 (GraphPad Software, Inc., USA) was used for statistical analysis and graphing. The unpaired two tailed Student’s t test was used for comparisons between two groups. One-way analysis of variance (ANOVA) or two-way ANOVA followed by Bonferroni test was used for multiple comparisons. Otherwise, the nonnormally distributed data were analysed by nonparametric test. Significance levels are displayed as $p \leq 0.05$, $p \leq 0.01$, *$p \leq 0.001$ and not significant (ns). ## Chronic nicotine exposure alters ACC microglial morphology Microglia exhibit substantial morphological and/or numerical changes once activated [33]. To evaluate both features during chronic nicotine exposure, we treated male mice with nicotine via their drinking water to induce nicotine dependence (Fig. 1A), as previously described [26, 34, 35]. Consistent with previously published findings [5, 36, 37], our results have shown that chronic exposure to nicotine produces stable, persistent allodynia in male mice. Compared to vehicle control group, mechanical and thermal thresholds were reduced in nicotine-treated mice (Fig. 1B). We performed morphological reconstruction of microglia by using Iba-1 staining and further assessed the changes in number and morphological processes. We used NAc microglia here as a control, since the results reported in previous studies have shown that microglia in the NAc, a midbrain limbic region related to the rewarding effects of nicotine, are activated in response to chronic nicotine in mice. In this experiment, NAc microglia underwent a series of morphological changes after mice were fed nicotine water (Fig. 1C–F), confirming previously published results. In addition to NAc reactive microglia, we found that microglia in the ACC from mice received nicotine and vehicle showed substantial differences in microglial morphology rather than cell number. Compared with the vehicle control, microglia in the ACC from nicotine-treated mice had different morphologies, with features of a larger cell area, shorter processes and decreased branch points (Fig. 1G, H). These results have shown that allodynia developed with a reactive status of ACC microglia in mice subjected to chronic nicotine. Fig. 1Chronic nicotine exposure alters ACC microglial morphology. A Schematic timeline of administration with L-tartaric acid and nicotine. B Changes of PWT and PWL in vehicle- and nicotine-treated mice C Representations from Paxinos & Franklin mouse atlas of regions of interest analysed. Anterior cingulate cortex (ACC) and Nucleus accumbens (NAc). D Representative imaging and 3D remodeling of Iba-1-labelled microglia in the Nucleus accumbens (NAc) of vehicle- and nicotine-treated mice after 28 days. Scale bars, 40 μm (overview) and 10 μm (Zoom and Rendering). E Quantification of Iba-1+ number and intensity in the NAc F Quantification of Iba-1+ soma size and Imaris-based semi-automatic quantification of Iba-1+ microglia morphometry in the NAc. G Representative imaging and 3D remodeling of Iba-1-labelled microglia in the anterior cingulate cortex (ACC) of vehicle- and nicotine-treated mice after 28 days. Scale bars, 40 μm (overview) and 10 μm (Zoom and Rendering). H Quantification of Iba-1+ number, intensity, soma size and Imaris-based semi-automatic quantification of Iba-1+ microglia morphometry in the ACC. Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Reactive microglia in the ACC are required for nicotine-induced allodynia To test the hypothesis that the hyperactivity of ACC microglia is necessary to sustain nicotine-induced allodynia, we aimed to investigate augmented pain-like response by suppressing and ablating reactive microglia by using intra-ACC administration of minocycline and liposome-clodronate, respectively (Fig. 2A). Minocycline has frequently been used to inhibit microglial activation [38]. Compared with ACSF control in the nicotine-treated mice, the morphological characteristics of microglia in nicotine-treated mice were significantly changed by intra-ACC minocycline (Fig. 2B). Our results showed that the number of Iba-1-labelled microglial cells was significantly reduced by intra-ACC minocycline, and minocycline significantly reversed the nicotine-induced reduction in both the number of microglial endpoints and process length (Fig. 2C). In addition, intra-ACC minocycline treatment resulted in a relief of nicotine-induced allodynia, which manifested by an obvious increase in mechanical and thermal thresholds (Fig. 2D). Intra-ACC minocycline increased the PWT and PWL in nicotine-treated mice compared with ACSF control. Next, liposome-clodronate was used for intra-ACC administration (Fig. 2E). Recent studies by Wang et al. [ 39] showed that liposome-clodronate produced a significant reduction in microglial cell numbers but had no effect on the number of neurons. After 7 days of liposome-clodronate intra-ACC administration, there was a strong microglial response in the ACC. Compared to ACSF control in nicotine-treated mice, the microglia in the ACC were almost completely ablated by intracranial administration of liposome-clodronate (Fig. 2F). In addition, compared to ACSF control, intracranial administration of liposome-clodronate relieved mechanical and thermal allodynia in nicotine-treated mice (Fig. 2G). These findings suggested that intracranial injection of minocycline and liposome-clodronate into ACC can reduce nicotine-induced allodynia. Fig. 2Reactive microglia in the ACC are required for nicotine-induced allodynia. A Schematic timeline of experiment. B Representative imaging of Iba-1-labelled microglia after intra-ACC ACSF in vehicle mice and intra-ACC ACSF or MINO in nicotine-treated mice. Scale bars, 40 μm (overview) and 10 μm (Zoom and Rendering). C Quantification of Iba-1+ number, intensity, soma size and Imaris-based semi-automatic quantification of Iba-1+ microglia morphometry in the ACC. D Changes of PWT and PWL in vehicle- and nicotine-treated mice with intra-ACC ACSF or MINO. E Schematic timeline of experiment. F Representative imaging of Iba-1-labelled microglia after intra-ACC ACSF in vehicle mice and intra-ACC ACSF or Lip in nicotine-treated mice. Scale bars, 40 μm (overview). G (Left) Quantification of Iba-1+ number in the ACC. ( Middle and right) Changes of PWT and PWL in vehicle- and nicotine-treated mice with intra-ACC ACSF or Lip. Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Nicotine-elicited reactive microglia do not exhibit inflammatory transcripts or phenotypes Microglia are essential for the early development of neuroinflammation and pain, and for promoting inflammation by releasing different proinflammatory cytokines including TNF-α, IL-1β and IL-6 [40]. To further test whether nicotine-induced allodynia causes classical inflammatory changes in microglia, we isolated ACC microglial cells by using fluorescence-activated cell sorting (Fig. 3A) and measured the changes in TNF-α, IL-1β and IL-6 messenger RNA (mRNA) transcripts. The results showed no distinct difference in the levels of TNF-α, IL-1β and IL-6 mRNA in nicotine-treated mice compared with vehicle control (Fig. 3B). To further validate this, other inflammation-related markers, including iNOS and Arg-1, were examined by using immunofluorescence staining of brain slices. Our results showed that neither Arg-1 nor iNOS showed significant changes in Iba1-labelled microglia between the nicotine and vehicle groups (Fig. 3C, D). Due to the nonmicroglial expression found in the brain slices, we focused on cellular Arg-1 and iNOS mRNA levels in isolated microglia. There was no significant difference in microglial Arg-1 or iNOS between the groups (Fig. 3B). In brief, these findings showed that nicotine did not cause inflammation with reactive microglia in the ACC in mice. Fig. 3Nicotine-elicited reactive microglia do not exhibit an inflammatory transcripts or phenotypes. A Workflow diagram and the scheme of flow cytometry and cell sorting. SSC: side scatter; FSC: forward scatter; FSC-H: forward scatter-height; FSC-A: forward scatter-area. B qPCR analysis of TNF-α, IL-1β, IL-6, Arg-1 and iNOS mRNA from wild-type mice on day-28 post-administration of l-tartaric acid or nicotine. C Representative images (left) and intensity of ROI cells in the ACC (right). ROI (region of interest), Arg-1 and Iba-1 double positive cells. Scale bar, 20 μm. D Representative images (left) and intensity of ROI cells in the ACC (right). ROI (region of interest), iNOS and Iba-1 double positive cells. Scale bar, 20 μm. Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Hyperactivity of ACC pyramidal neurons simultaneously develops in chronic nicotine-treated mice Microglia may be involved in the mechanism of pain sensitization by promoting hyperexcitation of innervated sensory neurons [13, 19]. We thus observed whether the alteration of ACC pyramidal neurons is tightly related to nicotine-induced allodynia. To investigate whether chronic nicotine can activate ACC pyramidal neurons, immunofluorescent staining was performed on c-Fos, a marker of neuronal activity in the ACC. The number and intensity of c-Fos in nicotine-treated mice increased significantly compared to vehicle treatment (Fig. 4A). Subsequent immunofluorescence results showed that more than $90\%$ of c-Fos colocalized with glutamatergic neurons (Fig. 4B). To confirm these results, we used ACC brain slices from CamKIIα-Cre:: Ai14 mice, in which tdTomato specifically labelled glutamatergic neurons (Fig. 4C). Whole-cell patch clamp results showed that, compared with vehicle control mice, nicotine-treated mice displayed increased excitability of ACC glutamatergic neurons, presenting an increase in spike number and a decrease in rheobase (Fig. 4D). Additionally, to explore whether ACC glutamatergic neurons are sensitive to subthreshold stimuli, rAAV-CaMKIIα-GCaMP6m-EGFP, a virus that expressed calcium indicators in glutamatergic neurons after 3 weeks, was injected into the ACC for fibre photometer recordings of nicotine-treated mice (Fig. 4E, F). Compared with the vehicle control, calcium signals increased rapidly after stimulation with a 0.07 g von Frey filament on the paws of nicotine-treated mice (Fig. 4G). Collectively, these data indicate that glutamatergic neurons are activated in chronic nicotine-treated mice. Fig. 4Hyperactivity of ACC pyramidal neurons simultaneously develops in chronic nicotine-treated mice. A (Left) Bilateral distribution of c-Fos+ neurons in the ACC in vehicle- and nicotine-treated mice after 28 days. Scale bar, 200 μm (left), 20 μm (right); (Right) number of c-Fos+ cells per 0.04mm2 area in the ACC. B showing that c-Fos co-labeled neurons within the ACC on day 28 after nicotine-treated were mainly co-localized with glutamatergic immunofluorescence and statistics data. Scale bars, 20 μm. C Schematic for viral injection and whole-cell recordings. D Sample traces (left) and data (middle and right) of action potentials and summarize of rheobase of vehicle- and nicotine-treated mice. E Schematic and timelines of fiber photometry. F Representative imaging of GCaMP6m viral expression in the ACC glutamatergic neurons 3 weeks after viral injection. The boxed region (dashed lines) represents the site of implantable fiber optic cannula. Scale bars, 200 μm (left), 20 μm (right). G The heatmaps (left) and mean (right) show that Ca2+ signals rapidly increased in nicotine-treated mice compared with vehicle mice after 28 days. The colored bar on the right indicates ΔF/F (%). H Average ΔF/F of ACCGCaMP6m signals in vehicle- and nicotine-treated mice. Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Inhibition of ACC pyramidal neurons reduces nicotine-induced allodynia and microglia activation To further understand the reciprocal interactions between neurons and microglia, chemogenetic approach was used to inhibit glutamatergic neurons, and the impact on the microglial response and allodynia over time was observed (Fig. 5A). rAAV-CaMKIIα-hM4Di-mCherry was injected into nicotine-treated mice and successfully expressed in ACC glutamatergic neurons after 3 weeks, and after selective inhibition of ACC glutamatergic neurons by intraperitoneal injection of CNO, both mechanical and thermal pain thresholds increased (Fig. 5B, C). Finally, to investigate whether there is an association between glutamatergic neuronal overactivity and microglial activation, we observed changes in microglial cells after consecutive intraperitoneal administration of CNO. Morphological analysis of microglial cells showed that activation of microglia was effectively eliminated due to chemical inhibition of glutamatergic neurons (Fig. 5D, E). In brief, these findings showed that the activation of ACC glutamatergic neurons may precede microglial activation in nicotine-treated mice. By inhibiting glutamatergic neurons, the activation of microglia was effectively eliminated and nicotine-induced pain thresholds were also increased. Fig. 5Inhibition of ACC pyramidal neurons reduces nicotine-induced allodynia and microglia activation. A Schematic timeline of experiment. B Representative imaging of hM4Di viral expression within glutamatergic neurons in the ACC 3 weeks after viral injection. Scale bars, 200 µm (left) and 20 µm (right). C Changes of PWT and PWL in nicotine-treated mice after inhibition of glutamatergic neurons. D Representative imaging and 3D remodeling of Iba-1-labeled microglia in the ACC in response to mCherry or hM4Di in nicotine-treated mice. E Quantification of Iba-1+ number, intensity, soma size and Imaris-based semi-automatic quantification of Iba-1+ microglia morphometry in the ACC. Scale bars, 40 μm (overview) and 10 μm (Zoom and Rendering). Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Fractalkine signalling mediates ACC neuron-microglia interactions in nicotine-induced allodynia Chemokines play a key role in mediating neuron-microglia communication, leading to increased pain perception [41]. Fractalkine is unique in the structure of the chemokine family, and its receptors are expressed both in the CNS and peripheral nerves, as well as in endothelial cells and lymphocytes. CX3CL1 (fractalkine)-CX3CR1 signalling represents the most important communication channel between neurons and microglia. To test the hypothesis that this pathway is involved in nicotine-induced allodynia changes, qPCR was used to detect the change in CX3CL1 content in the ACC. CX3CL1 expression was increased in nicotine-treated mice compared with vehicle-treated mice (Fig. 6A). CX3CL1 signalling relied on binding with downstream CX3CR1. JMS-17-2 acted as a selective small molecule inhibitor of CX3CR1 by intra-ACC injection. Our results showed that after intracranial injection of JMS-17-2 in nicotine-treated mice, there was a significant change in microglial morphology in the ACC, manifested by a decrease in the intensity of Iba-1-labelled microglia, accompanied by a decrease in the number of endpoints and the length of the process (Fig. 6B–D). Compared to intra-ACSF control, intra-ACC injection of JMS-17-2 alleviated nicotine-induced chronic allodynia (Fig. 6E). To verify whether inhibition of CX3CR1 can affect the excitability of glutamatergic neurons, ACC brain slices were prepared and incubated with the antagonist JMS-17-2 (10 nM) for 30 min at 37 °C, and then a patch-clamp electrophysiological experiment was performed. Electrophysiological results showed that, compared with nicotine-treated mice, the current-elicited action potentials decreased and rheobase increased in JMS-17-2-treated mice (Fig. 6F). These results suggest that microglia may maintain neuronal excitability through the CX3CL1-CX3CR1 signalling pathway. Fig. 6Fractalkine signalling mediates ACC neuron-microglia interactions in nicotine-induced allodynia. A qPCR analysis of CX3CL1 mRNA in vehicle- or nicotine-treated mice after 28 days. B Representative imaging of Iba-1-labeled microglia after intra-ACC ACSF or JMS-17-2 in nicotine-treated mice. Scale bars, 40 μm (overview) and 10 μm (Zoom and Rendering). C, D Quantification of Iba-1+ number, intensity, soma size and Imaris-based semi-automatic quantification of Iba-1+ microglia morphometry in the ACC. E Changes of PWT and PWL in nicotine-treated mice with intra-ACC ACSF or JMS-17-2. F Sample traces (left) and data of action potentials (middle) and summarize of rheobase (right) of intra-ACC ACSF or JMS-17-2. Data were presented as mean ± SEM or Median (IQR). $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; ns not significant. Details of the statistical analyses are presented in Additional file 1: Table S1 ## Discussion Nicotine, the main addictive ingredient in cigarettes, is thought to produce a temporary analgesic property when transiently administered [42, 43]. However, epidemiologic evidence shows that long-term smoking is a risk factor for chronic pain. A higher incidence and severity of pain experience has been found in regular smokers [44–46]. Our study suggests that chronic nicotine exposure causes nociceptive hypersensitivity in mice, and the neuroadaptation in the ACC, which manifests as hypersensitivity of glutamate neurons and activation of microglial cells, develops in response to chronic nicotine exposure. In addition, reciprocal communication between microglia and neurons occurred and further, at least in part, contributed to the allodynia induced by chronic nicotine. The family of nAChRs shows wide distribution in the central and peripheral nervous systems and is involved in numerous processes, including arousal, sleep, anxiety, cognition, and pain [47]. Nicotine addiction is a complex process. In addition to the release of VTA dopamine indirectly via stimulation of nAChRs, nicotine dependence may be related to neuronal interactions, transmitter release, receptor sensitization and desensitization [48, 49]. The ACC is known to be involved in pain and is associated with negative emotional regulation. Neuroimaging studies also support a functional role of the ACC in addiction-related neural networks [25]. A high density of neuronal nAChR expression in the human ACC suggests that nicotine may activate ACC neurons by direct action [50]. Microglial cells are highly specialized resident immune cells in the brain that detect and respond to interference by changing their morphology in response to the type of injury [51, 52]. Recent studies have shown that microglia in NAc are key mediators of nicotine dependence [10], but little is known regarding the role of ACC microglia in nicotine-elicited allodynia and their phenotypic changes in response to chronic nicotine exposure. Our study showed that chronic nicotine-induced allodynia is associated with concomitant reactive microglia in the ACC. In addition to antimicrobial activity, minocycline is well known for its anti-inflammatory and neuroprotective effects. It has been used frequently to suppress microglial activation in disease context. However, it has also been reported that minocycline has potential neuroprotective properties. In our previous study, by using a combination of ex vivo brain slice electrophysiology and PLX3397-induced microglia depletion, we concluded that a concentration of 50 µM minocycline ex vivo exerts an inhibitory effect on glutamatergic neurons in a microglia-dependent manner, and the dosage of 10 mg/ml used for intra-ACC administration of minocycline in vivo was calculated accordingly [19]. Similarly, we employed the dosage of 10 mg/ml minocycline in the present study. Indeed, similar findings have also been reported in prior literature, showing chronic nicotine does not elicit a classical inflammatory response despite microglial morphological changes in the NAc [9]. These findings in animal studies are consistent with many prior human studies demonstrating that smokers did not experience typical inflammatory responses compared with nonsmokers [53]. This finding is conflicting with the established view of a causal relationship between reactive microglia and inflammation. Thus it’s interesting to know whether and how reactive microglia contribute to ACC neuronal hyperactivity in nicotine-induced allodynia. Furthermore, an interaction between ACC neurons and microglia is involved in nicotine-induced allodynia, and this reciprocal process may be mediated through the CX3CL1 signalling cascade [54]. We suspect that ACC glutamatergic neurons are highly excited under chronic nicotine and then induce activation of microglia by the increased expression of fractalkine (CX3CL1), which acts on microglial receptor CX3CR1, resulting in altered signalling pathways that promote and maintain nociceptive hypersensitivity. In addition to their function in immunity and inflammation, microglial cells are involved in synaptic function plasticity, and microglial CX3CR1 signalling has been demonstrated to mediate developmental synaptic pruning through the neuronal ligand CX3CL1. Therefore, microglial activation under chronic nicotine within the ACC may enhance the development of aberrant synaptic connections and plasticity underlying nicotine dependency. However, one limitation of our study is that we did not work further on microglial synaptic pruning. Our experimental evidence showed that nicotine-induced allodynia in mice may be due to, at least in part, hyperexcitability of ACC glutamatergic neurons and activation of microglia. Therefore, one of the central mechanisms by which microglia sustain ACC neuronal hyperactivity underlies nicotine-induced allodynia in mice. ## Supplementary Information Additional file 1: Table S1 Detailed statistical information. ## References 1. 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--- title: Bayesian network modelling to identify on-ramps to childhood obesity authors: - Wanchuang Zhu - Roman Marchant - Richard W. Morris - Louise A. Baur - Stephen J. Simpson - Sally Cripps journal: BMC Medicine year: 2023 pmcid: PMC10031893 doi: 10.1186/s12916-023-02789-8 license: CC BY 4.0 --- # Bayesian network modelling to identify on-ramps to childhood obesity ## Abstract ### Background When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease “on-ramps” to be identified and targeted. ### Methods The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo. ### Results We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child’s BMI existed in over $99.99\%$ of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents’ BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different. ### Conclusions Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12916-023-02789-8. ## Background Chronic diseases emerge as the outcome of complex interactions among many variables, spanning individual biology (genetics, epigenetics, metabolism, physiology, behaviours) through to environmental, social and psychological, societal, and global influences [1]. Knowledge of this complexity has been important in moving beyond simple linear regression approaches to the prevention and treatment of chronic diseases. However, the challenge remains to tame the complexity of chronic disease systems by [1] simplifying the system and [2] identifying key causal pathways among the tangle of influences, which can then be targeted through public health and clinical interventions [2]. One advance towards simplifying the system has been the discovery that many chronic conditions (e.g. obesity, cardiometabolic diseases, many cancers, dementia, autoimmune diseases), as well as the biology of ageing, share a common immuno-metabolic substrate, which is powerfully modulated by diet, sleep, physical activity and mental health [3, 4]. Identifying such common mechanisms and causal structures simplifies the complex disease system, potentially rendering it more tractable to interventions that yield multiple simultaneous benefits. When developing effective intervention targets within a complex system, it is important to distinguish immediate causal factors from influences which serve as “on-ramps” to increased risk of disease. Commonly, health interventions target immediate causes, such as poor diet or physical inactivity in the case of obesity, while leaving upstream root causes untouched and the problem unsolved [5]. Hence, a priority is to develop modelling frameworks which can infer the causal structure of chronic disease networks. Here we implement one of the latest techniques in causal modelling, Bayesian networks (BN), to conduct a probabilistic causal analysis of the factors leading to childhood obesity, using data from a population study of Australian children. This method has the advantage of separating causal factors into those that are immediate factors, and therefore directly connected to the outcome, from those that serve as on-ramps, and are connected indirectly via intermediate variables [6]. Inference in BN has two parts: inference regarding the parameters of a particular network structure, and inference regarding the actual structure itself. BN studies in health care (reviewed by McLachlan et al. [ 7]) have largely ignored inference regarding the network structure and either assumed a particular structure a priori or sought the most likely structure without considering the relative probabilities of all possible structures. The latter is especially problematic when there are many near equally likely structures, as is inevitably the case within complex networks of interacting variables such as for chronic disease. To address these problems, we used a technique, known as Partition Markov chain Monte Carlo (PMCMC) [8], to place probabilities on all possible network structures rather than selecting a single most likely network structure. ## Data sources Data for the analyses came from ‘Growing Up in Australia: The Longitudinal Study of Australian Children’ (LSAC) [9], Australia’s nationally representative children’s longitudinal study, focusing on social, economic, physical, and cultural impacts on health, learning, social and cognitive development. The study tracks two cohorts of children, referred to as the birth (B) cohort (5107 infants from 0 to 1 years old) and the kindergarten (K) cohort (4983 children from ages 4 to 5 years). Data were collected over seven biennial visits (“Waves”) from 2004 to 2016. A selection of ~25 variables (Table 1) was chosen from the questionnaires for inclusion in Bayesian network models, informed by the existing literature on childhood obesity; e.g. the literature indicates that parental body mass index (BMI), socio-economic status, birthweight score and screen time are causally associated with childhood BMI.Table 1The descriptions of the variables in the analysisAbbreviationTypeDescriptionBMIContinuousChild BMI z-score for age based on CDC growth reference. The adjustment was made by the data provider. BMI1ContinuousParent 1’s BMI. Parent 1 is the primary carer who knows best of the child. BMI2ContinuousParent 2’s BMI. Parent 2 is Parent 1’s partner or another adult in the home with a parental relationship to the study child. In most cases this is the biological father, but step-fathers are also common. FTADiscreteStudy child’s choice to spend free time. 1: inactive, 2: both, 3: active. The data was collected via the face-to-face interview (F2F) with P1 and the study child. CDDiscreteSDQ conduct problems scale (integer 0 to 10) of child. Higher value indicates more severe conduct problem. The SDQ was completed by P1 during the interview questionnaire (P1D).DP1DiscreteParent 1 depression K6 score. Higher value indicates less depression. EGContinuousTotal minutes playing electronic games per week. This was reported by P1.EMDiscreteSDQ emotional problems scale (integer 0 to 10) of child. Higher value indicates more severe emotional problem. The SDQ was completed by P1 during the interview questionnaire (P1D).FHDiscreteHousehold financial hardship score (0–6). 0: not hard; 6: very hard. FSDiscreteParent 1 financial stress (1–6). 1: prosperous; 2: very comfortable; 3: reasonably comfortable; 4: just getting along; 5: poor; 6: very poor. The data was collected by F2F interview with P1.INCContinuousUsual weekly income for household. P1EDiscreteP1’s high school level. Higher value indicates more high school years completed. P2EDiscreteP2’s high school level. Higher value indicates more high school years completed. ODDiscreteThe quality of outdoor environment. Higher value indicates worse outdoor environment. This is derived from several F2F questions about the neighbourhood. RP1DiscreteThe scale of Parent 1 feeling rushed. Higher value indicates being less rushed. This data was completed by P1 during the interview questionnaire (P1D).SEContinuousThe z-score for socioeconomic position among all families. The derivation of this variable can be found in Gibbings et. al. [ 10].SLDiscreteThe study child sleep quality. Higher value indicates better sleep quality. The data was collected via the face-to-face interview (F2F) which was conducted with P1 and the study child. This variable is a summation of several questions, such as wheezing, snoring, waking during the night, bed wetting, nightmares and so on. SEXDiscreteGender. 1: male; 2: female. TVContinuousTotal minutes watching TV per week. This was reported by P1.BWZContinuousBirth weight Z-score. GWDiscreteGestation weeks. FVDiscreteServes of fruit and vegetables per day. This was reported by P1.HFDiscreteServes of high-fat food (inc. whole milk) per day. This was reported by P1.HSDDiscreteServes of high-sugar drinks per day. SLDContinuousSleep time duration (in hours). This was reported by P1.LOTEDiscreteIs the child regularly spoken to in a language other than English by you or other relatives, babysitters or at child care, pre-school or school? 1: NO, 2: YES. This data is collected via F2F with P1. ## Study design We analysed 12 of the cross-sectional datasets (waves 2–7 in the B cohort and waves 1–6 in the K cohort). For each wave and cohort, a Bayesian network (BN) [6] was used to model the factors surrounding childhood BMI. At each time point (wave) the cross-sectional dataset was used to construct the distribution of possible network structures, allowing for inference on the causal pathways to childhood BMI at that time point. By comparing cross-sectional networks, we could then follow the evolution of these causal pathways over time. To investigate the causal factors of childhood BMI in different genders, we further split each data set into boys and girls and made inferences on the corresponding Bayesian networks separately. ## Learning a Bayesian network When aiming to infer causality, graph structures are sought which do not contain any cycles/loops (such loops lead to self-causality, which is hard to interpret). These structures are called directed acyclic graphs (DAGs). Figure 1a illustrates a hypothetical DAG containing four variables: socio-economic status, BMI of the primary caregiver (BMI1), BMI of the second parent (BMI2), and BMI of the child (BMI). The interpretation of this DAG is as follows: First, socio-economic status is antecedent to parents’ BMI, i.e. socio-economic status is causal to the parents’ BMI and not the other way around. Second, both caregivers’ BMIs are causal to the child’s BMI. Third, conditional on the caregivers’ BMIs, a child’s BMI is independent of socio-economic status, i.e. socio-economic status has no impact on child BMI, given the parents’ BMI.Fig. 1An example of directed acyclic graph (DAG) containing four nodes. A directed edge between two nodes may indicate a causal relationship. For instance, SE → BMI1 could be interpreted as SE impacts BMI1. SE denotes socio-economic status, BMI1 denotes the primary caregiver’s BMI, BMI2 denotes the second caregiver’s BMI, and BMI denotes the child’s BMI. Panel (a) is the example DAG and panel (b) shows its corresponding completed partially directed acyclic graph, which will be discussed in section 'Learning a Bayesian network' A BN is a graphical representation of the equations in a structural equation model (SEM). In a Bayesian paradigm, one starts with a prior belief about the subject of interest (here, the DAG structure) based on existing knowledge. Then, on observing data, this prior belief is updated via what is known as a ‘likelihood function’ to arrive at a revised (‘posterior’) belief. In the context of BNs, the subject of interest has two components: first, the parameters of a particular DAG configuration, which we denote generically by θG, including quantities such as the strength of the connection between two factors; and second, the DAG itself, denoted by G. We wish to infer both θG and G, which is done via the joint posterior distribution P(θG, G∣ data) = P(θG∣ G, data)P(G∣ data). We first make inference regarding the structure G, by attaching probabilities to structures, P(G∣ data) and then, given a structure, infer the parameters needed to prescribe that structure P(θG∣ G, data). In the first step, P(G∣ data) is computed by integrating over all the possible values of parameters. This is different from traditional SEM which either assumes G is known or selects a single G,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{G}$$\end{document}G^ say, using a model selection technique and then makes inference only about \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\theta}_{\hat{G}}$$\end{document}θG^ [11, 12]. However, structure learning is arguably more fundamental to causal inference than parameter estimation, since the parameters can only be estimated once the structure is known. The review by McLachlan and colleagues [7] refers to three approaches for estimating a BN structure: data-driven, expert knowledge-driven, and hybrid approaches. These approaches are all Bayesian, which correspond to varying prior beliefs. The solely data-driven approach is analogous to a prior belief which assumes that each possible DAG is equally likely. The expert approach is analogous to a prior belief which assumes that the expert-constructed network is the true network, with probability 1. The hybrid approach, as used here, allows the strength of prior beliefs to vary both within and across structures; hence, information from different sources can be incorporated in a logically consistent manner, allowing the relative contributions of information from experts and from data to be measured. Importantly, hybrid approaches provide an ideal platform for formalising the collaboration between subject domain experts and specialist data experts: both groups are essential for success. Although Bayesian networks have the potential to implement causal inference using observational data, they are not without drawbacks. First, the number of possible DAGs grows super-exponentially with respect to the number of variables, and it is computationally infeasible to compute the likelihood for each possible DAG once there are more than only a moderate number (~10) of variables. Second, for linear Gaussian Bayesian networks, the structure learning algorithms can only learn up to a DAG’s equivalence class, in which all the DAGs are equally likely [6]. The equivalence class is represented by a completed partially directed acyclic graph (CPDAG) [6]. CPDAGs contain undirected links which could be in either direction. Figure 1b shows the CPDAG of the DAG in Fig. 1a. In Fig. 1b, the undirected link between socio-economic status and BMI1 indicates we cannot distinguish the causal directions. For computational reasons, almost all the existing algorithms to estimate network structures assume that continuous variables cannot be ‘parents’ of discrete variables [10]. In our data, there are both discrete and continuous variables. The algorithm we used to conduct structure learning is Partition Markov chain Monte Carlo (PMCMC) [7] and the code is available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/BiDAG/index.html). All the analyses in this paper were undertaken in R 4.0.4 (https://www.R-project.org/). PMCMC reduces the abovementioned computational challenges by collapsing the DAG space into partition space. We have adopted a strategy which considers every variable to be a Gaussian random variable to tackle the challenge caused by the existence of a mixture of continuous and discrete random variables in the data [13]. The details can be found in Additional file 1 [section of “The strategy in Partition MCMC to handle hybrid Bayesian networks”]. By applying PMCMC to the LSAC data, we obtained posterior samples of DAG structures at each time point for each wave and cohort of the LSAC data. Following the changes in DAG structures across waves allowed us to observe how causal patterns change as children age. We also calculated the posterior probability of each DAG (top left corner), which describes the probability of each DAG given the data. These probabilities are expressed as a proportion of the sum of the posterior probability densities corresponding to the top 100 graphs. The larger the value, the more probable is the graph. Mathematically, the probability is defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{d_i}{\sum_{$t = 1$}^{100}{d}_t}$$\end{document}di∑$t = 1100$dt, where di is the likelihood of the ith graph; i.e. a value of $70\%$ indicates that when considering the subset of the top 100 graph structures, that graph has a posterior probability of 0.70 if each graph is equally likely a priori. ## Results Table 2 lists the demographic features of the 2135 children depicted in Fig. 2 (B cohort wave 5), stratified over three weight classes according to BMI (underweight or less, normal weight, overweight or greater, based on Cole and colleagues [14]). The pattern of mean differences between weight classes is consistent with much of the previous literature on obesity. Children with obesity were more likely to have a lower socio-economic status score and more financial hardship; were less active with more TV minutes; have parents with higher BMI; and have a higher birth weight z-score. However, these mean differences cannot elucidate the causal dependencies represented by the DAGs. See the Supplementary Material for the demographic features of the other waves. Table 2Birth cohort aged 8 to 9 yearsCharacteristicUnderweight $$n = 107$$aNormal $$n = 1601$$aOverweight $$n = 427$$aFemale57 ($53\%$)763 ($48\%$)218 ($51\%$)BMI z-score (BMI)-1.86 (0.68)0.13 (0.59)1.62 (0.38)socioeconomic position (SE)0.35 (0.99)0.33 (0.92)0.06 (0.88)Child’s choice to spend free time (FTA) Active29 ($27\%$)430 ($27\%$)86 ($20\%$) Active and inactive55 ($51\%$)750 ($47\%$)199 ($47\%$) Inactive23 ($21\%$)420 ($26\%$)142 ($33\%$)Total No. of TV minutes for an average week (TV)12 [7]13 [8]14 [8]Total No. of electronic game minutes for an average week (EG)5.3 (5.2)5.0 (4.9)5.3 (5.4)SDQ Emotional symptoms scale (EM)2.06 (1.99)1.62 (1.76)1.82 (1.87)SDQ Conduct problems scale (CD)1.00 (1.14)1.08 (1.30)1.30 (1.44)Weekly household income (annual) (INC) $0–$999 ($0–$51999)10 ($9.3\%$)86 ($5.4\%$)46 ($11\%$) $1000–$1999 ($52,000–$103,999)34 ($32\%$)516 ($32\%$)133 ($31\%$) $2000–$2999 ($104,000–$155,999)37 ($35\%$)540 ($34\%$)154 ($36\%$) $3000 or more ($156,000 or more)26 ($24\%$)459 ($29\%$)94 ($22\%$)How family is getting on financially (FS) Prosperous/very comfortable25 ($23\%$)533 ($33\%$)113 ($26\%$) Comfortable/getting along82 ($77\%$)1,057 ($66\%$)308 ($72\%$) Poor/very poor0 ($0\%$)11 ($0.7\%$)6 ($1.4\%$)Hardship scale (FH)0.15 (0.45)0.13 (0.51)0.21 (0.56)Parental school completion (P1E)78 ($73\%$)1,281 ($80\%$)302 ($71\%$)Parental school completion (P2E)68 ($64\%$)1,088 ($68\%$)265 ($62\%$)Parental body mass index (BMI1)24.2 (4.9)25.5 (5.1)28.8 (6.0)Parental body mass index (BMI2)25.7 (3.3)27.3 (3.9)29.4 (4.7)Frequency of feeling rushed (RP1) Always/often65 ($61\%$)992 ($62\%$)242 ($57\%$) Sometimes37 ($35\%$)502 ($31\%$)149 ($35\%$) Rarely/never5 ($4.7\%$)107 ($6.7\%$)36 ($8.4\%$)K-6 Depression scale summed score (DP1)8.57 (2.65)8.42 (2.83)8.86 (3.33)Frequency ate fruit and vegetables (FV)3.19 (1.25)3.41 (1.38)3.28 (1.38)Frequency ate high-fat food (inc. whole milk) (HF)3.20 (1.41)3.24 (1.43)3.12 (1.54)Frequency drank high-sugar drinks (HSD)0.99 (1.08)0.94 (1.02)1.04 (1.07)Poor sleep quality (SL)b33 ($31\%$)463 ($29\%$)116 ($27\%$)Wake up in the morning (Time) (SLD)612 [43]618 [38]611 [43]Child regularly spoken to in a language other than English (LOTE)23 ($21\%$)251 ($16\%$)69 ($16\%$)No. weeks of gestation (GW)38.23 (6.01)38.92 (3.76)39.01 (3.44)Birth weight z-score (BWZ)-0.42 (0.99)0.04 (1.04)0.21 (1.10)an (%); Mean (SD)bSleep problems > 0Fig. 2The CPDAG derived from the most probable DAG for Wave 5 in B cohort. The child BMI node is highlighted by a red diamond shape. The thicknesses of the edges in the network correspond to the strength of relationship between nodes exists, with a thicker line denoting a higher absolute value. The edge coefficients are obtained by regression analysis given the DAG structure. The coefficients of undirected edges are inherited from the values of directed edges. The blue and orange edges indicate positive and negative relationships respectively. Orange ellipse nodes denote ancestors of child BMI ## Central role of socio-economic status and parental education over all time points The CPDAG derived from the most probable DAG for B cohort waves 5 (age 8–9) is shown in Fig. 2. It clearly shows that socio-economic status played a central role in the obesity networks we studied. For every wave in the B cohort, socio-economic status sits in the central position of the CPDAG structure. This implies that socio-economic status drives almost everything else in the network structure. The same conclusion applies to other waves. For Fig. 2, the effect size of socio-economic status on a child’s BMI z-score is about −0.062. In other words, a unit change in socio-economic status can lead to a decrease of 0.062 in a child’s BMI z-score on average. In LSAC, socio-economic status was derived from family income, parents’ education and parents’ occupational status (Gibbings and colleagues [15]); however, our results indicate that socio-economic status represents an important influence on child BMI over and above any of its constituents alone. In addition, more than $99\%$ of the posterior samples of DAG structures contain a pathway from socio-economic status or parental high school level to child BMI. The detail of the percentages is found in Table 3.Table 3The percentage of the path (SE/P1E/P2E → BMI1/BMI2 → BMI) appearing in the posterior samples for every waveWave1234567B cohortNA1.0001.0000.9980.9991.0001.000K cohort0.9961.0001.0001.0001.0001.000NA DAG structures from every wave show the importance of both parents finishing high school (P1E for mother, P2E for father). These two variables are correlated with socio-economic status, and the relationships are present in every DAG. The importance of this relationship is especially apparent in the network for K cohort wave 1 (Fig. 3), for which no specific socio-economic status variable was available. Figure 3 shows that in the absence of a specific socio-economic status variable, the parental high school level becomes the central node of the network. Fig. 3The CPDAG derived from the most probable DAG for Wave 1 in K cohort. The child BMI node is highlighted by a red diamond shape. The thicknesses of the edges in the network correspond to the strength of relationship between nodes exists, with a thicker line denoting a higher absolute value. The edge coefficients are obtained by regression analysis given the DAG structure. The coefficients of undirected edges are inherited from the values of directed edges. The blue and orange edges indicate positive and negative relationships respectively. Orange ellipse nodes denote ancestors of child BMI ## Free time activity becomes a driver of obesity as children age For children up to the age of 6 years (Fig. 4), a child’s BMI is on the periphery of the DAG and is connected to the other variables only via the BMI of the child’s carers (BMI1 and BMI2) and the child’s birth weight z-score. After the age of 6 years, the drivers of childhood obesity become more complex. There is a formation of another sub-graph around child-specific variables, such as conduct disorder, emotional problems, sleep quality and quantity and electronic games, although there is considerable uncertainty associated with the direction and strength of these relationships at different waves. Fig. 4The CPDAG derived from the most probable DAG for Wave 4 in B cohort. The child BMI node is highlighted by a red diamond shape. The thicknesses of the edges in the network correspond to the strength of relationship between nodes exists, with a thicker line denoting a higher absolute value. The edge coefficients are obtained by regression analysis given the DAG structure. The coefficients of undirected edges are inherited from the values of directed edges. The blue and orange edges indicate positive and negative relationships respectively. Orange ellipse nodes denote ancestors of child BMI Figure 2 shows that after age 8 years, free time activity (e.g. dancing and sports) becomes an important driver of obesity, and this, in turn, is driven by socio-economic status and the extent of electronic gaming by the child. In Fig. 2, the effect size of free time activity for a child’s BMI z-score is −0.118, which is significant. In other words, a unit change in free time activity can lead to a decrease of 0.118 in a child’s BMI z-score on average. Figure 4 also indicates that gender begins to impact a child’s BMI from age 6 (B cohort wave 4). However, gender does not directly influence a child’s BMI; rather, it passes its influence through other paths, e.g. SEX → electronic gaming→ free time activity → child BMI, which is shown in Fig. 2. To further investigate the impact of gender, we applied PMCMC to boys and girls separately. The CPDAG derived from the most likely DAG of B cohort wave 5 is presented in Fig. 5 for boys (Fig. 5a) and girls (Fig. 5b), respectively. For boys, the causal pathway electronic gaming → free time activity → child BMI emerges. However, for girls, sleep → free time activity → child BMI is the main pathway regarding how free time activity impacts child BMI. It would appear that boys and girls have different upstream factors influencing free time activity. Fig. 5a The CPDAG derived from the most probable DAG for boys in Wave 5 B cohort. The child BMI node is highlighted by a red diamond shape. The thicknesses of the edges in the network correspond to the strength of relationship between nodes exists, with a thicker line denoting a higher absolute value. The edge coefficients are obtained by regression analysis given the DAG structure. The coefficients of undirected edges are inherited from the values of directed edges. The blue and orange edges indicate positive and negative relationships respectively. Orange ellipse nodes denote ancestors of child BMI. b The CPDAG derived from the most probable DAG for girls in Wave 5 B cohort. The child BMI node is highlighted by a red diamond shape. The thicknesses of the edges in the network correspond to the strength of relationship between nodes exists, with a thicker line denoting a higher absolute value. The edge coefficients are obtained by regression analysis given the DAG structure. The coefficients of undirected edges are inherited from the values of directed edges. The blue and orange edges indicate positive and negative relationships respectively. Orange ellipse nodes denote ancestors of child BMI To illustrate the difference between BN and multiple regression, we conducted analyses using both techniques on a dataset containing variables: child BMI, parents’ BMI, socio-economic status, and parental high school level. Child BMI was the dependent variable in multiple regression analysis, and we compared its results to that of BN. The most probable DAG obtained by PMCMC showed the complete set of direct and indirect causal pathways from each of the variables to the child’s BMI. However, multiple regression only revealed the direct paths between parental BMIs and children’s BMI, with the other indirect relationships not detected. More details of this comparison can be found in the Supplementary Material. Table 4 shows that in B cohort wave 5, several links are so strong that they appear in almost all the posterior samples, such as BMI1/BMI2 → BMI, GW→ BWZ, BWZ → BMI and SE→BMI1. These links are well supported by the literature. We have created similar tables for other waves of data in both B and K cohorts. The details can be found in Supplementary Material. The tables imply that the above links are also the most common links for other datasets. It can also be seen that socio-economic status is a driving node in all the networks. It confirms the central role of socio-economic status. Table 4The percentages of posterior DAGs which contain the following edges in B cohort wave 5FromToProbabilityBMI1BMI1.000BMI2BMI1.000BWZBMI1.000GWBWZ1.000SETV1.000SEXEG1.000SEXCD1.000TVEG1.000SEINC0.998SEBMI10.997SERP10.997SEFV0.995EGFTA0.992FTABMI0.991DP1CD0.990SEHSD0.989SEFTA0.983DP1RP10.980LOTEFV0.979FSDP10.975FSFH0.960SEFS0.952LOTEBWZ0.945SLSLD0.937SEHF0.936SEXSLD0.929DP1EM0.917EMCD0.903SLINC0.884P1ESL0.864SLRP10.859SLEM0.848BMI2INC0.846LOTEFTA0.843EGFV0.831BMI1BMI20.815SEXEM0.772FSINC0.732TVRP10.719BMI1BWZ0.714SEBMI20.688HSDHF0.683BMI1FS0.613FHDP10.555SLDP10.503FVFTA0.496P2EP1E0.490P1ESE0.486TVHSD0.471P2ESE0.444FHSL0.427BMI1FH0.406P1EFS0.365EMRP10.343BMI1TV0.298FTAEM0.292INCRP10.287HFSL0.284FTASL0.179FHRP10.148 ## Discussion Obesity is a complex health issue, with multiple factors that operate at the level of the individual, family and beyond contributing to its development and maintenance [1, 16, 17]. For example, strong positive associations between parental and offspring BMI have been documented in many studies using traditional regression analytic approaches [18–20]. A range of other individual, family and socio-demographic characteristics are also associated with childhood obesity, including poor dietary intake, lower levels of physical activity, higher recreational screen time, family income and parental high school levels [19, 21, 22]. Studies in high-income countries have shown that social disadvantage, measured via family or parental income, parental high school level, occupation or employment status, is associated in childhood with both higher obesity prevalence rates and a range of obesity-related behaviours [19, 23]. Such complexity has made it challenging to identify key causal pathways and hence to implement effective interventions [24]. Our analyses have not only reinforced previous findings in relation to the multiple factors associated with childhood obesity but have now clarified the causal structure that underpins these associations. We have highlighted the central role of lower socio-economic status and low high school level for parents as the primary root cause of childhood obesity, which exerts its effect via several more proximal factors. Among these downstream factors, there was a strong and independent positive relationship between birth weight and childhood obesity, in keeping with findings from studies using traditional regression analyses [25]. Birth weight itself is influenced by a range of genetic, epigenetic, maternal, in utero and social factors. It is this ability to infer complex causal structures without temporal information which makes BN such a powerful and useful technique in health and medical research. Causal inference is achieved by estimating the full joint distribution of potential factors as a product of conditionally independent distributions, thereby distinguishing between direct and indirect dependencies. In contrast, more conventional multiple regression techniques lack a mechanism to infer causality without temporal information [26]. Indeed, multiple regression can be considered a specific example of a BN, where a particular dependency structure is imposed a priori, namely that all independent variables are directly related to the dependent variable. The marked difference between these two approaches is illustrated in the two distinct causal pathways shown in the Supplementary Materials, developed using a cut-down version of our dataset. In contrast to the structural equation modelling (SEM), another popular causal model, Bayesian networks learn the causal links, and the corresponding probabilities from the data, while SEM requires users either to specify the causal model prior to parameter estimation, based on expert knowledge or select an optimal structure based on some model selection criteria [11, 12]. In our analysis, the computational challenge is greatly alleviated, firstly, by working closely with content experts to incorporate domain knowledge by constructing a form of “blacklist” in DAG structures, which includes all forbidden links, i.e. those considered by domain experts to be illogical or infeasible (see Supplementary Materials for full “blacklist”). Secondly, PMCMC is used to reduce the DAG space by grouping individual DAG structures into partitions [8]. Importantly, PMCMC also allows samples to be drawn from the posterior distribution over graphs and thereby to quantify uncertainty, which is of paramount importance for domain practitioners who use the resulting graph structures to make decisions. Our results have important implications for interventions to address the complex issue of childhood obesity and demonstrate why intervening at the level of more proximate, downstream factors risks leaving the root causes of childhood obesity untouched leaves the problem unsolved. It is well recognised that low levels of maternal and paternal high school levels are associated with inequalities in child health status and mortality [27, 28]. These disparities appear to be mediated through other social determinants of health, including socio-economic status and living conditions [29]. There is some evidence that interventions which improve parental, especially maternal, education are associated with improvements in general measures of early childhood health and child mortality [30]. However, to our knowledge, there have been no such studies that measure offspring weight status by mid-childhood or adolescence. Our analyses imply that interventions that improve the socio-economic status, including through increasing high school completion rates, may lead to improvements in childhood obesity prevalence over much longer time spans. ## Limitations The LSAC data were collected in Australia which is a developed country. Thus, the children in this data set may only be representative of wealthy countries. It does not necessarily cover the characteristics of children from low- and middle-income countries. Our study used Bayesian networks to model the variables surrounding childhood obesity. Whereas BNs are powerful, they are not without their drawbacks. They are computationally expensive, due to the super-exponential growth of the number of possible graph structures. For example, a system with 20 factors has an order of 2190 possible graph structures, which is greater than the number of atoms in the universe. Therefore, an exhaustive search is impossible and some constraints on the number of possible graph structures need to be imposed. All the presented causal pathways are only valid for the LSAC data. There is the possibility that some confounders were not measured in these data and misleading causal links may have resulted. For example, there could be further ‘upstream’ variables influencing both socio-economic status and parental high school levels which might explain the apparent undirected link between those two variables. However, under the current dataset, socio-economic status and parental high school levels are co-dependent. ## Conclusions The Bayesian networks were used to model and infer the causal pathways leading to childhood obesity and show how these pathways change as children age. Our analysis of the LSAC data demonstrated that parental high school levels (both paternal and maternal) serve as an on-ramp to childhood obesity. Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. When children were aged 2–4 years the causal pathway was: socio-economic status/parental high school level → parental BMI → child BMI. By the time the child was 8–10 years old, an additional pathway had emerged: parental high school level − socio-economic status → electronic games → free time activity → child BMI. The strong and independent causal relationship between parents’ BMIs and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence. ## Supplementary Information Additional file 1. Details of data, data pre-processing, prior setting and comparison with multiple linear regression. Table S1. Design of the LSAC data collection. Table S2. The availability of variables in different waves for cohort B and K respectively. The white cells indicates missing values. Table S3. The implausible directed links from prior knowledge. Table S4. The estimation using linear regression. Figure S1. The most probable DAG learned by Partition MCMC. Table S5. Kindergarten cohort aged 4 to 5. Table S6. Birth cohort aged 6 to 7. Table S7. Birth cohort boys aged 8 to 9. Table S8. Birth cohort girls aged 8 to 9. Table S9. Top 40 edges found in the posterior samples of DAG for B cohort. Table S10. Top 40 edges found in the posterior samples of DAG for K cohort. A visualization tool about model selection regarding the graphs can be found here https://childhood-obesity-bayesian-network-playground.shinyapps.io/childhoodobesityDAG/. ## References 1. Butland B, Jebb S, Kopelman P. Tackling Obesities: Future Choices – Project Report (2007.0) 2. Rutter H, Savona N, Glonti K. The need for a complex systems model of evidence for public health. Lancet (2017.0) 390 2602-2604. DOI: 10.1016/S0140-6736(17)31267-9 3. Fontana L, Partridge L. Promoting health and longevity through diet: from model organisms to humans. Cell (2015.0) 161 106-118. DOI: 10.1016/j.cell.2015.02.020 4. Fontana L, Fasano A, Chong YS, Vineis P, Willett WC. Transdisciplinary research and clinical priorities for better health. PLoS Med (2021.0) 18 e1003699. DOI: 10.1371/journal.pmed.1003699 5. Swinburn BA, Kraak VI, Allender S. 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--- title: No evidence for associations between brood size, gut microbiome diversity and survival in great tit (Parus major) nestlings authors: - Martta Liukkonen - Mikaela Hukkanen - Nina Cossin-Sevrin - Antoine Stier - Eero Vesterinen - Kirsten Grond - Suvi Ruuskanen journal: Animal Microbiome year: 2023 pmcid: PMC10031902 doi: 10.1186/s42523-023-00241-z license: CC BY 4.0 --- # No evidence for associations between brood size, gut microbiome diversity and survival in great tit (Parus major) nestlings ## Abstract ### Background The gut microbiome forms at an early stage, yet data on the environmental factors influencing the development of wild avian microbiomes is limited. As the gut microbiome is a vital part of organismal health, it is important to understand how it may connect to host performance. The early studies with wild gut microbiome have shown that the rearing environment may be of importance in gut microbiome formation, yet the results vary across taxa, and the effects of specific environmental factors have not been characterized. Here, wild great tit (Parus major) broods were manipulated to either reduce or enlarge the original brood soon after hatching. We investigated if brood size was associated with nestling bacterial gut microbiome, and whether gut microbiome diversity predicted survival. Fecal samples were collected at mid-nestling stage and sequenced with the 16S rRNA gene amplicon sequencing, and nestling growth and survival were measured. ### Results Gut microbiome diversity showed high variation between individuals, but this variation was not significantly explained by brood size or body mass. Additionally, we did not find a significant effect of brood size on body mass or gut microbiome composition. We also demonstrated that early handling had no impact on nestling performance or gut microbiome. Furthermore, we found no significant association between gut microbiome diversity and short-term (survival to fledging) or mid-term (apparent juvenile) survival. ### Conclusions We found no clear association between early-life environment, offspring condition and gut microbiome. This suggests that brood size is not a significantly contributing factor to great tit nestling condition, and that other environmental and genetic factors may be more strongly linked to offspring condition and gut microbiome. Future studies should expand into other early-life environmental factors e.g., diet composition and quality, and parental influences. ### Supplementary Information The online version contains supplementary material available at 10.1186/s42523-023-00241-z. ## Introduction The digestive tract hosts a large community of different microorganisms (i.e., gut microbiome) and is known to be a fundamental part of organismal health and a powerful proximate mechanism affecting host performance [1, 2]. The gut microbiome has been studied across a wide range of animal taxa e.g., humans [3–5], fish [6], and economically important species such as poultry [7], and data from wild populations is slowly increasing [8]. Generally, a more diverse gut microbiome is considered beneficial for individual health [9], but there are also community structure effects that define the functionality [10]. For example, laboratory-bred mice with a less diverse gut microbiome have a substantially lower chance of surviving an influenza infection compared to their wild counterparts unless receiving a gut microbiota transplant from their wild counterparts [11, 12]. Moreover, gut microbiome had been linked to host fitness and survival in the Seychelles warbler (Acrocephalus sechellensis). Individuals that harbored opportunistic pathogens, i.e., microbes that usually do not cause disease in healthy individuals, but may become harmful in individuals that are immunocompromised, in their gut microbiome showed higher mortality [13, 14]. Therefore, understanding how gut microbiome affects fitness within and between individuals is necessary for not only understanding species survival but also evolution [15–17]. Gut microbiome forms at a young age and remains somewhat stable in adulthood as found for example in laboratory bred mice [18–20]. Disruption in the gut microbiome that leads to a microbiome imbalance at a young age could result in both short-term and long-term changes in the gut microbiome [21, 22]. Of the environmental effects, diet [23], including e.g., macronutrient balance (carbohydrates, fats, amino acids) [3, 24] has been concluded to be major determinants of rat and mouse gut microbiome, and this effect has recently been seen in avian models as well [25–28]. Moreover, macronutrient balance has been linked to intestinal microbiome composition [3, 24] and the functioning of individual immune response [29, 30]. However, as a large part of the prior research has focused strictly on humans or species living in controlled environments in which environmental effects on both the microbiome and host are sidelined [31, 32], many species, including most birds [8], have only started to attract attention [33]. The mechanisms of bacterial colonization of the bird gut are somewhat unique as avian life-histories differ significantly from those of e.g., mammals [34]. In mammals, the offspring are exposed to bacterial colonization during vaginal birth [35] and lactation [36, 37], whereas bird hatchlings are exposed to bacteria first upon hatching [20, 38]. Few studies have investigated the possibility of bacterial colonization in ovo, but results are still lacking [39]. Genetics [40–42] as well as the post-hatch environment [20, 43–46] have a significant effect on the formation of the avian gut microbiome. Once hatched, most altricial birds feed their young, which exposes the hatchlings to various bacteria that originate from the parents i.e., via vertical transmission [47]. It has also been shown that environmental factors are major contributors in the formation of gut microbiome [48–51], one of these being the rearing environment in the nest [44]. As early-life environment is connected to the establishment of gut microbiome, brood size may affect gut microbiome [52]. Brood size is often associated with parents’ performance and ability to feed their young [53], and the trade-off between offspring quality and quantity has been studied widely [54, 55]. Food quantity per nestling can decrease in enlarged broods, as parents may not be able to fully compensate for the additional amount of food an enlarged brood requires [56, 57]. For example, in great tits (Parus major) it has been shown that nestlings from reduced broods may have a higher body mass [58] and tend to survive better [59]. Importantly, great tit nestling body mass has been connected to gut microbiome diversity and composition: body mass positively correlates with gut microbiome richness [52]. This could imply that good physiological condition and high food availability would allow the host to have a diverse gut microbiome that promotes a healthy gut. Alterations in early-life gut microbiome could have long-term consequences on individual performance [60], yet such effects have rarely been studied in wild organisms. In wild birds, some bacterial taxa have been linked to better survival. For example, a high abundance of bacteria in the order Lactobacillales of the phylum Firmicutes is related to higher individual fitness in Seychelles warblers [14] and great tits [61]. These bacteria are also known for the benefits for bird health in economically important species such as poultry, in which Lactobacilli are used as probiotics to boost immune functioning [62]. Besides Lactobacillales, gut bacteria belonging to other genera such as Clostridium and Streptococcus are important for the degradation of non-starch polysaccharides and for the synthesis of essential molecules such as the short-chain fatty acids [63, 64]. Short-chain fatty acids are important in host energy metabolism [65] and therefore crucial for performance. Changes in nestling’s early-life gut microbiome could affect such key physiological processes that could influence for example nestling body mass, which is tightly linked to survival to fledging [58, 59]. Because the gut microbiome establishes at a young age and is less plastic later in life [18–20], gut microbiome and changes to its richness can have long-term effects on juvenile and adult survival [21, 22]. For example, antibiotic treatment at infancy can affect the expression of genes involved in immune system functioning and lead to long-term effects on host metabolism [20]. Moreover, changes in the rearing environment can affect individual physiology and these effects can carry over to later stages of an individual's life such as survival to fledging and lifetime reproductive success [66]. Here, we use an experimental approach to investigate whether brood size manipulation influenced wild great tit nestlings’ bacterial gut microbiome diversity on day 7 post-hatch. We also investigated whether brood size influenced nestling body mass on day 7 or on day 14 post-hatch, and if the gut microbiome predicts short-term (i.e., survival to fledging) and mid-term (i.e., apparent juvenile) survival. The great tit is a well-studied species in the fields of ecology and evolution, and it is easy to monitor in the wild due to its habit of breeding in nest boxes. Great tit nestlings’ gut microbiome undergoes profound shifts during early life [52], and it has been linked to nestling natal body mass and body size [52, 61], yet studies focusing on gut microbiome associations with survival are still scarce. Here, we manipulated wild great tit broods by reducing or enlarging the original brood size in order to analyze if this affected the gut microbiome. In large broods, nestlings need to compete for their food more [67, 68], and the lower food availability could result in a lower gut microbiome diversity. This might impair nestling body mass and fitness prospects [13, 52]. We used a partial cross-fostering design that enabled us to disentangle the relative contributions of genetic background, early maternal effects, and rearing environment such as parents, nest and nestmates on gut microbiome. Furthermore, we used an unmanipulated control group in which no nestling was cross-fostered to control for the possible effects of moving the nestlings between nests. For example, early human handling such as marking and weighing at day 2 post-hatch could influence gut microbiome later on. We hypothesized that [1] in reduced broods nestlings would have a higher body mass, [2] in reduced broods nestling gut microbiome would be more diverse than in enlarged broods, and [3] higher gut microbiome diversity on day 7 post-hatch would increase survival to fledging and potentially reflect apparent juvenile survival. Such knowledge could provide new information about gut microbiome in wild passerine bird population and how the early-life environment may associate with nestling gut microbiome, body mass, and short-term and mid-term survival. ## Study area and species The great tit is a small passerine bird, which breeds in secondary holes and artificial nest-boxes, making it a suitable model species. Great tits breed throughout Europe and inhabit parts of Northern Africa and Asia as well, and the breeding areas differ in environment and diet [69]. In Finland the great tit is a common species with an estimate of 1.5 to 2 million breeding pairs. They lay 6 to 12 eggs between April and May and the female incubates the eggs for 12–15 days. The nestlings fledge approximately 16 to 21 days after hatching. The study was conducted during the breeding season (May–July 2020) on Ruissalo island (60°25′59.99″ N 22°09′60.00″ E). Ruissalo island habitat is a mostly temperate deciduous forest and meadows, and some areas have small patches of coniferous trees. ## Brood size manipulation experiment Nest boxes were first monitored weekly and later daily when clutches were close to the estimated hatching date. Brood size manipulation took place on day 2 after hatching. Increases in great tit brood size can lead to lowered weight in both the nestlings and adults [70–75], and our decision on the number (i.e., + 2 or − 2) of manipulated nestlings (i.e, + 2 or − 2) followed the cited studies. We had four treatment groups (see Fig. 1): in the ‘enlarged group (henceforward called E)’, we increased the brood size by two individuals that were taken from a ‘reduced brood’. Correspondingly, in the ‘reduced group (henceforward called R)', we decreased the brood size by two individuals, that were added to the enlarged broods. In the ‘control group (henceforward called C)', we swapped nestlings between nests but did not change the brood size. And lastly, in the ‘unmanipulated control group (henceforward called COU)’, we only weighed and collected fecal samples on day 7 but did not move the nestlings between nests. We also moved nestlings between the reduced nests to ensure that all nests except for COU had both original and fostered nestlings. Control nests were used to control for potential cross-fostering effects unrelated to brood size. Additionally, in the unmanipulated control group nestlings were not moved or weighed on day 2 in order to control for any handling effects per se. This study design enabled us to test the potential impacts of handling nestlings and swapping the nest early after hatching. We aimed to move approximately half of the chicks in the manipulated nests, so that the number of original and the fostered nestlings would be the same in each nest after manipulation. Fig. 1Brood size manipulation experiment schematic diagram. 2-day-old nestlings were moved between nestboxes to enlarge or to reduce original brood size (an example with brood size of seven is given). Some nests were kept as control nests (nestlings were moved but brood size remained the same) and some were kept as unmanipulated control nests (nestlings were not moved at all to test whether early-life handling affects gut microbiome). The original brood size varied between nests Before they were moved, nestlings were weighed using a digital scale with a precision of 0.1 g and identified by clipping selected toenails. We aimed to add/remove nestlings that were of similar weight to avoid changing the sibling hierarchy in the brood. The moving procedure was performed as quickly as possible to minimize the risk of stress and the nestlings were kept in a warmed box during transportation. For each pair of nests in the brood size manipulation experiment, we selected nests that had a similar hatching date. In case of uneven number of nests hatching within a day, one or three nest(s) was/were allocated to the COU group. To avoid potential bias from hatching date, we allocated nests in any given day evenly to each treatment. We also checked that the treatments had an equal brood size on average i.e., we did not want to only reduce the larger clutches and enlarge the smaller clutches. These is also a significant bias towards COU nests being later in the season on average (Table 1).Table 1(A) Brood size before and after manipulation, (B) hatching date across treatments(A)Brood sizeBefore manipulation (mean ± SD)After manipulation (mean ± SD)Enlarged broods (E)7.700 ± 1.619.650 ± 1.309Reduced broods (R)8.375 ± 1.6376.375 ± 1.637Control broods (C)7.565 ± 1.8057.565 ± 1.805Unmanipulated broods7.810 ± 2.112naANOVAF3 = 0.987, $$p \leq 0.403$$(B)Hatching dateMean ± SDEnlarged broods (E)58.60 ± 5.77Reduced broods (R)59.83 ± 6.41Control broods (C)58.74 ± 5.34Unmanipulated broods63.81 ± 4.79(B)Tukey’s post-hoc for between-group comparisonsAverage hatching dateANOVAF3 = 3.964, $$p \leq 0.011$$ContrastsEstimateSEt.ratiopCOU-C5.0701.702.9830.019COU-E5.2101.762.9610.020COU-R3.9761.682.3630.092C-E0.1391.720.0810.100C-R− 1.0941.64− 0.6660.910E-R− 1.2331.70− 0.7230.888(E) enlarged brood size, (R) reduced brood size, (C) control brood size, (COU) unmanipulated control brood size. Brood size was successfully either reduced or enlarged by two chicks ## Fecal sample collection To study the effects that brood size may have on the nestling gut microbiome and its links to individual nestling body mass, survival to fledging and apparent juvenile survival, we used a subset of data from a larger experiment (Cossin-Sevrin et al., unpublished data). In this subset, we use individuals from which fecal samples were collected on day 7 after hatching and analyzed for microbiome diversity and composition ($C = 23$ nestlings/15 nests, COU = $\frac{22}{13}$, $E = 23$/15, $R = 24$/16) We aimed to collect two samples (one from original and one from foster nestlings) per nest. Fecal samples from the nestlings were collected gently by stimulating the cloaca with the collection tube. Samples were collected straight into a sterile 1.5 ml Eppendorf tube to avoid possible contamination of the sample. At time of sampling, each nestling was weighed (0.1 g), and the nestlings were ringed for individual identification using aluminum bands. The samples were stored in cool bags onsite and afterwards moved to a -80 °C freezer for storage until DNA extraction. ## Apparent juvenile survival We monitored all study nests until fledging to measure short-term survival. On day 14 post-hatch, the sampled nestlings were weighed, and wing-length was measured to detect if the manipulation had any effects on nestling growth. Nests were subsequently monitored for fledging success. Additionally, we monitored our study population for apparent juvenile survival (i.e., mid-term survival) after the breeding season (i.e., approximately 3 months after fledging) to assess the association between gut microbiome and post-fledging survival. We captured juvenile great tits by mist netting during the autumn–winter 2020 at six different feeding stations that had a continuous supply of sunflower seeds and suet blocks. Feeding stations were located within the previously mentioned nest box population areas. For each site mist netting with playback was conducted on three separate days during October–November 2020 for three hours at a time, leading to a total of 69 h of mist netting. A total of 88 individuals from the brood size manipulation experiment were caught, and the caught juvenile great tits were weighed, and wing length was measured. Our catching method provides an estimate of post-fledging survival yet, it could be slightly biased based by dispersal. In a previous study in our population [76], none of the birds ringed as nestlings were recaptured outside the study area, suggesting that dispersal is likely limited. ## DNA extraction and sequencing We chose two samples per nest for DNA extraction, yet in such a way that both fledged and not-fledged nestlings would be included in the dataset. DNA was extracted from nestling fecal samples using the Qiagen QIAamp PowerFecal Pro DNA Kit (Qiagen; Germany) following the manufacturer’s protocols. Additionally, we included negative (RNAse and DNAse free ddH2O) controls to control for contamination during DNA extraction and additional controls to confirm successful amplification during PCR. A short fragment of hypervariable V4 region in the 16S rRNA gene was amplified using the purified DNA samples as template with the following primers: 515F_Parada (5’-GTGYCAGCMGCCGCGGTAA-3’) and 806R_Apprill (5’-GGACTACNVGGGTWTCTAAT-3’) [77, 78]. PCRs were performed in a total volume of 12 µL using MyTaq RedMix DNA polymerase (Meridian Bioscience; Cincinnati, OH, USA). The PCR cycling conditions were as follows: first, an initial denaturation at 95 °C for 3 min followed by 30 cycles of 95 °C for 45 s, 55 °C for 60 s, and 72 °C for 90 s, and finished with a 10-min extension at 72 °C. After the first round of PCR, a second round was conducted to apply barcodes for sample identification [79]. For this, PCR cycling conditions were as follows: first, an initial denaturation at 95 °C for 4 min followed by 18 cycles of 98 °C for 20 s, 60 °C for 15 s, and 72 °C for 30 s, and finished with a 3-min extension at 72 °C. We performed replicate PCR reactions to control for errors during the amplification. Further on, the PCR products were measured for DNA concentration with Quant-IT PicoGreen dsDNA Assay Kit (ThermoFischer Scientific; Waltham, MA, USA) and for quality with TapeStation 4200 (Agilent; Santa Clara, CA, USA). The samples from each of the PCR replicates were pooled equimolarly creating two separate pools and purified using NucleoMag NGS Clean-up and Size Select beads (Macherey–Nagel; Düren, Germany). Finally, pooled samples were sequenced (2 × 300 bp) on the Illumina MiSeq platform (San Diego, CA, USA) at the Finnish Functional Genomic Center at the University of Turku (Turku, Finland). ## Sequence processing All statistical analyses were performed with R (v. 4.11.0; R Development Core Team 2021) unless otherwise stated. The demultiplexed Illumina sequence data was first processed with Cutadapt version 2.7 [80] to remove locus-specific primers from both R1 and R2 reads. Then, the DADA2 pipeline (v. 1.24.0; [81]) was used to filter the reads based on quality, merge the paired-end (R1 and R2) reads, to define the single DNA sequences i.e., Amplicon Sequence Variants (henceforward ASV), and to construct a ‘seqtab’. Seqtab is a matrix also known as otutable or readtable: ASVs in columns, samples in rows, number of reads in each cell, using default parameter settings. In total, our seqtab consisted of 6,929,537 high-quality reads. Reads were assigned to taxa against the SILVA v132 reference database [82] resulting in 8658 ASVs. To control for contamination, negative DNA extraction and PCR controls were used to identify contaminants (60 ASVs) using the decontam package (v. 1.12; [83]) and all were removed from the dataset. Sequencing runs (replicate PCR’s) were merged using the phyloseq package (v. 1.32.0) and non-bacterial sequences (mainly Chlorophyta) were removed from the data as they were not of interest in this study resulting in a total of 6566 ASVs (a total of 4,107,028 high-quality reads in all samples; mean per sample: 15,155.085; mean range per sample: 0–97,264). Singleton reads were removed from the dataset by the DADA2 pipeline. Data was further analyzed with the phyloseq package (v. 1.32.0; [84]), and the microbiome package (v. 1.18.0; [85]) and visualized with the ggplot2 package (v. 3.3.6; [86]). The final dataset contained 92 samples from great tit nestlings resulting in a total of 3,161,696 reads (mean per sample: 34,366.261; mean range per sample 108 – 189,300 reads), which belonged to 6,505 ASVs. The dataset was then rarefied for alpha diversity analyses at a depth of 5000, as this was where the rarefaction curves plateaued (see Additional file 2). The rarefied dataset contained 4,791 ASVs in 88 samples. For beta diversity, the unrarefied dataset was used after confirming that the beta diversity statistics were quantitatively similar for the rarefied and unrarefied datasets. Bacterial relative abundances were summarized at the phylum and genus level and plotted based on relative abundance for all phyla and genera. A Newick format phylogenetic tree with the UPGMA algorithm to cluster treatment groups together was used to visualize sample relatedness (see Additional file 3) and was constructed using the DECIPHER (v. 2.24.0; [87]), phangorn (v. 2.8.1; [88]), and visualized with ape (v. 5.6-2; [89]), and ggtree (v. 3.4.0; [90]) packages. ## Nestling body mass First, to analyze whether brood size manipulation affected nestling body mass in the C, E, and R treatment groups, we ran two linear mixed-effects models with the lme4 package (v. 1.1-29; [91]). In these models we used either body mass on day 7 or 14 as the dependent variable and brood size manipulation treatment, hatching date, body mass on day 2 post-hatch and original brood size as predicting variables. Hatching date is used as a predicting variable because it is known to affect nestling body mass during the breeding season [92] and there were differences in hatching date between the COU and other treatment groups (see Table 1). We included the interaction between original brood size and brood size manipulation treatment in both models as the effect of manipulation may depend on the original brood size. For example, there could be stronger effect of enlargement in already large broods. Nest of origin and nest of rearing were used as random intercepts to control for the non-independence of nestlings sharing the same original or foster nests. Here, we did not include the COU group in the analysis because we wanted to measure the effects of treatment on nestling body mass, and only enlarged, reduced or control broods’ nestlings were moved between nests. Second, to analyze whether the actual brood size affected nestling body mass, we ran two models where we used it as a continuous dependent variable to explain body mass either on day 7 or on day 14 post-hatch. Hatching date and body mass on day 2 post-hatch were used as predicting variables and nest of origin and nest of rearing as random intercepts to control for the non-independency of samples. We included the interaction between manipulated brood size and hatching date in the models because the effect of brood size may depend on the hatching date. For example, hatching date can reflect environmental conditions and large broods may perform poorly late in the season due to poorer food availability. The COU group was initially excluded from this model to see which of the two random effects, nest of origin or nest of rearing, explained a larger portion of variation in the treatment groups. In the COU group, nest of origin and nest or rearing were the same, which meant we could not include both random effects in models where all treatment groups were present due to the model failing to converge. Nest of origin explained more of the variation in the first model (see Additional file 4) and therefore, we used it in the full models with all treatment groups: C, COU, E and R. In these models, nestling body mass either on day 7 and or on day 14 post-hatch was used as a dependent variable and manipulated brood size as the explanatory variable. Hatching date and body mass on day 2 post-hatch were set as predicting variables. Nest of rearing was used as a random intercept to control for the non-independence of nestlings sharing the same foster nests. The significance of factors included in the models were tested using the F-test ratios in analysis of variance (Type III ANOVA). ## Alpha diversity For alpha diversity analyses, which measures within-sample species diversity, we ran two linear mixed-effects models with the lme4 package (v. 1.1-29; [91]) to measure if either brood size manipulation or manipulated brood size as a continuous variable were associated with gut microbiome diversity. We used two alpha diversity metrics: the Shannon Diversity Index, which measures the number of bacterial ASVs and their abundance evenness within a sample, and Chao1 Richness, which is an estimation of the number of different bacterial ASVs in a sample. Both metrics were used to check if alpha diversity results were consistent across different metrics. Each diversity index was used as the dependent variable at a time and either brood size manipulation treatment or manipulated brood size as a predicting variable. In both models we included original brood size, weight on day 7 post-hatch and hatching date as covariates. We included interaction between brood size manipulation treatment and original brood size as there could be a stronger effect of enlargement in initially large broods. We also included interaction between manipulated brood size and weight on day 7 post-hatch because effect of brood size on microbiome may depend on nestling weight. We also tested whether alpha diversity predicted weight on day 7 post-hatch, as weight and gut microbiome diversity have been connected in previous studies. In this analysis we used weight on day 7 post-hatch as the dependent variable and alpha diversity (Shannon Diversity Index and Chao1 Richness), treatment and hatching date as predicting variables and nest of rearing as the random effect. In these sets of models, we first excluded the COU group to see which of the two random effects, nest of origin or nest of rearing, explained a larger proportion of variation in the treatment groups. Nest of rearing explained more of the variation in this model (see Additional file 4) and therefore, we used it in the full model with all treatment groups: C, COU, E and R. The significance of factors included in the models were tested using the F-test ratios in analysis of variance (ANOVA). As 7-day-old nestlings, most bacterial taxa belonged to the phyla Proteobacteria, Firmicutes, and Actinobacteria (Fig. 2).Fig. 2Bacterial relative abundances on Phylum level across the four treatment groups. Each bar represents an individual sample. Treatment groups are control (C), unmanipulated control (COU), enlarged (E), and reduced (R). $$n = 88$$ samples divided into treatment groups as follows: $C = 23$, COU = 21, $E = 20$, $R = 24.$ Phyla with less that $10\%$ in relative abundance is collapsed into the category “< $10\%$ abundance” Brood size manipulation did not significantly influence alpha diversity (Shannon Diversity Index) (ANOVA: F3, 47.488 = 1.026, $$p \leq 0.390$$, Fig. 3; see Additional file 7). Moreover, original brood size (ANOVA: F1, 50.269 = 0.388, $$p \leq 0.536$$; see Additional file 7), weight on day 7 post-hatch (ANOVA: F1, 80.551 = 0.003, $$p \leq 0.959$$; see Additional file 7), and hatching date (ANOVA: F1, 50.276 = 1.073, $$p \leq 0.305$$; see Additional file 7) did not significantly associate with alpha diversity. There was no significant interaction between brood size manipulation and original brood size (ANOVA: F3, 48.053 = 0.126, $$p \leq 0.944$$; see Additional file 7). Results for Chao1 Richness were quantitatively similar: brood size manipulation did not affect alpha diversity (ANOVA: F3, 45.936 = 0.358 $$p \leq 0.784$$, Fig. 3; see Additional file 7). Nest of rearing explained a larger proportion of the observed variance in alpha diversity ($27.7\%$) than nest of origin ($10.8\%$), but the result was not statistically significant (Pr > χ2 = 1) (see Additional file 4).Fig. 3The gut microbiome alpha diversity of 7-day-old great tit nestlings across the four treatment groups visualized with two diversity metrics: A Shannon Diversity Index and B Chao1 Richness. The black dots represent each observation within a treatment group. The whiskers represent $95\%$ confidence intervals. Treatment groups are control (C), unmanipulated control (COU), enlarged (E), and reduced (R). $$n = 88$$ samples divided into treatment groups as follows: $C = 23$, COU = 21, $E = 20$, $R = 24$ Next, we tested whether the manipulated brood size as a continuous variable was associated with alpha diversity (Shannon Diversity Index), but found no significant association (ANOVA: F1, 63.001 < 0.001, $$p \leq 0.984$$; see Additional file 8) in this analysis either. Weight on day 7 post-hatch (ANOVA: F1, 82.840 = 0.015, $$p \leq 0.903$$; see Additional file 8) and hatching date (ANOVA: F1, 59.734 = 0.137, $$p \leq 0.713$$; see Additional file 8) did not correlate with alpha diversity in this model either. There was no significant interaction between manipulated brood size and weight on day 7 post-hatch (ANOVA: F1, 82.702 < 0.000, $$p \leq 0.998$$; see Additional file 8). Results for Chao1 Richness were quantitatively similar (ANOVA: F1, 65.064 = 0.246, $$p \leq 0.622$$; see Additional file 8): manipulated brood size did not affect alpha diversity, and neither did weight on day 7 post-hatch (ANOVA: F1 83.513 = 0.690, $$p \leq 0.409$$; see Additional file 8) nor hatching date (ANOVA: F1 57.110 = 1.133, $$p \leq 0.292$$; see Additional file 8). ## Short-term survival To explore whether alpha diversity associated with survival to fledging (i.e., short-term survival) and with apparent juvenile survival in Autumn 2020 (i.e., mid-term survival), we used generalized linear models with binomial model (v. 1.1-29; lme4 package, [91]), and then tested the significance of factors with type 2 ANOVA from the car package (v. 3.0-13; [93]). Type 2 ANOVA was used because the model did not contain interaction between predicting and there was no order between covariates, as they could not be ranked. Survival to fledging and recapture in Autumn 2020 were used as the binomial response variable (yes–no) in each model. Alpha diversity (Shannon Diversity Index and Chao1 Richness) was the main predicting variable, and weight on day 7 post-hatch (same time as sampling the fecal gut microbiome), hatching date and manipulated brood size were included as covariates in the model. We did not include brood size manipulation treatment in the survival models as not enough birds from each treatment group were recorded for fledging and juvenile survival. Moreover, we excluded random effects from this model as the model failed to converge. 65 nestlings fledged successfully, while 8 nestlings were found dead in nest boxes. For 15 nestlings we had no fledging record, so these were excluded from the survival to fledging analysis. In apparent juvenile survival, 19 birds out of 92 (with data on microbiome diversity) were recaptured as juveniles. For all analyses, the R package car (v. 3.0-13; [93]) was used to test Variance Inflation Factors (VIFs) and the package DHARMa (v. 0.4.5; [94]) to test model diagnostics for linear mixed-effects and generalized linear models. ## Beta diversity For visualizing beta diversity, i.e., the similarity or dissimilarity between the treatment group gut microbiomes, non-metric multidimensional scaling (NMDS) was used with three distance matrices: Bray–Curtis [95], weighted UniFrac, and unweighted UniFrac [96]. Permutational multivariate analysis of variance (PERMANOVA) using the Euclidean distance matrix and 9999 permutations was tested with the R package vegan (adonis2 function; v. 2.6-2; [97]) to investigate if any variables affected to the variation in gut microbiome composition. Nest of rearing was set as a blocking factor in the PERMANOVA to control for the non-desirable effects of the repeated sampling of foster siblings. The test for homogeneity of multivariate dispersions was used to measure the homogeneity of group dispersion values. We used the phyloseq package (v. 1.32.0; [84]) to run a differential abundance analysis with a significance cut-off $p \leq 0.01$ to test the differential abundance of ASVs between the treatment groups. Non-metric multidimensional scaling (NMDS) using weighted and unweighted UniFrac and Bray–Curtis dissimilarity did not show clear clustering of samples based on brood size manipulation treatment (see Additional file 3). The test for homogeneity of multivariate dispersions supported the visual assessment of the NMDS (Betadispersion9999 permutations: F3, 0.069 = 0.650, $p \leq 0.001$; see Additional file 12). Pairwise PERMANOVA further indicated that the treatment (PERMANOVA: R2 = 0.061, $F = 1.951$, $$p \leq 0.278$$; see Additional file 12), weight on day 7 post-hatch (PERMANOVA: R2 = 0.015, $F = 1.387$, $$p \leq 0.091$$) or hatching date (PERMANOVA: R2 = 0.0232, $F = 2.214$, $$p \leq 0.993$$) did not significantly contribute to the variation in gut microbiome composition between the treatment groups. Differential analysis of ASV abundance between the treatment groups showed that there is variation in taxa abundance. E group showed higher taxa abundance when compared to COU and C groups and was slightly higher than the R group. C and COU groups were generally lower in taxa abundance than R and E groups, and COU group showed lower abundance than the other groups in each comparison (see Additional file 13). ## The effects of brood size manipulation on nestling body mass Brood size manipulation did not significantly affect nestling body mass on day 7 post-hatch (ANOVA: F2, 25.832 = 0.441, $$p \leq 0.648$$; see Additional file 5). Moreover, there was no significant interaction between brood size manipulation and original brood size (ANOVA: F2, 24.610 = 0.678, $$p \leq 0.517$$; see Additional file 5). On day 14 post-hatch, brood size manipulation did not significantly affect nestling body mass (ANOVA: F2, 24.335 = 0.831, $$p \leq 0.448$$; see Additional file 5). However, body mass increased with increasing hatching date (ANOVA: F1 24.070 = 13.367, $$p \leq 0.001$$; see Additional file 5). Next, we did not find any significant associations between manipulated brood size and nestling body mass (ANOVA for weight on day 7: F1, 35.149 = 1.777, $$p \leq 0.191$$; ANOVA for weight on day 14: F1, 29.491 = 2.156, $$p \leq 0.153$$; see Additional file 6). Nest of origin explained a larger proportion of the variation in weight than the nest of rearing on both day 7 (nest of origin $41.1\%$ and nest of rearing $24.4\%$) and day 14 (nest of origin $65.5\%$ and nest of rearing $21.9\%$) post-hatch, but this result was not statistically significant (Pr > χ2 = 1) (see Additional file 4). ## Alpha diversity and short/mid-term survival Next, we explored whether alpha diversity (Shannon Diversity Index and Chao1 Richness) contributed to predicting short/mid-term survival (survival to fledging and apparent juvenile survival). Survival to fledging was not predicted by alpha diversity (Shannon Diversity Index: χ2 = 0.010, df = 1, $$p \leq 0.923$$; see Additional files 9 and 10), manipulated brood size (χ2 = 0.090, df = 1, $$p \leq 0.764$$; see Additional file 9), weight on day 7 post-hatch (χ2 = 0.388, df = 1, $$p \leq 0.533$$; see Additional file 9) or hatching date (χ2 = 0.438, df = 1, $$p \leq 0.508$$; see Additional file 9). Apparent juvenile survival was not significantly associated with alpha diversity (Shannon Diversity Index: χ2 = 1.916, df = 1, $$p \leq 0.166$$; see Additional file 9 and Additional file 10). Moreover, there was no significant interaction between alpha diversity and manipulated brood size (χ2 = 1.268, df = 1, $$p \leq 0.260$$; see Additional file 9). However, apparent juvenile survival was negatively associated with hatching date (χ2 = 4.654, df = 1, $$p \leq 0.031$$; see Additional file 9). Additional analyses to check for the consistency of results were tested the following way: survival to fledging with nestlings from the COU group removed and apparent juvenile survival without the nestlings with no recorded survival for fledging (see methods). These results were quantitatively similar as in the whole dataset for both Shannon Diversity Index (survival to fledging: χ2 = 2.285, df = 1, $$p \leq 0.131$$; apparent juvenile survival: χ2 = 1.515, df = 1, $$p \leq 0.218$$; see Additional file 11) and Chao1 Richness (survival to fledging: χ2 = 0.665, df = 1, $$p \leq 0.415$$; apparent juvenile survival: χ2 = 2.654, df = 1, $$p \leq 0.103$$; see Additional file 11). ## Discussion In this study, we investigated the associations between great tit nestling gut microbiome, brood size, and nestling body mass by experimentally manipulating wild great tit broods to either reduce or enlarge the original brood size. The results show that even though there was individual variation in the nestling gut microbiome (Fig. 2), brood size did not significantly contribute to gut microbiome diversity. Neither did gut microbiome diversity explain short-term (survival to nestling) nor mid-term (apparent juvenile) survival. Body mass was also not significantly affected by brood size manipulation. The COU group that functioned as a control for moving and handling effects, did not differ in this respect from the other groups. This suggests that human contact or handling nestlings 2 days post-hatch did not influence nestling gut microbiome or body mass. The partial cross-fostering design enabled us to disentangle the relative contributions of rearing environment (i.e., parents, nest and nestmates) from genetic, prenatal such as maternal allocation to egg, and early post-natal effects such as feeding up to day 2. Nest of rearing seemed to explain more of the variation in nestling gut microbiome diversity than the nest of origin (although not statistically significant), which follows previous studies. Contrastingly, nest of origin seemed to be a stronger contributor than the nest of rearing on nestling body mass on day 7 and day 14 post-hatch. This result was also not statistically significant. ## Brood size manipulation and nestling body mass First, we explored whether brood size was associated with nestling body mass, as such changes may explain the underlying patterns in gut microbiome [52]. Against our hypothesis, we found no significant association between nestling body mass and brood size: neither reduction nor enlargement of the broods resulted in significant body mass differences in the nestlings on day 7 and day 14 post-hatch. While the result is supported by some studies in which associations between nestling body mass and brood size have been tested [61, 98], the majority of the literature shows that brood size negatively correlates with nestling body mass: in larger broods nestlings are generally of lower mass [52, 53, 57, 67, 99–104]. There are a few possible explanations why brood size manipulation did not affect nestling body mass. Firstly, if environmental conditions were good, parents may have been able to provide enough food even for the enlarged nests and thus, variance in brood size may not result in differences in nestling body mass between reduced and enlarged nests. In that case the number of nestlings transferred between enlarged and reduced nests should probably have been larger to create differences in nestling body mass between the two treatments. Still, we think that the decision to transfer + 2/− 2 was reasonable since it was based on extensive evidence from previous studies [103]. Secondly, it could be that the enlarged brood size negatively influences some other physiological traits while body mass was retained at the expense of these other traits e.g., immune system functioning [105, 106]. Moreover, our analysis showed that hatching date had a significant effect on nestling body mass: nestlings that hatched later in the season were of lower weight. This could be a result of changes in the food items that great tits use, changes in temperature conditions or in parental investment during the breeding season. As the season progresses, the abundance of insect taxa varies, and this can result in changes in nutrient rich food [103, 107]. For example, great tits can select certain lepidopteran larvae that vary in their abundance during the great tit breeding season [108]. Thirdly, it could be that the change in brood size was influencing the parents’ condition instead of the nestlings [109, 110]. In enlarged broods, parents are required to forage more which can lead to higher energy expenditure and increased stress levels in parents [72, 73, 109]. ## Brood size manipulation and gut microbiome We found large inter-individual differences in gut microbiome diversity, yet this variation was not explained by brood size or nestling body mass. It is possible that brood size did not result in differences in food intake. For example, parents were likely able to provide an equivalent amount of food, given that body mass was not significantly affected by the brood size manipulation. Therefore, brood size manipulation did not affect gut microbiome diversity through differences in nutrient uptake. Alternatively, in this study, fecal sampling took place 5 days after the initial brood size manipulation (day 2 post-hatch). It could be that sampling on a later date or at multiple timepoints [61, 111] would have led to different results. Firstly, the time interval may not have been long enough to detect effects of the brood size manipulation. Secondly, it has been shown in previous studies that the nestling gut microbiome undergoes profound shifts at the nestling stage: overall gut microbiome diversity decreases but relative abundance in some taxa increases [52]. We suggest that fecal samples could be collected on multiple days post-hatch to understand the potential day to day changes in the nestling gut microbiome. Our results suggest that the variance in gut microbiome is a result of other factors than those linked to brood size. Firstly, one of these factors could be diet (i.e., food quality) which has gained attention in gut microbiome studies during the past years [25, 27, 112–115]. The overall diversity in gut microbiome could be explained by adaptive phenotypic plasticity because it is sensitive to changes in the environment e.g., changes in diet [116, 117]. The food provided by the parents can vary between broods in different environments [118], and this variation in diet can lead to differences in gut microbiome diversity [114–119]. For example, abundance in certain dietary items such as insects or larvae can result in lower gut microbiome diversity than other dietary items [113–116]. As great tits have been reported to adapt their diet along the breeding season due to changes in insect taxa frequency [103, 107] this could affect the between-nestling and between-nest gut microbiome diversity. However, using wild bird populations in gut microbiome studies limits the ability to control the consumed dietary items because parents may use variable food resources and there can be variance in dietary between sexes and even individuals. Visual assessment of dietary items [116] and metabarcoding could be of use here as they enable the identification of food items on genus and even species level from e.g., fecal samples [119]. Secondly, breeding habitat may lead to differences in gut microbiome diversity [120]: adult birds living in deciduous forests have shown to harbor different gut microbiome diversity than their counterparts living in open forested hay meadows. Here, we used a cross-fostering design to study if the rearing environment contributed to the variation in gut microbiome diversity: Our study indicated that the nest of rearing seemed to explain more of the gut microbiome variation than the nest of origin (although not significant), which follows some previous results [43, 44, 52]. For example, a study with great and blue tit (Cyanistes caerulaeus) nestlings showed that the nest of rearing contributed more to the gut microbiome than the nest of origin [43], and another study with the brown-headed cowbird (Molothrus ater) concluded that the sampling locality had a significant contribution to the gut microbiome [44]. Teyssier et al. [ 52] conducted cross-fostering at day 8 post-hatch in great tits and found that the nest of rearing influenced the gut microbiome more than the nest of origin. Additionally, parents can pass down their bill and feather microbiome through vertical transmission, which could influence nestling gut microbiome [20]. Results from beta diversity analysis were similar to that of alpha diversity: brood size manipulation did not contribute to the variation in gut microbiome composition. Overall, variation in gut microbiome composition could be a result of different genetic and environmental contributors. Firstly, great tit nestling gut microbiome composition could be explained by underlying genetic effects that we did not measure in this study. Phylosymbiosis i.e., the matching of gut microbiome composition to host genetic structure, could be explained by underlying genetics that may translate into physiological differences that affect the gut microbiome e.g., founder effects or genetic drift [121]. Davies et al. [ 14] found that MHC genes correlate with gut microbiome composition: the expression of specific alleles in the MHC genes was connected to the abundance of specific bacterial taxa such as Lactobacillales and Bacteroidales that influenced host health. In a study by Benskin et al. [ 41] captive zebra finches (Taeniopygia guttata) showed significant variation in gut microbiome composition between individuals even though their diet and housing conditions were standardized. The study suggested that individual homeostatic mechanisms linking to naturally occurring differences in individual gut microbiome could be why gut microbiome composition varied even with standardized housing conditions [41]. Secondly, gut microbiome composition could have been affected by the same environmental effects that may have linked to the variation in gut microbiome diversity: diet and feeding behavior [115, 116]. Differential analysis of ASV abundance showed variation in differential abundance of taxa between the treatment groups. However, several ASVs were not taxonomically assigned beyond family level making it difficult to draw conclusions about the significance of these results. All treatment groups had taxa belonging to the order Firmicutes, Proteobacteria and Actinobacteria, which was to be expected because they are usually the most core phyla in passerine gut microbiomes [33]. Nestlings belonging to E, R or C group showed higher taxa abundance than the COU group in each comparison. This result could be a result of the COU nestlings generally hatching later in the season and potentially having a less diverse diet [103, 107]. Of the E, R and C groups, C group was less abundant than E and R groups. Both E and R group showed high taxa abundance, which is interesting because we hypothesized that nestlings belonging to the E group would potentially experience less parental investment per nestling and have lower gut microbiome diversity and therefore, be less abundant [56, 57, 67, 68]. We did not observe differential abundance in e.g., the order Lactobacillales which would have been of interest, because the order hosts taxa that are beneficial for gut microbiome health [14, 62]. The genus Staphylococcus was* differentially abundant in the E group, but not in the other groups. Staphylococcus is a gram-positive genus of bacteria and known to cause infections in its host species [122]. Curiously, the COU group was differentially abundant in the genus Dietzia, which is a human pathogen [123]. ## Gut microbiome and short-term and mid-term survival Our results showed that gut microbiome diversity and brood size were not significantly associated with short-term (survival to fledging) or mid-term (apparent juvenile) survival. However, while a more diverse gut microbiome is considered a possible indicator of a healthy gut microbiome, the effects of the gut microbiome on the host health may often be more complex and related to specific taxa [9, 10]. For example, Worsley et al. [ 13] did not find a correlation between body condition and gut microbiome diversity, yet they found that specific taxa in the gut microbiome linked with individual body condition and survival. Not only environment, but also genetic background of the individual may contribute to gut microbiome and survival. In a study by Davies et al. [ 14], Ase-ua4 allele of the MHC genes was linked to lower gut microbiome diversity and it was suspected that the variation in the MHC genes could affect the sensitivity to pathogens that could lead to variation in gut microbiome diversity and eventually, host survival. To gain a better understanding of gut microbiome diversity and the contribution of different taxa to host survival, functional analyses of the gut microbiome should be included in gut microbiome studies. Different bacterial taxa can have similar functions in the gut microbiome [5, 124] and therefore, the absence of some taxa may be covered by other functionally similar taxa, resulting in a gut microbiome that is functionally more stable [125]. Similarity in functions may also contribute to host’s local adaptation e.g., to the changes in the host’s early-life environment [124]: changes in brood size or dietary items could result in variation in the gut microbiome diversity, yet there may be no effects on host body condition. The lack of association between brood size, nestling size and survival contrasts with previous studies, but it should be noted that the majority of previous studies have been done with adult birds and not nestlings. Because nestling gut microbiome is still quite flexible compared to that of the adults [20], it is possible that our experiment did not result in a strong enough effect on the gut microbiome. In future studies, it would be important to study the parents as well as it could be more likely to find an association between adult microbiome and fitness than with nestling gut microbiome and survival. Also, our sample size in the survival analyses was small, and it is hard to determine if the result was affected by the sample size. Firstly, nestling survival is often found to correlate with brood size and more specifically, with fledging mass and in particular, the ability to forage for food [61, 126]. Intra-brood competition may explain survival to fledging, as competition between nestlings can limit food availability and thus, leading to lower nestling body condition [68, 127]. A study with blackbirds (Turdus merula) showed that nestling body mass explained juvenile survival [128], and similar results have been shown with great tits and collared flycatchers (Ficedula albicollis; [31]). Contrastingly, Ringsby et al. [ 129] observed that in house sparrows (Passer domesticus) juvenile survival was independent of nestling mass and brood size. Moreover, natal body mass is often positively correlated with survival to fledging and juvenile survival as heavier nestlings are more likely to be recruited [92, 130, 131], yet we failed to demonstrate this in our study. Hatching date is also often positively correlated with fledging success [132] yet we did not find this association in our study, but instead found a significant association between hatching date and apparent juvenile survival. ## Conclusions Offspring condition can be affected by the early-life environment and early-life gut microbiome, thus highlighting the importance of understanding how changes in the rearing environment affect individual body mass and survival. Even though our results showed between-individual variation in nestling gut microbiome diversity, we did not find a significant link between brood size and nestling gut microbiome. Moreover, we did not find a significant association between nestling gut microbiome diversity and short-term or mid-term survival. This suggests that other environmental factors (e.g., diet quality) may contribute more to variation in nestling gut microbiome. Further research is needed to uncover the environmental factors that contribute to nestling gut microbiome in wild bird populations, and how gut microbiome may be linked to nestling survival. Gut microbiome can adapt faster to environmental changes than the host, which makes it important to understand the causes of inter-individual variation in microbiome, and how variation in microbiome possibly mediate adaptation to environmental changes. ## Supplementary Information Additional file 1: Brood size before and after manipulation: brood sizes between treatment groups were tested with a linear model to see if the differences were statistically significant. Additional file 2: Rarefaction curves for the unrarefied dataset. Species (ASVs) plateaued at about 5000 reads which was used as the rarefying depth. Additional file 3: Phylogenetic tree using the Newick-format. The tree describes the dissimilarity among the treatment groups. Each tip represents an individual sample, and each tip is colored and shaped based on treatment. Treatment groups are clustered using the UPGMA algorithm. Additional file 4: (A) Linear mixed effects model for gut microbiome diversity (Shannon Diversity Index and Chao1 Richness) and brood size manipulation treatment. ( B) Linear mixed effects model for GM diversity (Shannon Diversity Index and Chao1 Richness) and manipulated brood size. Additional file 5: A linear mixed effects model investigating the effects of brood size manipulation on nestling body mass on day 7 and day 14 post-hatch. Additional file 6: A linear mixed effects model investigating the effects of manipulated brood size on nestling body mass on day 7 and day 14 post-hatch. Additional file 7: A linear mixed effects model investigating the associations between alpha diversity (Shannon Diversity Index and Chao1 Richness) and brood size manipulation. Additional file 8: A linear mixed effects model investigating the association between alpha diversity (Shannon Diversity Index and Chao1 Richness) and manipulated brood size. Additional file 9: A generalized linear model exploration into alpha diversity’s (Shannon Diversity Index and Chao1 Richness) association with short-term (survival to fledging) and mid-term (apparent juvenile) survival. Additional file 10: The gut microbiome alpha diversity (Shannon Diversity Index and Chao1 Richness) and short-term survival. Additional file 11: Generalized linear model to measure the association between alpha diversity (Shannon Diversity Index and Chao1 Richness) survival to fledging and apparent juvenile survival. Additional file 12: Ordination of the gut microbial communities. 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--- title: 'Association of main meal quality index with the odds of metabolic syndrome in Iranian adults: a cross-sectional study' authors: - Amin Mirrafiei - Mohaddeseh Hasanzadeh - Fatemeh Sheikhhossein - Maryam Majdi¹ - Kurosh Djafarian - Sakineh Shab-Bidar journal: BMC Nutrition year: 2023 pmcid: PMC10031905 doi: 10.1186/s40795-023-00711-2 license: CC BY 4.0 --- # Association of main meal quality index with the odds of metabolic syndrome in Iranian adults: a cross-sectional study ## Abstract ### Background Metabolic syndrome (MetS) is a common global issue linked to the quality of one’s eating occasions. The current cross-sectional study evaluates the association between a novel index, the Main Meal Quality Index (MMQI), and MetS among Iranian adults. ### Methods A total of 824 men and women were recruited, and a 24-hour dietary recall assessed the dietary intake of the participants. Lunch was selected as the main meal based on energy density. The MMQI score was calculated based on ten components of dietary intake, with a higher score indicating more adherence to the index, with the final scores ranging from 0 to 100 points. The associations were assessed using binary logistic regression. ### Results The mean age was 42.2 years and the range of the calculated MMQI was 22 to 86 (mean in total participants: 56.62, mean in women: 56.82, mean in men: 55.64). The total prevalence of MetS in the sample was $34\%$. After adjustments for potential confounders, the participants at the top quartile of MMQI had a lower odds ratio for hypertriglyceridemia and low high-density lipoprotein (HDL) level, and a higher odds ratio for hypertension, hyperglycemia, abdominal obesity, and MetS. The sex-specific analysis also did not show any significant associations between adherence to MMQI and MetS and its components. ### Conclusion Overall, MMQI is not associated with MetS and its components in a sample of Iranian men and women. More research is needed to examine MMQI and its possible association with current health-related problems including MetS. ## Introduction Metabolic syndrome (MetS) is defined as a bundle of metabolic abnormalities consisting of abdominal obesity, impaired glucose tolerance, high blood pressure, an upraised triglyceride (TG) level, and a lessened high-density lipoprotein cholesterol (HDL-C) level [1] that increases the risk of type 2 diabetes (T2D) fivefold [2] and doubles the chance of cardiovascular disease (CVD) [3]. Diet, as a key part of behavior adjustment, is beneficial for all the components of MetS [4, 5], such as dyslipidemia [6], hypertension [7], adiposity [8], insulin resistance [9], and hyperglycemia [10, 11], mainly by contributing to the depletion of additional weight [5]. To interpret this more precisely, the risk of MetS, is predicted by indicators of diet quality that are utilized in research worldwide. Because there are so many different guidelines and requirements for the consumption of various nutrients, it is challenging to define clearly what constitutes a high-quality diet [12]. Based upon multiple types of research and estimations over time, several indices have been introduced to evaluate overall diet quality [13, 14]. These indices assess particular dietary patterns, such as the dietary inflammatory index (DII) [15], or guidelines presented at a regional level [16]. Among demographic and cultural differences, nutritional scores and indices have been developed for global populations, independent of social and racial circumstances. These scores aim to enhance the quality of eating when facing a specific condition or as a habitual diet guideline [17, 18]. Most of these recommendations have some components in common, for instance increasing the daily consumption of vegetables, fruits, fibers, and whole grains, and reducing the intake of saturated fats, processed foods, and sugary products [19]. On the other hand, focusing on the meals consumed by people might be a more straightforward method for understanding the significance of healthy intake in the prevention of chronic diseases, as has been shown through different studies on this particular issue (20–22). This led to the development of a novel indicator for assessing diet quality named the Main Meal Quality Index (MMQI), which is specifically designed for the main meal of the day [23], as dietary guidelines based on meals may be a useful tool in aiding people to maintain a healthy lifestyle due to their easy-to-understand and comprehensive nature [24, 25]. In addition, a single meal could be sufficient to promote health benefits [26]. No previous study has evaluated the association between meal quality and MetS in Iranian adults. Due to the high prevalence of MetS in Iranians ($30.4\%$) [27], this study aims to inspect the association between MMQI (in this case, lunch, as the main contributor to the total daily energy intake) and the probability of MetS and its components in Iranian adults for the first time. ## Study design and participants The current cross-sectional study was conducted on 824 healthy adults attending health centers affiliated with Tehran University of Medical Sciences in 2017–2018 who were sorted out by a multi-stage cluster random sampling method from the 5 regions (north, south, west, east, and center) of Tehran. Multiple health centers from each region were chosen, and qualified individuals from each center were selected by the mean of easy sampling, based on the proportion of the total number of people referring to a health center. The study sample size of 850 was calculated employing the formula: n = (pqz²)/E², contemplating that n = sample size; z² = square of the confidence level in standard error units (1.96); p = the estimate of the proportion of healthy adults; $q = 1$ − p, or the estimated proportion of people with metabolic syndrome; and E² = the square of the maximum allowance for error between the true proportion and the sample proportion (0.04) [28, 29]. The inclusion criteria were adults in the age range of 20 to 60 years, apparently healthy, eager to participate in the study, a member of a health center, and a resident of Tehran. They were informed about the purpose of the study and filled out the consent to participate in it. The exclusion criteria consisted of a history of diabetes, cancer, and CVD, a possible change to the usual diet before participation, and lactating and pregnant women. A demographic questionnaire consisting of age, sex, education, marital status, occupation, and smoking status was used by experienced interviewers to gather and record general information about the subjects. ## Anthropometric measures and blood pressure We used a stadiometer with a sensitivity of 0.1 cm (Seca, Hamburg, Germany) and a digital scale instrument with a precision of 0.1 kg (808Seca; Seca) to evaluate anthropometric measures compromising body height and weight. Participants were dressed in light clothing without shoes. Body Mass Index (BMI) was calculated individually and participants were divided into four categories of underweight (< 18.5), normal (≤ 18.5–24.9≥), overweight (≤ 25-29.9≥), and obese (≥ 30) [30]. Waist and hip circumferences were measured between the iliac crest and lower ribs by a flexible measuring tape. Physical activity was evaluated using a validated International Physical Activity Questionnaire (IPAQ) [31]. Systolic and diastolic blood pressure were evaluated in a sitting stance by a digital sphygmomanometer (BC 08; Beurer, Ulm, Germany) after a resting time of ten to fifteen minutes. Blood pressure was measured twice in each participant and the average amount was entered into the analysis. ## Dietary assessment The dietary intake of the participants was recorded using a 24-hour recall questionnaire on three non-consecutive days. A trained dietician collected the first recall via a face-to-face interview, and the next two recalls were gathered by phone calls on random days of the week. Meals, energy, and food groups were determined by the dietary recalls, and micro and macronutrients were extracted utilizing the Nutritionist IV software. ## Meal definitions Meals were known as occasions where large amounts of food were consumed or were standardized based on time of consumption [32, 33] to contain no more than one breakfast, lunch, and dinner, but allow for multiple snacks. Based on prior studies, breakfast was defined as an eating occasion where a large amount of food or energy was consumed between 5:00 and 11:00; lunch, if it was consumed between 11:00 and 16:00; and dinner, if it was eaten between 16:00 and 23:00 [34]. ## Calculating MMQI The main meal of the day, lunch, was selected based on its contribution to total calorie intake, and the MMQI was evaluated by the standards stated by Gorgulho et al. The components and scoring system are expressed in Table 1 [23]. The MMQI is based on 10 components: fruit, vegetables (except potatoes), animal protein/total protein ratio, fiber, carbohydrates, total fat, saturated fat, processed meat, sugary beverages and desserts, and energy density. A score range of 0 to 10 points is clarified for every single component; thus, the final score varies between 0 and 100 points for each individual. To get the maximum score, an individual must consume at least 80 grams of fruit and 160 grams of vegetables during the main meal. More than $20\%$ of the protein intake must come from plant sources, and a minimum of 10 grams of dietary fiber should be consumed. Based on the WHO recommendation, total carbohydrates ought to supply above $55\%$ of total energy intake (maximum $75\%$), total fat below $30\%$ of total energy intake (minimum $15\%$), and saturated fat lower than $10\%$ of total energy intake. Complete avoidance of sugary beverages, desserts, and processed meats is considered optimal, and lastly, an energy density of less than 1.25 kcal/g is applied for a perfect MMQI score. The correlation between MMQI and determined nutrient intake of the lunch meal was assessed using univariate linear regression, adjusting for age and sex. Table 1Mean MMQI scores and distribution of adults in MMQI categories according to socioeconomic, demographic and anthropometric characteristicsCharacteristicsMean$95\%$ CIPTotal population (n)1st tertile (n)2nd tertile (n)3rd tertile (n)P valueOverall population56.6255.85–57.37-824275275274-SexMen55.5953.74–57.450.2311424849450.90Women56.8255.99–57.66682227226229Body weight statusUnderweight49.9045.42–54.380.167107210.19Normal56.4955.14–57.83270958590Overweight56.3755.18–57.56332111114107Obese57.4655.94–58.99212627476EducationIlliterate57.9354.96–60.900.346551516240.66Under-diploma56.9055.35–58.46195656565Diploma57.0755.73–58.41286939598Academic55.7054.44–56.962881029987OccupationEmployed55.9954.73–57.260.295302104103950.81Housekeeper57.2456.20-58.28431139143149Retired56.9153.48–60.3547131717Unemployed54.6350.98–58.2843181213Marital statusSingle55.5453.42–57.660.5401114038330.96Married56.8856.04–57.72664218222224Divorced56.5048.21–64.7912435Widowed55.0350.89–59.1637131212SmokingNon-smokers56.5855.80-57.350.6547962662662840.74Smokers57.5453.56–61.52289910Mean and $95\%$ confidence intervals ($95\%$ CI) are described and p-value between groups using ANOVA. Number of participants in each category of MMQI were evaluated using Pearson’s chi-square. ## Laboratory investigations Each participant provided a 12-hour fasting blood sample for the quantification of fasting plasma glucose (FPG), TG, and HDL. Blood samples were measured by standard methods at the Nutrition and Biochemistry Laboratory of the School of Nutritional Sciences and Dietetics at Tehran University of Medical Sciences. Glucose was assayed by the enzymatic (glucose oxidase) colorimetric method. Commercial kit (Pars Azmoon, Tehran, Iran). Serum total cholesterol (TC) and high-density lipoprotein-cholesterol (HDL-C) were measured using a cholesterol oxidase phenol amino antipyrine method, and triglyceride (TG) was measured using a glycerol-3 phosphate oxidase phenol amino antipyrine enzymatic method. All these tests were done by commercial kits (all from Pars Azmoon, Iran) using an auto-analyzer system (Selectra E, Vitalab, the Netherlands). ## Metabolic syndrome definition We used the criteria of the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) to define MetS. As per the guideline, MetS is explained as the presence of three or more of the following criteria: 1) increased waist circumference (WC) (> 102 cm [> 40 in] for men, > 88 cm [> 35 in] for women); 2) elevated TG (≥ 150 mg/dl); 3) low HDL cholesterol (< 40 mg/dl in men, < 50 mg/dl in women); 4) hypertension (≥ 130/≥85 mmHg); and 5) impaired fasting glucose (≥ 110 mg/dl) [35]. ## Statistical analysis We utilized the Statistical Package for the Social Sciences (SPSS version 26; SPSS Inc., Chicago, IL, USA) to carry out all of the statistical analyses. The $p \leq 0.05$ was considered significant. We used a one-way analysis of variance test (ANOVA) to compute the mean of the participants for every component of the MMQI, and the association between MMQI and nutrient intake was assessed by linear regression, adjusting for age and sex. Subsequently, the MMQI score was divided into tertiles and the general characteristics of subjects among tertiles of the index score were compared using Chi-square for qualitative variables and the ANOVA test for continuous variables. The mean level of the biochemical parameters was also compared across tertiles of MMQI using analysis of covariance (ANCOVA). We used binary logistic regression to assess the relationship between MMQI and the likelihood of MetS in the crude Model, Model 1, adjusted for education, occupation, marital status, smoking status, and activity score, and Model 2, additionally adjusted for sex, age, and mean energy intake. The first tertile of the MMQI was considered the reference category. Due to the sex differences in etiology, biology, and clinical expression of MetS, we conducted the analyses on men and women separately. ## Results Twenty-six participants dropped out due to under or over-reporting of energy intake, and the final sample size of 824 people entered the concluding analysis (Fig. 1). The majority of the sample size were women, non-smokers, the married, housekeepers, with an academic degree. The mean age of the participants was 42.2 ± 10.5 years. Table 1 shows the general characteristics of participants across MMQI categories. Women had a higher average score of MMQI compared to men ($$P \leq 0.23$$). Also, obese participants ($$P \leq 0.17$$), smokers ($$P \leq 0.65$$), married participants ($$P \leq 0.54$$), illiterates ($$P \leq 0.35$$), and housekeepers ($$P \leq 0.29$$) possessed a higher mean MMQI score than others in their subgroup, but none of them were significant. Figure 1Flow diagram of study participants The standard for scoring and the mean of each component of the MMQI in the study population is presented in Table 2. The 25th, 50th, and 75th percentiles of MMQI score for every dietary component are also shown in Table 2. Table 2Main Meal Quality Index components, standards for scoring and average valuesComponentsStandard for maximum score [10]Standard for minimum score [0]MeanSEMLower CIUpper CIP 25P 50P 75Fruit≥ 8000.550.0340.490.620.000.001.00Vegetable (excluded potato)≥ 160≤ 800.960.0760.821.100.000.000.00Animal protein/ total protein≤ $80\%$$100\%$8.630.1158.398.8710.0010.0010.00Fiber≥ 10≤ 72.760.1372.483.060.000.006.00Carbohydrate≥ $55\%$ of total energy≤ 40 of total energy5.710.1245.465.933.006.009.00Total fat≤ $30\%$ of total energy≥ $40\%$ of total energy5.570.1395.285.841.006.0010.00Saturated fat≤ $10\%$ of total energy≥ $13\%$ of total energy8.430.1158.208.6510.0010.0010.00Processed meat0 portion1 portion (190 kcal)9.700.0459.619.7810.0010.0010.00Sugary beverages and desserts0 portion1 portion (110 kcal)9.470.0469.379.5510.0010.0010.00Energy density≤ 1.25 kcal/ gr≥ 1.65 kcal/gr4.830.1584.535.130.005.0010.00Final score--56.620.38855.8557.3749.0057.0065.00Abbreviations: SEM, standard error of the mean Results of Table 3 shows that the final score was positively associated with carbohydrates ($P \leq 0.001$), calcium ($$P \leq 0.01$$), vitamin A ($$P \leq 0.04$$), and vitamin C ($P \leq 0.001$), and was negatively associated with the energy ($P \leq 0.001$), protein ($P \leq 0.001$), total fat ($P \leq 0.001$), saturated fat ($P \leq 0.001$), cholesterol ($P \leq 0.001$), polyunsaturated fat (PUFA) ($P \leq 0.001$), monounsaturated fat (MUFA) ($P \leq 0.001$), zinc ($P \leq 0.001$) and sodium ($$P \leq 0.01$$). Table 3Association between MMQI and nutrient intakeNutrientsβSE$95\%$ CIP valueEnergy (kcal)-2.520.53-3.56 to -1.47< 0.001Carbohydrate (g)0.400.070.26 to 0.54< 0.001Protein (g)-0.110.02-0.15 to -0.06< 0.001Total fat (g)-0.410.02-0.46 to -0.36< 0.001Saturated fat (g)-0.110.01-0.13 to -0.09< 0.001Cholesterol (mg)-1.170.19-1.54 to -0.80< 0.001Polyunsaturated fat (g)-0.090.01-0.11 to -0.07< 0.001Monounsaturated fat (g)-0.130.02-0.18 to -0.09< 0.001Zinc (mg)-0.010.003-0.02 to -0.008< 0.001Selenium (mcg)0.000.006-0.01 to 0.010.99Iron (mg)0.020.02-0.03 to 0.070.43Calcium (mg)1.180.370.46 to 1.900.01Sodium (mg)-4.141.24-6.57 to -1.700.01Vitamin A (RE)1.590.790.30 to 3.140.04Vitamin E (mg)-0.02-0.01-0.03 to 0.0010.07Vitamin C (mg)0.300.070.15 to 0.44< 0.001Abbreviations: CI, confidence interval; MMQI, Main Meal Quality Index. Linear regression between MMQI and each nutrient adjusted by gender and ageobtained by linear regression analysis Metabolic biomarkers of the study participants across the tertiles of the MMQI are presented in Table 4. After adjusting for sex, age, occupation, marriage, smoking, energy intake, physical activity, and education, subjects in the highest quartile had a higher value of TC, LDL, HDL, SBP, DBP, and BMI. Also, compared with the participants in the first tertile of the MMQI, those in the top quartile had a lower value of FPG and TG, while WC was the same value between the first and the last tertile, although only DBP showed statistically significant results ($$P \leq 0.03$$). Table 4Metabolic biomarkers of participant according to MMQI tertile1st tertile2nd tertile3rd tertileP valueFPG (mg/dl)108.3 ± 2.12106.8 ± 2.12108.1 ± 2.130.86TG (mg/dl)150.1 ± 4.62137.2 ± 4.61146.9 ± 4.640.12TC (mg/dl)195.0 ± 2.64194.0 ± 2.63198.4 ± 2.650.46LDL (mg/dl)115.1 ± 2.27116.8 ± 2.27118.8 ± 2.280.51HDL (mg/dl)49.8 ± 0.6049.7 ± 0.6050.2 ± 0.600.84SBP (mmHg)118.0 ± 0.87116.9 ± 0.87119.4 ± 0.870.14DBP (mmHg)79.5 ± 0.5777.6 ± 0.5779.2 ± 0.570.03WC (cm)89.3 ± 0.6689.0 ± 0.6689.3 ± 0.660.93BMI (kg/m²)27.0 ± 0.2627.4 ± 0.2627.3 ± 0.260.54FPG Fasting Plasma Glucose, TG triglyceride, TC total cholesterol, LDL low density lipoprotein, HDL high density lipoprotein, SBP systolic blood pressure, DBP diastolic blood pressure, WC waist circumference, BMI body mass indexValues are based on mean ± standard erroradjusted for sex, age, occupation, marriage, smoking, energy intake, physical activity, and education Multivariate adjusted odds ratios and $95\%$ confidence intervals for metabolic syndrome and its components across tertiles of MMQI in the total population, women, and men are provided in Table 5. In the total population, those who were in the highest tertile of the MMQI were more likely to have MetS (OR: 1.04; $95\%$ CI: 0.70,1.54; $$P \leq 0.86$$), hypertension (OR: 1.04; $95\%$ CI: 0.63,1.69; $$P \leq 0.91$$), hyperglycemia (OR: 1.15; $95\%$ CI: 0.81,1.64; $$P \leq 0.42$$), and greater abdominal obesity (OR: 1.14; $95\%$ CI: 0.72,1.79; $$P \leq 0.58$$), and a lower risk of hypertriglyceridemia (OR: 0.92; $95\%$ CI: 0.61,1.25; $$P \leq 0.43$$) and low HDL-C level (OR: 0.97; $95\%$ CI: 0.68,1.37; $$P \leq 0.86$$), compared with participants in the lowest tertile of MMQI after adjustment of possible confounders. In the sex subgroups, women in the highest tertile had a lower OR for hypertriglyceridemia (OR: 0.88; $95\%$ CI: 0.60,1.13; $$P \leq 0.50$$) and low HDL (OR: 0.92; $95\%$ CI: 0.64,1.34; $$P \leq 0.70$$), and a higher OR for MetS (OR: 1.19; $95\%$ CI: 0.79,1.78; $$P \leq 0.41$$), hypertension (OR: 1.06; $95\%$ CI: 0.63,1.80; $$P \leq 0.87$$), abdominal obesity (OR: 1.36; $95\%$ CI: 0.92,2.01; $$P \leq 0.14$$), and hyperglycemia (OR: 1.15; $95\%$ CI: 0.78,1.68; $$P \leq 0.49$$) in the fully adjusted model. In men, high adherence to MMQI was associated with a lower chance of MetS (OR: 0.43; $95\%$ CI: 0.14, 1.39; $$P \leq 0.18$$) and all of its components. None of these associations in Table 5 were statistically significant in any model. Table 5Multivariate adjusted odds ratios and $95\%$ confidence intervals for metabolic syndrome and its components across tertiles of MMQI1st tertile2nd tertile3rd tertileP trendHypertriglyceridemia (total)Crude1.000.76 (0.54,1.07)0.85 (0.60,1.20)0.34Model 11.000.71 (0.50,1.02)0.86 (0.60,1.23)0.39Model 21.000.70 (0.49,1.00)0.92 (0.61,1.25)0.43Hypertriglyceridemia (women)Crude1.000.84 (0.57,1.24)0.85 (0.58,1.25)0.40Model 11.000.81 (0.54,1.19)0.87 (0.59,1.28)0.47Model 21.000.80 (0.53,1.18)0.88 (0.60,1.13)0.50Hypertriglyceridemia (men)Crude1.000.64 (0.29,1.42)0.96 (0.42,2.20)0.91Model 11.000.63 (0.28,1.41)0.95 (0.41,2.19)0.90Model 21.000.60 (0.26,1.38)0.96 (0.42,2.23)0.92Hypertension (total)Crude1.000.64 (0.39,1.05)1.00 (0.64,1.58)0.98Model 11.000.56 (0.33,0.95)1.07 (0.66,1.74)0.80Model 21.000.56 (0.33,0.95)1.04 (0.63,1.69)0.91Hypertension (women)Crude1.000.65 (0.37,1.12)1.01 (0.62,1.66)0.98Model 11.000.58 (0.33,1.02)1.10 (0.65,1.86)0.76Model 21.000.57 (0.32,1.02)1.06 (0.63,1.80)0.87Hypertension (men)Crude1.000.66 (0.21,2.08)0.77 (0.24,2.43)0.64Model 11.000.56 (0.17,1.85)0.64 (0.19,2.14)0.46Model 21.000.61 (0.18,2.06)0.64 (0.19,2.18)0.48Hyperglycemia (total)Crude1.001.06 (0.76,1.48)1.13 (0.81,1.58)0.46Model 11.001.02 (0.72,1.44)1.17 (0.83,1.67)0.36Model 21.001.02 (0.72,1.45)1.15 (0.81,1.64)0.42Hyperglycemia (women)Crude1.000.99 (0.68,1.43)1.12 (0.78,1.62)0.54Model 11.000.94 (0.64,1.38)1.17 (0.80,1.71)0.42Model 21.000.94 (0.64,1.38)1.15 (0.78,1.68)0.49Hyperglycemia (men)Crude1.001.15 (0.52,2.56)0.85 (0.37,1.96)0.71Model 11.001.33 (0.58,3.07)0.94 (0.39,2.25)0.91Model 21.001.44 (0.60,3.41)0.94 (0.39,2.28)0.91Low HDL-C (total)Crude1.001.23 (0.88,1.71)0.92 (0.66,1.29)0.63Model 11.001.26 (0.89,1.78)0.97 (0.69,1.37)0.87Model 21.001.26 (0.89,1.78)0.97 (0.68,1.37)0.86Low HDL-C (women)Crude1.001.25 (0.87,1.82)0.95 (0.66,1.37)0.81Model 11.001.27 (0.87,1.85)0.93 (0.64,1.34)0.72Model 21.001.27 (0.87,1.84)0.92 (0.64,1.34)0.70Low HDL-C (men)Crude1.001.29 (0.56,2.95)0.73 (0.30,1.78)0.51Model 11.001.41 (0.60,3.29)0.73 (0.29,1.83)0.52Model 21.001.50 (0.63,3.58)0.71 (0.28,1.80)0.50Abdominal obesity (total)Crude1.000.98 (0.70,1.37)1.17 (0.83,1.63)0.36Model 11.000.87 (0.56,1.36)1.17 (0.75,1.84)0.49Model 21.000.87 (0.56,1.36)1.14 (0.72,1.79)0.58Abdominal obesity (women)Crude1.000.97 (0.67,1.40)1.28 (0.89,1.85)0.19Model 11.000.88 (0.60,1.31)1.41 (0.96,2.08)0.09Model 21.000.89 (0.60,1.33)1.36 (0.92,2.01)0.14Abdominal obesity (men)Crude1.001.04 (0.40,2.75)0.47 (0.15,1.52)0.23Model 11.000.93 (0.34,2.50)0.42 (0.13,1.37)0.16Model 21.000.76 (0.27,2.17)0.41 (0.12,1.42)0.16Metabolic syndrome (total)Crude1.000.98 (0.68,1.40)1.02 (0.71,1.46)0.90Model 11.000.89 (0.60,1.31)1.06 (0.71,1.57)0.78Model 21.000.89 (0.60,1.32)1.04 (0.70,1.54)0.86Metabolic syndrome (women)Crude1.001.06 (0.72,1.56)1.15 (0.78,1.68)0.48Model 11.000.99 (0.66,1.49)1.22 (0.82,1.83)0.33Model 21.000.99 (0.66,1.49)1.19 (0.79,1.78)0.41Metabolic syndrome (men)Crude1.001.15 (0.46,2.90)0.52 (0.17,1.54)0.27Model 11.001.20 (0.45,3.15)0.46 (0.15,1.44)0.20Model 21.001.29 (0.48,3.50)0.43 (0.14,1.39)0.18Obtained by logistic regression analysisModel 1 adjusted for age, sex (for total population), mean energy intake, smoking and physical activity, body mass indexModel 2 additionally adjusted for occupation, education and marriage ## Discussion In this study, no significant relationship was found between MMQI and MetS and its components, except for DBP. Furthermore, stratification by sex showed that MMQI was not associated with the MetS in men or women. MMQI is one of the indexes that can be used to assess the quality of meals consumed by different populations. It helps to compare the quality of diets independently of cultural and social contexts between countries around the world [36]. To the best of our knowledge, no study has been conducted to investigate the association between MMQI and MetS. However, accumulating evidence has been studied on the relationship between the quality of a habitual diet and chronic diseases. According to a cross-sectional study by Saraf-Bank et al., performed on 1036 Iranian women, participants with a higher Healthy Eating Index (HEI) score had a $28\%$ lower chance of developing MetS [37]. Compared to the MMQI, HEI evaluates the overall diet based on 9 adequacy components and 4 moderation components that may not be adapted well for the main meals and is quite weakly correlated with MMQI. In a cohort study on 339 participants, after controlling for potential confounders, adherence to Alternative HEI (AHEI) was associated with MetS reversion, particularly in those with central obesity and those with high triglycerides [38]. In another cohort study on 8719 healthy adults, HEI, Recommended Foods Score (RFS), and Dietary Diversity Score for recommended foods (DDS-R) were all strong independent negative predictors of BMI, plasma glucose, and hemoglobin A1C. The RFS and DDS-R were also inversely related to blood pressure and serum cholesterol [39]. DDS-R is used to assess the diversity within food groups based on a healthy and balanced diet in various age groups that are calculated based on the consumption of different food groups including grains, meat, vegetables, fruits, and dairy products which are partially in common with MMQI [40]. RFS is calculated using a minimum intake of at least half a serving of one food item from each of the cereals and white roots, green leafy vegetables, other vegetables and fruits, vitamin A-rich vegetables and fruits, organ meat, meat, fish and seafood, eggs, nut, seeds and legumes, milk, and dairy food groups [41]. Unlike MMQI, DDS-R and RFS are indicators of the overall diet quality and are not meal-specific. In another cross-sectional study conducted on 300 obese Iranian adults, lower Diet Quality Index-International scores accompanied by food insecurity were associated with a higher risk of MetS, hypertriglyceridemia, reduced HDL, and increased blood pressure [42]. In an article published by Felix et al. on a sample of Brazilian adults and the elderly, Breakfast Quality Index was associated with lower odds of cardiometabolic risk factors and MetS [43]. The BQI was specifically developed based on the frequent foods that are eaten at breakfast to assess the nutritional quality of breakfasts in children and adolescents and is not available for other types of meals. In contrast, there was no significant association between the dietary phytochemical index (DPI) and the odds of MetS and other components of MetS in adults based on a cross-sectional study [44]. DPI has many components similar to MMQI including the antioxidant-rich food groups fruits, and vegetables, but is not exclusively for meals [45]. Discrepancies in the findings of studies may be related to the effect of circadian rhythm on metabolism. It seems that changes in food composition or feeding time may result in a differential response of the circadian clock. A meta-analysis by Tian et al. showed that fruit and vegetable intakes, two components of the MMQI, were inversely associated with the risk of MetS [46]. Other meta-analyses on observational studies by Zhang and Zhang, and Lee et al. confirmed the former results [47, 48]. Another meta-analysis by Chen et al. found a negative association between dietary fiber intake and MetS [49]. Other components of the MMQI also were influential on the prevalence of MetS per previous research [50, 51]. We also did not detect any significant difference between men and women in the association of MMQI and MetS. Although sex has a significant role in determining biomarker levels of MetS and dietary behavior, sex consideration in the creation of indices related to nutrition and biomarkers is not fully studied [52]. Based on the evidence, the effect of sex hormones such as progesterone, testosterone, and estrogen on appetite, energy metabolism, and eating behavior might cause a difference in the obesity prevalence of men and women [53]. Furthermore, the under-representation of each sex may have an impact on the observed outcome [54]. We found no significant association between the meal-based quality index and the components of MetS. Since studies regarding meal quality and adverse outcomes are quite scarce, this study could be of greater value for future research. Recently, we reported that eating occasions and snack frequency, regardless of diet quality, increased the risk of MetS [55]. Based on the socioeconomic findings of our study, women, illiterate participants, smokers, housekeeping wives, and married individuals had a higher quality of lunch, insignificantly. In a survey of Spanish workers, being male and smoking tobacco was associated with a lower-quality of diet [56]. In another study on Iranian adults, a higher quality of eating was positively associated with education, being a woman, and reversely associated with smoking and marriage [57]. Although these results are insignificant, higher consumption of junk foods in restaurants as lunch might interpret the employees’ lower quality of lunch compared to housekeepers. Also. It seems that based on the existing economical gap in the society of Tehran, those who are wealthier, typically smoke more often, but in turn, have a higher meal quality. In a study, we found that higher daily energy irregularity was linked to poorer consumption of fruits, vegetables, legumes, low-fat dairy products, and chicken, as well as higher consumption of soft drinks, processed meat, and nuts, and overall, a worse total DASH diet score and HEI-2015 [58]. Furthermore, Augustina et al. in a cross-sectional survey of 335 school-going adolescent girls aged 12–19 years from Indonesia, reported an improvement in nutritional quality and diversity in a regular meal pattern by highlighting meal frequency and meal skipping [59]. In another study by Gorgulho et al., it was revealed that the main meals consumed by adolescents, adults, and the elderly are not nutritionally adequate by assessing nutritional quality of the main meals, especially when consumed outdoors [23]. Meals appear to be a major driver of nutrient intake and diet quality. This could be attributed in part to the meal’s structural properties. Meals may have distinct effects on food intake, and eating patterns are complex as they are all linked to the risk factors for cardiovascular disease, and reduced nutritional intake [60]. There was a negative significant association between energy, protein, total fat, saturated fat, cholesterol, polyunsaturated fat, monounsaturated fat, zinc, and sodium intakes, and MMQI scores. We also found a positive significant association between carbohydrate intake, calcium, vitamin A, and vitamin C and MMQI scores. A marginally significant relationship between vitamin E intake and MMQI was found. Previous studies have demonstrated the inverse associations of fruits, vegetables, and MetS [61, 62]. High consumption of fruits and vegetables is significantly associated with a reduction in MetS [62]. In this regard, this association is mediated by the high content of fiber, phytochemicals, and antioxidants in fruits and vegetables [63]. Besides the well-known effects of energy density and sugars on fats on MetS, it seems that multiple bioactive substances found in each meal, such as polyphenols and fibers, act as health boosters. Polyphenols, concentrated in a large amount in vegetables and fruits that have anti-inflammatory and antioxidant properties, are a fantastic way to improve the quality of the meal. By reducing the overproduction of reactive oxygen species and suppressing free radicals, polyphenols regulate cellular and enzymatic processes involved in inflammatory pathways and play a role in glucose homeostasis as well as decreasing apoptosis and increasing pancreatic-cell proliferation, although we did not observe it in our results [64]. In the present study, the large sample size is a significant advantage, and an accurate assessment of the disorder is another strength of this study. Also, we used multiple 24-h dietary recalls. There were some limitations when interpreting the findings. The main limitation is the inability to prove causality due to the cross-sectional design of the study. Certainly, prospective cohort studies are needed to provide evidence for a causal relationship. Another concern is incorrect classification. Like other epidemiological studies, the findings of this study may not be generalizable due to the nature of the study population. Also, the 24-hour food recall may be erroneous because it is self-reported. Furthermore, because of the economic condition in Iran, most people are obligated to gain their daily protein from plant sources that are significantly cheaper than animal sources, which can cause a false increase in the MMQI score. Due to the existence of several clinical definitions of MetS, the findings may change as the MetS definition changes. It should be noted that in the present study, we used an updated definition of the Joint Scientific Statement. On the other hand, the determined waist values for abdominal obesity in Iran have been obtained from small cross-sectional studies on non-demonstration samples [65]. In this study, we used international waist circumference cutoff points to ascertain central obesity. This matter might have a minor effect on the findings. ## Conclusion MMQI is a new index designed to evaluate the quality of the main meal of the day. 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--- title: An indirect comparison of 144-week efficacy, safety, and tolerability of dolutegravir plus lamivudine and second-generation integrase inhibitor–based, 3-drug, single-tablet regimens in therapy-naive people with HIV-1 authors: - Lee A. Evitt - Sakina Nanji - Richard A. Grove - Chinyere Okoli - Jean van Wyk - Sonya J. Snedecor journal: AIDS Research and Therapy year: 2023 pmcid: PMC10031916 doi: 10.1186/s12981-023-00507-1 license: CC BY 4.0 --- # An indirect comparison of 144-week efficacy, safety, and tolerability of dolutegravir plus lamivudine and second-generation integrase inhibitor–based, 3-drug, single-tablet regimens in therapy-naive people with HIV-1 ## Abstract ### Background The long-term efficacy and safety of the 2-drug regimen dolutegravir (DTG) + lamivudine (3TC) and 3-drug single-tablet regimens recommended for antiretroviral therapy (ART)-naive people with HIV-1 (PWH) have yet to be compared directly in clinical trials. This indirect treatment comparison (ITC) was conducted to compare the durability of efficacy and long-term safety of DTG + 3TC vs second-generation, integrase strand transfer inhibitor (INSTI)-based, 3-drug, single-tablet regimens bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and DTG/abacavir/3TC (DTG/ABC/3TC) at Week 144 after treatment initiation. ### Methods A systematic literature review identified 4 trials evaluating the treatment regimens of interest in ART-naive PWH (GEMINI-1, GEMINI-2, GS-US-380-1489, and GS-US-380-1490). Safety, efficacy, and tolerability results were compared using fixed-effects Bucher ITC methodology to calculate relative outcomes. ### Results Rates of virologic suppression (HIV-1 RNA < 50 copies/mL, US Food and Drug Administration Snapshot analysis) and virologic failure (HIV-1 RNA ≥ 50 copies/mL) as well as mean change in CD4 + cell count were similar with DTG + 3TC, BIC/FTC/TAF, and DTG/ABC/3TC at Week 144. Serious adverse events occurred less frequently with DTG + 3TC compared with both BIC/FTC/TAF (odds ratio [OR], 0.51; $95\%$ CI 0.29–0.87; $$P \leq 0.014$$) and DTG/ABC/3TC (OR, 0.38; $95\%$ CI 0.19–0.75; $$P \leq 0.006$$). Discontinuations and overall adverse events were similar across all 3 regimens. ### Conclusions These results suggest that the 2-drug regimen DTG + 3TC offers comparable and durable efficacy with fewer serious adverse events vs BIC/FTC/TAF and DTG/ABC/3TC through 144 weeks of treatment in ART-naive PWH. These long-term comparative data support the therapeutic value of DTG + 3TC for PWH. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12981-023-00507-1. ## Introduction HIV treatment guidelines published by the US Department of Health and Human Services (DHHS) in 2021, the International Antiviral Society–USA (IAS) in 2020, and the European AIDS Clinical Society (EACS) in 2020 recommend integrase strand transfer inhibitor (INSTI)-based antiretroviral therapy (ART) with either 1 or 2 nucleoside reverse transcriptase inhibitors for most previously untreated adults and adolescents [1–3]. Considering the requirement for lifelong ART, the high prevalence of comorbidities among people with HIV (PWH), and the toxicities associated with ART, 2-drug regimens that maintain efficacy comparable to that of 3-drug regimens are of considerable interest and potential value, offering the possibility of reduced cumulative drug exposure, adverse effects, and potential drug interactions [1, 2, 4]. The 2-drug regimen dolutegravir (DTG) + lamivudine (3TC) demonstrated non-inferior efficacy, a high barrier to resistance, and a comparable safety profile relative to the 3-drug regimen DTG + tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) at 144 weeks after treatment initiation in the GEMINI-1 and GEMINI-2 trials in ART-naive PWH [5]. Notably, there was a lower risk of drug-related adverse events (AEs) through Week 144 with DTG + 3TC than DTG + TDF/FTC in the pooled safety population from these 2 studies [5]. Efficacy and safety of the recommended 3-drug ART regimens have also been well established [1, 2]. Although DTG in combination with 3TC has been directly compared with DTG + TDF/FTC in ART-naive PWH, this 2-drug regimen has not been directly compared with other second-generation INSTI-based, 3-drug combinations in randomized clinical trials, ie, those containing bictegravir [5]. In the absence of randomized clinical trial data, indirect treatment comparisons can provide valuable supplementary information for clinicians, PWH, and other interested parties. Previous network meta-analyses have shown DTG + 3TC to have efficacy and safety comparable to those of guideline-recommended 3-drug regimens at Weeks 48 and 96 [6, 7]. This indirect treatment comparison was undertaken to further assess the durability of efficacy and long-term safety of DTG + 3TC vs recommended second-generation, INSTI-based regimens 144 weeks after treatment initiation. ## Study identification Systematic literature searches of PubMed, Embase, and Cochrane databases were conducted in 2013, 2018, and 2019, as previously described [6–9]. The aim of these searches was to identify phase 3 or 4 randomized controlled trials evaluating the efficacy and/or safety outcomes of regimens recommended by DHHS or EACS guidelines in treatment-naive adult or adolescent (aged ≥ 13 years) PWH [1, 2, 6, 9]. Identified trials evaluating second-generation guideline-recommended INSTI-based ART regimens were described in previously published meta-analyses based on Week 48 and Week 96 results [6, 8]. A manual search was subsequently conducted to identify any reported 144-week outcomes from the same clinical trials. Internal clinical study reports were used for unpublished data from ViiV studies. ## Outcomes Efficacy outcomes of interest in the indirect treatment comparison were the proportion of trial participants achieving virologic suppression (HIV-1 RNA < 50 copies/mL) calculated according to the US Food and Drug Administration (FDA)-defined Snapshot algorithm [10] and the proportion with virologic failure (HIV-1 RNA ≥ 50 copies/mL; missing, switch, or discontinuation equals failure). CD4 + cell counts reported as mean change from baseline in the number of cells per microliter were also included for analysis. All-cause discontinuations, discontinuations due to AEs, grade 3 or 4 AEs, serious AEs, and drug-related AEs through 144 weeks of treatment were included in the comparison of safety and tolerability. Otherwise identical regimens with either TDF or TAF were assumed to have no clinical differences in efficacy, safety, and tolerability outcomes, which is supported by a meta-analysis showing no significant differences between TAF/FTC and TDF/FTC for these endpoints [11]. Therefore, DTG + TDF/FTC was assumed to be equivalent to DTG + TAF/FTC in this analysis. ## Statistical analysis Analyses were performed using Microsoft Excel (Redmond, WA). Anchored Bucher’s frequentist-adjusted, fixed-effects indirect treatment comparison methodology [12] was used to evaluate relative outcomes between treatments, in accordance with the International Society for Pharmacoeconomics and Outcomes Research guidelines [13]. This method indirectly compares the effect of 2 therapies when the randomized controlled trials evaluating them both share a common control arm. The results were expressed as risk difference for virologic outcomes, mean difference for change in CD4 + cell count, and odds ratio (OR) with $95\%$ CIs for safety outcomes. Indirect treatment comparison–generated risk differences and mean differences with $95\%$ CIs that did not contain 0, and ORs with $95\%$ CIs that did not contain 1, were considered statistically significant. ## Study characteristics Four studies were identified for inclusion (GEMINI-1, GEMINI-2, GS-US-380–1489, and GS-US-380-1490; Additional file 1: Table S1) [5, 14, 15], all of which have been previously included in published indirect treatment comparisons [6–8, 16–18]. The 4 studies formed a connected network via DTG + TD(A)F/FTC and bictegravir/FTC/tenofovir alafenamide (BIC/FTC/TAF; Fig. 1). Our analysis included a 2-step process: DTG + 3TC was initially indirectly compared with BIC/FTC/TAF based on DTG + TD(A)F/FTC being the common comparator to each regimen in the GEMINI trials and the GS-US-380-1490 trial, respectively. This result was subsequently used to indirectly compare DTG + 3TC with DTG/ABC/3TC using BIC/FTC/TAF (GS-US-380–1489 trial) as a common comparator (Fig. 1).Fig. 1Schematic of studies and treatment regimens included in the indirect treatment comparison. Data from direct comparisons (solid lines and filled circles) between (i) DTG + 3TC and DTG + TD(A)F/FTC (GEMINI-1 and GEMINI-2) and (ii) BIC/FTC/TAF and DTG + TD(A)F/FTC (GS-US-380–1490) were used for indirect comparison (dashed lines and open circles) between (iii) DTG + 3TC and BIC/FTC/TAF. Data from indirect comparison (iii) and direct comparison between (iv) BIC/FTC/TAF and DTG/ABC/3TC (GS-US-380–1489) were used for indirect comparison between (v) DTG + 3TC and DTG/ABC/3TC. ABC, abacavir; BIC, bictegravir; DTG, dolutegravir; FTC, emtricitabine; QD, once daily; TAF, tenofovir alafenamide; 3TC, lamivudine; TDF, tenofovir disoproxil fumarate. aDTG 50 mg + FTC 200 mg/TDF 300 mg QD or TAF 25 mg QD (DTG + TDF/FTC was assumed to be clinically equivalent to DTG + TAF/FTC). bDTG 50 mg + 3TC 300 mg QD. cBIC 50 mg/FTC 200 mg/TAF 25 mg QD. dDTG 50 mg/ABC 600 mg/3TC 300 mg QD All 4 included studies were randomized, active-controlled, multinational trials that enrolled ART-naive adults. Eligible participants had HIV-1 RNA ≥ 500 copies/mL (GS-US-380-1489 and GS-US-380-1490) [14, 15] or 1000 to 500,000 copies/mL (GEMINI-1 and GEMINI-2) [5] and showed no evidence of viral resistance mutations to study drugs. Participants in GS-US-380-1489 and GS-US-380-1490 were required to have an estimated glomerular filtration rate ≥ 30 mL/min [14, 19]. In GS-US-380-1489, participants who were HLA-B5701 positive or who had hepatitis B virus were excluded [14]. In GS-US-380-1490, participants with hepatitis B or C virus and previous antiretroviral use for pre-exposure or post-exposure HIV prophylaxis were permitted [19]. Women of reproductive potential were eligible for the GEMINI trials if they were not pregnant or lactating and using highly effective contraception, and exclusion criteria included active Centers for Disease Control and Prevention stage 3 HIV disease except for cutaneous Kaposi’s sarcoma and CD4 + cell count < 200 cells/μL [20]. Consistent with previous publications [6–8, 16–18], despite some small differences in inclusion and exclusion criteria, we found that the trial populations were similar with respect to key baseline characteristics, including age, sex at birth, race and ethnicity, mean viral load (4.39 to 4.45 HIV-1 RNA log10, copies/mL), and mean CD4 + cell count (453 to 476 cells/μL; Additional file 1: Table S2). ## Efficacy For the different treatment groups of the individual trials included in the analysis, the percentage of participants with virologic suppression ranged from $81.5\%$ to $84.1\%$ and the percentage with virologic failure ranged from $0.6\%$ to $4.7\%$ (Table 1). The results of the indirect treatment comparison showed no difference between DTG + 3TC and the 3-drug INSTI-based regimens BIC/FTC/TAF or DTG/ABC/3TC based on the risk difference ($95\%$ CI) for Week 144 virologic suppression ($0.1\%$ [− $6.9\%$, $7.2\%$] and − $2.5\%$ [− $11.6\%$, $6.7\%$], respectively) and virologic failure (− $1.3\%$ [− $4.8\%$, $2.1\%$] and − $3.5\%$ [− $7.6\%$, $0.5\%$], respectively; Fig. 2 and Additional file 1: Table S3). Mean changes from baseline to Week 144 in CD4 + cell count were also similar for all 4 treatment regimens ranging from 278 to 317 cells/μL (Table 1) and in the indirect treatment comparison (Fig. 2 and Additional file 1: Table S3).Table 1Week 144 efficacy and safety data from the included trialsOutcome, n (%)aGEMINI-1/-2 (pooled analysis)GS-US-380-1489GS-US-380-1490DTG + 3TC ($$n = 716$$)DTG + TDF/FTC ($$n = 717$$)BIC/FTC/TAF ($$n = 314$$)DTG/ABC/3TC ($$n = 315$$)DTG + TAF/FTC ($$n = 325$$)BIC/FTC/TAF ($$n = 320$$)HIV-1 RNA < 50 copies/mLb584 (81.6)599 (83.5)256 (81.5)265 (84.1)273 (84.0)262 (81.9)HIV-1 RNA ≥ 50 copies/mLb23 (3.2)21 (2.9)2 (0.6)9 (2.9)10 (3.1)15 (4.7)Change in CD4 + cell count from baseline, mean (SD), cells/µL300 (203.5)298 (227.1)299 (224.9)317 (219.5)289 (218.5)278 (236.6)Discontinuations134 (18.7)123 (17.2)54 (17.2)48 (15.2)47 (14.5)59 (18.4)AEs613 (85.6)625 (87.2)300 (95.5)304 (96.5)300 (92.3)291 (90.9)Grade 3–4 AEs83 (11.6)88 (12.3)50 (15.9)50 (15.9)43 (13.2)54 (16.9)Serious AEs76 (10.6)85 (11.9)41 (13.1)53 (16.8)40 (12.3)63 (19.7)Drug-related AEs146 (20.4)192 (26.8)94 (29.9)132 (41.9)95 (29.2)71 (22.2)Discontinuations due to AEs24 (3.4)25 (3.5)0 [0]5 (1.6)6 (1.8)6 (1.9)ABC, abacavir; AE, adverse event; BIC, bictegravir; DTG, dolutegravir; FDA, US Food and Drug Administration; FTC, emtricitabine; TAF, tenofovir alafenamide; 3TC, lamivudine; TDF, tenofovir disoproxil fumarateaUnless otherwise specified. bFDA Snapshot algorithmFig. 2a Efficacy and b safety results of the indirect treatment comparisons at Week 144. Comparisons assumed TDF/FTC and TAF/FTC to be equivalent. Horizontal lines represent $95\%$ CI. ABC, abacavir; BIC, bictegravir; DTG, dolutegravir; FDA, US Food and Drug Administration; FTC, emtricitabine; TAF, tenofovir alafenamide; 3TC, lamivudine. aComparison against DTG/ABC/3TC could not be estimated due to zero events in one treatment group. bFewer SAEs occurred with DTG + 3TC than with BIC/FTC/TAF ($$P \leq 0.014$$) and DTG/ABC/3TC ($$P \leq 0.006$$) ## Safety Serious AEs occurred less frequently in the DTG + 3TC treatment group than with the 3-drug regimens (Table 1). Among those treated with DTG + 3TC, the odds of experiencing a serious AE were lower than those treated with BIC/FTC/TAF (OR [$95\%$ CI], 0.51 [0.29–0.87]; $$P \leq 0.014$$) or DTG/ABC/3TC (OR [$95\%$ CI], 0.38 [0.19–0.75]; $$P \leq 0.006$$; Fig. 2). The frequencies of discontinuations (all-cause and AE-related) and AEs (all-cause, drug-related, and grade 3 or 4) were similar between DTG + 3TC and the comparator regimens (Fig. 2 and Additional file 1: Table S3). A comparison of DTG + 3TC and DTG/ABC/3TC was not possible due to the lack of AE-related discontinuations in the BIC/FTC/TAF group of the GS-US-380-1489 trial. ## Discussion Guideline-recommended initial ART regimens for most PWH include INSTI-based 3-drug and 2-drug regimens that have “demonstrated durable virologic efficacy, favorable tolerability and toxicity profiles, and ease of use” but have not been compared with each other in randomized clinical trials in ART-naive PWH [1, 2]. In the absence of randomized data, indirect treatment comparisons can provide useful information to help clinicians, PWH, and other interested parties, such as payers, make appropriate treatment choices. Long-term data are particularly relevant because PWH require lifelong ART to maintain virologic suppression. In addition, the high prevalence of comorbidities associated with HIV, especially in older adults, means that safety and tolerability are important to consider [1, 2]. The findings of our indirect treatment comparison suggest that DTG + 3TC offers similar efficacy (measured by virologic suppression and change from baseline in CD4 + cell count), with a comparable or better safety profile than BIC/FTC/TAF and DTG/ABC/3TC at Week 144 in previously untreated adults and adolescents. In particular, DTG + 3TC was estimated to result in fewer serious AEs than BIC/FTC/TAF and DTG/ABC/3TC. These results are broadly in line with results of analyses carried out at earlier time points (48 and 96 weeks) and add long-term data to the growing body of evidence supporting the non-inferiority of 2-drug regimens compared with 3-drug regimens [7, 9]. In GS-US-380-1489 and GS-US-380-1490, no participants had treatment-emergence resistance [14]. In GEMINI-1 and -2, no participants who met confirmed virologic withdrawal criteria had treatment-emergent resistance [5]. One participant with reported non-adherence in the GEMINI trials had treatment-emergent R263R/K at Week 144, which conferred a 1.8-fold reduction in susceptibility to DTG [5]. Altogether, these findings support the high barrier to resistance of standard-of-care INSTI-based regimens through 3 years. Larger, more complex networks of randomized controlled trials are usually analyzed using Bayesian methodology [6–8, 16, 18], whereas the structure of our network allowed the use of the simpler Bucher analysis. This method has been widely used in various therapeutic areas and has not been shown to generate generally consistent results in similarly structured networks [21–25]. All the trials included in the analysis were designed to demonstrate anticipated equivalence between the treatment regimens. Few long-term randomized clinical trial data exist for guideline-recommended ART regimens as many trials switch to open-label designs after 48 or 96 weeks. Albeit limited, the trials included in our analysis are landmark investigations of the respective treatment regimens and all remained blinded through Week 96 (GEMINI-1 and GEMINI-2) or 144 (GS-US-380-1489 and GS-US-380-1490) and included pre-specified secondary endpoints at Week 144 [5, 14, 15]. We are unaware of any controversy surrounding the validity of the findings from these trials, and we consider that the comparative estimates from this work are derived from the best evidence currently available. 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--- title: 'PWN: enhanced random walk on a warped network for disease target prioritization' authors: - Seokjin Han - Jinhee Hong - So Jeong Yun - Hee Jung Koo - Tae Yong Kim journal: BMC Bioinformatics year: 2023 pmcid: PMC10031933 doi: 10.1186/s12859-023-05227-x license: CC BY 4.0 --- # PWN: enhanced random walk on a warped network for disease target prioritization ## Abstract ### Background Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. ### Results We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. ### Conclusions We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12859-023-05227-x. ## Background Deciphering target proteins for disease treatment has been an important challenge in medical care, as it is the first step in the drug discovery process and one that critically affects its success rate. To effectively solve this problem, we must first understand disease biology, and due to the increased accessibility of high-throughput technologies in recent years, diverse types of unbiased data have been generated for a range of diseases. However, the causes and consequences of disease states are concurrently reflected in those unbiased high-throughput data that compare the disease samples and normal samples. Thus, discriminating potential causes from widespread consequences is an essential task when using disease-perturbed data to prioritize targets to cure. A well-constructed protein-protein interaction (PPI) network can help tackle this issue because it provides hints for dealing with massive amounts of high-throughput omics data by showing the overall landscape of the protein relations. Several previous studies adopted random walk-based approaches for utilizing PPI networks to associate genes and diseases and reported some encouraging results by suggesting a number of disease genes with literature evidence [1–9]. A biological network consists of nodes and edges, which represent the biological entities and the relations between these entities, respectively. Since the information constituting the network is usually obscured in the given omics data, researchers try to integrate the topological properties into the omics data analysis process and enhance the initial analysis results. One of the methods that leverage the network is a random walk. A random walk diffuses the initial signal through its neighbors. Therefore, the diffused signal is heavily affected by its original signal and the signals derived from the direct neighbors, while other nodes also slightly affect the signal. Most of these studies provided a collective set of known disease genes to initiate random walk processes and obtain novel targets that reflected previously studied disease biology. In the same way, this approach can be applied to extract important genes from omics data. For example, differentially expressed genes derived from omics data can be used as the starting points of random walk processes [10–12]. Most random walk-based methods heavily rely on an unweighted and undirected network when they spread the information assigned to nodes; i.e., they do not make a distinction between different neighbors when choosing which neighbor to use to spread information, although the neighbors have different biological importance levels. Therefore, one can expect that using a weighted and directed network to propagate more information through important edges can yield improved accuracy. Nevertheless, how to assign suitable edge properties remains a question. In the case of a PPI network, the network contains not just simple interactions between its constituents but much more information, such as the intraconnectivity of protein complexes or a set of proteins involved in the same pathway. This property obviously implies that we must carefully landscape the PPI network to let information flow in the proper direction. The most intuitive and direct way to achieve this is to use biological information related to the way the researcher wants to. Hristov [11] seems to be a representative example of utilizing this idea. In that study, they showed that the accuracy of random walk algorithm can be improved by using proper prior knowledge. Noteworthy finding among their experiments was that using cancer-specific prior knowledge gives more accurate result than using general cancer-related prior knowledge, in the target identification per cancer type experiment. Another available option is using the network’s own properties. We choose the curvature in a PPI network, which is based on the local connectivity and other network-derived properties. Curvature is a concept originating from differential geometry that measures the rate of bending at a given point or how much the region in question is warped from flat lines, flat surfaces, or flat manifolds. For instance, the curvature of a circle of radius \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r$$\end{document}r is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r^{-1}$$\end{document}r-1. Various types of curvatures have been suggested and studied: Gaussian curvature, geodesic curvature, and sectional curvature, to name a few [13]. A Ricci curvature, one of the most important variants, has been used in a wide range of fields, such as fluid mechanics, Einstein’s general theory of relativity, and Perelman’s proof of the Poincaré theorem [14]. We thereby suggest a new algorithm named prioritization with a warped network (PWN), which can be applied to disease target identification methods using a series of high-throughput data, “-omics” data, based on a PPI network. PWN incorporates both network-dependent features and network-independent features to warp the network by applying graph curvature and known disease genes, respectively. ## Overview of PWN PWN is designed to be an efficient variant of random walk with restart (RWR) [15]. Unlike the usual RWR algorithm that employing simple unweighted network, PWN uses a weighted asymmetric network that is generated from an unweighted and undirected network. The weights come from two distinct features. One is an internal feature that depends on the network topology, and the other is an external feature that is independent of the given network (see Fig. 1 for a graphical overview).Fig. 1Graphical overview of PWN. PWN generates a weighted and implicitly directed network (lower right) from an unweighted and undirected network (upper left) using two distinct sources. First, PWN computes the Ricci curvature of the edges and derives the first edge weights by applying an exponential function on the computed curvature (upper middle). We consider this curvature an internal feature. After that, the external feature warps the network; prior knowledge is mapped on the network nodes (upper right) and then spread. The spread prior knowledge is then applied to the edges (lower right). Finally, gene scores acquired from the omics data is mapped on the nodes (lower middle) and spread to obtain the final scores (lower left) PWN uses the graph Ricci curvature, which is highly related to local structural information, as the internal feature (see “Warping via an internal feature: graph curvature” section). While curvature was originally defined on smooth continuous domains, several researchers have extended this concept to discrete objects such as networks. The graph Ricci curvature [16–19] is one of these extensions that was independently developed by [18] and [16]. The Ollivier–Ricci curvature lies in optimal transport theory, while the Forman–Ricci curvature was derived using CW complexes introduced in homotopy theory. These types of curvatures have been applied to several recent graph-based machine learning algorithms [20–22]. Note that the edges in a dense complete graph (or clique) tend to have higher curvatures, which implies that higher curvatures can be observed on the edges in intraprotein complexes [23, 24]. In contrast, intercomplex edges have a higher probability of possessing lower curvatures. Therefore, the graph Ricci curvature can be used to overcome the indiscriminate nature of a random walk, as shown in Fig. 2. Consider a random walk starting from the purple node, where the left and right sides have different structures. Naturally, one would like to distinguish them in random walks, but the probabilities of being on the left side and right side are both equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$\frac{5}{10}$=50\%$$\end{document}$\frac{5}{10}$=$50\%$ when using an unweighted graph. To solve this issue, one can inject the curvature into random walks, and thus, the random walk is now affected by the local structure. Fig. 2By using curvature, one can precisely control random walks. Consider a random walk starting from the purple node, where one would like to distinguish between the probabilities of being on the left side and right side. Note that the blue edges have curvatures of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1$$\end{document}1, while the red edges have curvatures of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-7$$\end{document}-7, making it easy to control the odds ratio with the curvatures In the context of a bioinformatics application, this implies that one can control the amount of information that propagates through or flows out from protein complexes and/or hubs by using curvature. Proteins are mutually influenced by whether they are physically linked or by various non-physical relationships hidden prior to protein formation (at the level of DNA or RNA). Therefore, it is intuitive that the properties of networks (e.g., hub, curvature,...) affect the physical or non-physical (information transfer) relationships of protein-protein interaction [25, 26]. Note that most of the nodes belonging to protein complexes are connected to each other, so the edges in the complexes can have large positive curvatures. In contrast, the neighbors of a hub node are likely to not be connected, so the edges attached to the hub have large negative curvatures. It can be seen in various PPI networks that it is possible to distinguish hub nodes from other nodes by a color determined solely by the curvature. Using PPI networks in E. coli and humans, we confirmed that these properties are common. As shown in the Fig. 3 and Additional file 1, the protein complexes appear red while the hub proteins appear blue. Fig. 3The PPI network of the E. coli. The edges are colored using their curvatures, and the nodes are colored using the average of the connected edges’ curvatures. Values close to zero are colored gray, positive values are colored red, and negative values are colored blue. Furthermore, in order to display a more clear difference, nodes with a degree less than 10 were painted gray regardless of the value In fact, various existing methods already employed Ricci curvatures to their biological applications. Sandhu et al. [ 22] used curvatures on gene co-expression networks to distinguish between cancer networks and normal networks. Coupled with side information, Sia et al. [ 27] can construct functional communities with the curvature-based community detection algorithm. Murgas et al. [ 28] applied PPI curvatures on a single-cell RNA-seq data and obtained successful results on several tasks, which includes distinguishing pluripotent cells and distinguishing cancer cells. Zhu et al. [ 29] combined the curvature of a PPI with a clustering technique to extract cancer subtypes from the multi-omics data. PWN is designed to manage the proportion of information circulating in and flowing out of certain regions by controlling this internal feature. We empirically show that this internal feature has little impact on the resulting performance (see “Effectiveness of internal features” section) but provides significant improvements when it is combined with the external feature (see “Effectiveness of the external feature” section). We use the (augmented) Forman–Ricci curvature instead of the Ollivier–Ricci curvature. Although several studies have suggested that both types of curvature behave similarly on various biological networks (including PPI networks) [30, 31], computing the Forman–Ricci is much faster than computing the Ollivier–Ricci curvature, especially in a large-scale network. Since PPI networks often have large numbers of nodes and edges (see “Collecting PPI networks” section), using the Forman–Ricci curvature seems preferable for us. After warping the network using curvatures, the prior knowledge (see “Collecting the ground truth and constructing experiments” section) related to the given task is applied to the network as an external feature. While conventional random walk-based methods do not consider the prior knowledge from a given task, some types of modern algorithms, including machine learning methods, heavily employ prior knowledge from the context and encode that knowledge into their algorithms. When attempting to obtain a more appropriate result for a specific task, an algorithm reflecting prior knowledge would perform better than a general task-independent algorithm. Given this assumption, we use external data, which cannot be gathered from the network, to provide a clear guide for the propagation of information and enhance performance. PWN warps the network by assigning higher weights to prior knowledge-related edges. Note that the prior knowledge is not guaranteed to cover the ground truth in its entirety, so PWN first spreads the prior knowledge, and the missing information can be covered (see “Warping via an external feature: prior knowledge” section). uKIN, previously suggested by [11], also diffuses the prior information using a (shifted) Laplacian and multiplies the smoothed knowledge with the edge weights. However, uKIN occasionally encounters tuning limitations since the amount of shift is not bounded from above. If the optimal amount of shift is larger than the expected amount, the search space of the tuning hyperparameters rarely contains the optimal region and results in suboptimal performance. Therefore, we use an alternative method based on a RWR [15], where the range of each hyperparameter is always bounded between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0$$\end{document}0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1$$\end{document}1 and is thus easier to tune. Finally, the gene scores obtained from the unbiased omics data (see “Computing the initial gene scores” section) are diffused through the warped network, which gives the final gene scores (see “Score diffusion with a warped network” section). We add hyperparameters to control the amount of information spread during each step, which makes PWN more versatile and flexible. ## Comparison between PWN and other methods PWN can be used for identifying the targets with properly given prior knowledge and gene scores. To demonstrate this, we designed series of simulations using various cancer-related data from Homo sapiens, including The Cancer Genome Atlas (TCGA) and Cancer Gene Census (CGC), and check whether the methods can find the known cancer targets. First, we collect the data as follows (see “Dataset preparation for a simulation study” section for more details). First, we download public PPI databases and compute their initial gene scores using statistical tests performed on transcriptome data. Then, we collect the ground-truth genes and randomly divide them into two groups: one is an (optional) train set used by diffusion methods, and the other is a test set required for performance measurement. From the collected data, we apply various methods and measure the resulting performance metrics. We repeat this multiple times to achieve a robust performance comparison. Under this setup, we compare the performance of several methods. For baselines, we use the RWR [15], the RWR with GDC [32], uKIN [11], and mND [33], each of which is equipped with the default hyperparameters presented in the original papers. For PWN, we use \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0.5$$\end{document}β=0.5, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma =0.5$$\end{document}γ=0.5 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$q = 0.3$$$\end{document}$q = 0.3$ as the default parameters. Note that these values are not tuned or cherry-picked. Additionally, we add some variant methods to our experiments: the RWR with curvatures, uKIN with curvatures and PWN without curvatures (see “Variants of PWN” section). We evaluate the performance of each simulated trial and draw box plots for each method and metric, as shown in Fig. 4, Tables 1 and 2, which clearly shows that PWN outperforms the other methods. PWN consistently achieves the highest AveP, with PWN without curvature placing second. Additionally, the significance of the improvement is shown in Fig. 5, which turns out that PWN is significantly better than every other baselines. Also note that the uKIN with curvature performs better than the vanilla uKIN. Contrary to our expectations, the RWR with curvatures does not work well; it is sometimes even inferior to the baseline. Fig. 4Box plots of the performance metrics. The details are listed in Tables 1 and 2Table 1Detailed performance metrics obtained in experiments using STRINGSTRINGAvePPrec@100Prec@200RWR0.084489 ± 0.0028540.203000 ± 0.0185970.160333 ± 0.010822RWR w/GDC0.065111 ± 0.0019860.154667 ± 0.0145590.123333 ± 0.006989mND0.090178 ± 0.0031850.179000 ± 0.0129590.160333 ± 0.012658RWR w/curvature0.085537 ± 0.0030520.228000 ± 0.0193690.158333 ± 0.011321uKIN0.092099 ± 0.0035550.227333 ± 0.0201600.179500 ± 0.013918uKIN w/curvature0.100554 ± 0.0047080.264333 ± 0.0243090.204500 ± 0.013980PWN w/o curvature0.116091 ± 0.0074040.299000 ± 0.0360410.225833 ± 0.020556PWN0.121372 ± 0.0086690.329000 ± 0.0363270.234500 ± 0.022604The numbers denote averages and standard deviations. The best performances are in bold; the second-best performances are in italicsTable 2Detailed performance metrics obtained in experiments using BioGRIDBioGRIDAvePPrec@100Prec@200RWR0.084178 ± 0.0027870.183000 ± 0.0191460.149833 ± 0.011332RWR w/GDC0.075219 ± 0.0028410.185333 ± 0.0159160.147833 ± 0.010803mND0.084701 ± 0.0028580.202333 ± 0.0190610.148000 ± 0.011641RWR w/curvature0.083100 ± 0.0027620.189333 ± 0.0131130.153667 ± 0.012172uKIN0.086875 ± 0.0029950.192333 ± 0.0167500.154667 ± 0.011059uKIN w/curvature0.088618 ± 0.0032090.206333 ± 0.0182860.163000 ± 0.013235PWN w/o curvature0.106239 ± 0.0050780.265667 ± 0.0248700.209333 ± 0.019728PWN0.110223 ± 0.0056240.282333 ± 0.0267410.221833 ± 0.021794The numbers denote averages and standard deviations. The best performances are in bold; the second-best performances are in italics Fig. 5Significance test for performance improvements over baselines. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p values are obtained via one-sided paired \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document}t-tests, and adjusted via the Bonferroni–Hochberg method. Gray color means that the corresponding adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p value is larger than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-4}$$\end{document}10-4 In the remaining sections, we focus on analyzing the behavior of PWN, such as the effects of curvature (see “Effectiveness of internal features” section) and prior knowledge (see “Effectiveness of the external feature” and “Effectiveness of the amount of prior knowledge” sections). Additionally, we find that PWN has slightly more volatile performance than the other methods. The major cause of this variance is identified in “Post hoc analysis of the large induced variance” section. ## Effectiveness of internal features To observe the pure influence of the curvature alone, we compare the methods that do not rely on prior knowledge. Three methods are chosen: the RWR, RWR with GDC [32], and RWR with curvatures. We plot the results in Fig. 6, which reveals that the effect of curvature might be different when using different PPI networks. In the experiment using STRING, one can confirm that Prec@100 and Prec@200 decrease when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β decreases. In contrast, Prec@100 and Prec@200 decrease when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β increases if BioGRID is used. Note that AveP seems to remain the same for both cases. Fig. 6Comparison among methods that do not use prior knowledge This phenomenon occurs because the characteristics of the two networks are different, as clearly shown in Fig. 7. Both figures appear to be V-shaped, but some differences are also identified. BioGRID has longer tails; in other words, it contains nodes with more extremely negative average curvatures than those in STRING.STRING contains nodes with sufficiently positive average curvatures, while BioGRID does not. Fig. 7PPI comparison based on average curvatures and node degrees. For each node in the networks, we compute its degree (number of connected edges) and average curvature (mean of the curvatures of its connected edges) and display them on the 2d plane From these differences, we suspect that STRING has more protein complexes than BioGRID, while BioGRID tends to have more hub proteins interacting with a large number of neighbors (recall Fig. 2). Furthermore, notice the difference in perspective of the relative densities of priors. In STRING, the priors are concentrated at near the origin and the positive-curvature region, while the priors are spread more widely and tends to have more negative curvature in BioGRID. These differences seem to have made the difference in Fig. 7; if the priors are in the negative-curvature region (as in BioGRID), it would be advantageous to send prior knowledge towards it by setting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β to negative and vice versa. ## Effectiveness of the external feature In this experiment, we measure the performance achieved by PWN with varying hyperparameters so that we can understand the comprehensive effect of curvature and prior information. Figure 8 displays the results of an experiment conducted with uKIN as the baseline. Fig. 8Effect of curvature when curvature and prior knowledge are simultaneously employed. The white boxes are baselines obtained from uKIN The most remarkable aspect of Fig. 8 is that the application of curvature information enhances performance when used with prior information. PWN with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =$\frac{1}{2}$$$\end{document}β=$\frac{1}{2}$ always performs better than PWN without curvature, as seen when the AveP is calculated. However, larger \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β usually harms the performance again. We suspect the following hypothesis. Due to the nature of the sigmoid function, as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β increases, edges weights in the matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K squashes to 0 s (see Fig. 9). When the edge weight becomes 0, it is impossible for the prior knowledge to be diffused in that direction. Therefore, the prior knowledge does not spread well. Because of this phenomenon, larger \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β interferes the diffusion and harms the performance. Similarly, we suspect that negative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β also adversely affects the spread of prior knowledge. Fig. 9Distributions of edge weights of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K Additionally, note that PWN works well when the restart probability \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ satisfies \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.3\le \gamma \le 0.7$$\end{document}0.3≤γ≤0.7, implying that a moderate level of smoothing is important. ## Effectiveness of the amount of prior knowledge We have argued that the combination of internal and external features can improve the resulting analysis performance. However, note that external information is not always available. It is important to make PWN work even if there is little prior knowledge; otherwise, PWN could not work on most real-world cases. Thus, to determine the influence of the amount of prior knowledge, we plot the performance of PWN and uKIN under various amounts of prior knowledge in Fig. 10. We can observe that PWN always performs better than the baseline as long as prior knowledge is given, regardless of the amount of information. Thus, one can expect a performance improvement even though little prior knowledge is available. In addition, the performance seems to increase linearly as the amount of prior knowledge increases. This implies that PWN can efficiently fuse prior knowledge, since if an information loss exists, the increasing trend is likely to be weak or not detected. Fig. 10Effect of the amount of prior knowledge ## Post hoc analysis of the large induced variance Although the performance of PWN is superior to that of other methods, we find that the variance of PWN is much larger than that of other methods. We suspect that the large performance variance originates from the large variance of the smoothed prior knowledge, so we empirically verify that hypothesis by comparing the internal variances of uKIN and PWN using the same simulated dataset, and the results are displayed in Fig. 11.Fig. 11Variance of the smoothed prior knowledge for each gene. The dashed line denotes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=x$$\end{document}y=x As the figure implies, the standard deviation of the smoothed prior information of PWN is much larger than that of uKIN. We conclude that these high variances of PWN trivially make the transition matrix and resulting gene scores highly volatile, and thus, the performance metrics are affected. ## Discussion Through the various experiments, we find that PWN performs better than other available random walk methods, although some points related to the effects of using curvatures and the properties of PWN remain to be discovered. Our first question concerns the reason that a side effect is induced when no prior knowledge is available. We suspect that the curvature itself might have no strong relation to the given task. Topological properties are not explicitly related to biological tasks, and no one can guarantee their effectiveness. Thus, using only topological properties might distort the diffusion process in an unwanted way. Furthermore, due to the massive number of edges, the dissonance becomes large and unrecoverable. As a result, the diffusion process becomes inefficient and might damage the resulting performance. One of our unexpected observations is that applying curvature in a negative sense (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta <0$$\end{document}β<0) does not affect or even increases the accuracy. Utilizing curvatures with prior knowledge, however, has a synergetic effect on the analysis. Note that the amount of prior knowledge is much less than the number of genes in the network; thus, applying prior knowledge removes the effects of most edges by suppressing every edge attached to meaningless nodes. From this observation, we hypothesize that the effectiveness of curvature is at last revealed when the prior information leaves only meaningful and relevant edges; otherwise, the side effect of applying curvature is dominant because there are too many irrelevant edges. Another noticeable point is related to the effects of the hyperparameters, as shown in Fig. 8. Recall that an extreme value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ harms the performance. Although we have yet to determine explicit evidence, we interpret this phenomenon as follows. In the case of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ, we conjecture the following statement: excessive smoothing might cause a slight disparity between the priors and non-priors, and the use of prior knowledge without smoothing can remove important edges related to unobserved or missing knowledge. Furthermore, a large \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β can make PWN completely ignore low-curvature edges and cause inefficacy in the random walks. As mentioned above, most edges have low edge weights after applying PWN since the prior information suppresses every edge attached to meaningless nodes. Furthermore, since the edges with large curvatures are attached to a few nodes that have many edges, the reconstructed network is very sparse and far away from a single connected network. ## Future works We’d tested PWN on three PPIs (STRING, BioGRID, IID) and all three experiments confirmed that PWN has the best performance. However, it is a matter to be further confirmed whether this is also true for all PPI. Empirically, it can be verified through other PPI databases or in other organisms. Default hyperparameters in this paper also have to be verified whether they operate universally well. By our intuition, tuning is necessary depending on the nature of the network. We also think that the larger \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β makes the performance worse, so we can find a range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β for the PWN to works well. Although PWN is very effective, the results of PWN are more volatile than those of other available baselines. We initially suspect that this problem is related to the prior smoothing process due to the following observations: the lack of a variance between PWN and PWN without curvature in Fig. 4, and the intuitive approximated computations (see Additional file 2). Figure 11 empirically proves this hypothesis and provides some hints for reducing the variance. We should build a novel method to effectively smooth the prior information for achieving lower variance. In this study, we used the driver genes of cancer that have already been experimentally proven as prior information. In addition, in many cases there is significant prior knowledge of the disease, which will affect the performance and application of our algorithms. For example, additional information that can be considered is prior knowledge by cancer type. Interestingly, the more prior knowledge about a particular type of cancer is used, the better we will be able to discover the gene responsible for the cancer. This has already been demonstrated by other groups using similar random walks approach [11]. We tested our idea only on random-work algorithms, but it can be easily extended to other network-based algorithms, especially graph neural networks. Although most graph neural networks are based on message-passing architectures, there exist such cases where random walk is directly used, such as [34], and our idea seems more suitable in the latter case. In line with this, the experiment should be conducted on a task with more complex features and outputs, not just score aggregation. Lastly, we’re planning to explore other network properties that have greater relevance to disease target identification and employ a high-throughput data analysis to achieve increased performance whether prior knowledge is available or not. ## Conclusion The random walk approach has become a popular tool in integrative analyses. The trends in recent work suggest that it will continue to be used and further refined as demands related to various data types arise. Several random walk methods have been developed to derive an effective procedure [11, 32]. We introduced PWN, a new method that combines a graph curvature approach for controlling the amount of information flowing in networks with prior knowledge to achieve enhanced prediction performance. We showed above that a synergetic effect was observed when a graph curvature approach and prior knowledge were applied simultaneously. Furthermore, our method achieved the most performance gains relative to GDC [32] and uKIN [11]. In future work, we will also test whether PWN can successfully help analyze other biological data. ## Methods The Python package and related datasets and code used for our reproducible experiments are available on GitHub.1 ## Notations Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G=(V,E)$$\end{document}G=(V,E) be an unweighted network denoting the interactions between nodes, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V=\{1,\dots,n\}$$\end{document}V={1,⋯,n} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\subset V\times V$$\end{document}E⊂V×V are sets of nodes and edges, respectively. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e\in E$$\end{document}e∈E, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _e$$\end{document}σe and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta _e$$\end{document}δe denote the source and target node of edge \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e$$\end{document}e, respectively, so \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _{(i,j)}=i$$\end{document}σ(i,j)=i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta _{(i,j)}=j$$\end{document}δ(i,j)=j. Assume that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(i,j)\in E\iff (j,i)\in E$$\end{document}(i,j)∈E⟺(j,i)∈E and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\not \exists i\in V: (i,i)\in E$$\end{document}∄i∈V:(i,i)∈E, which means that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G$$\end{document}G is undirected and has no self-loops. The neighbors of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i are defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{i}=\{k: (i, k)\in E\}$$\end{document}Ni={k:(i,k)∈E}. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A\in {\{0, 1\}}^{n\times n}$$\end{document}A∈{0,1}n×n be the adjacency matrix of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G$$\end{document}G:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} A_{ji}={\left\{ \begin{array}{ll} 1 &{} \text {if }(i, j)\in E \\ 0 &{} \text {otherwise.} \end{array}\right. } \end{aligned}$$\end{document}Aji=1if(i,j)∈E0otherwise. From the adjacency matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A$$\end{document}A, the degree matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D\in \mathbb {N}^{n\times n}$$\end{document}D∈Nn×n is defined as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} D_{ji}={\left\{ \begin{array}{ll} \sum _{k} A_{ki} &{} \text {if }i=j, \\ 0 &{} \text {otherwise.} \end{array}\right. } \end{aligned}$$\end{document}Dji=∑kAkiifi=j,0otherwise. ## Warping via an internal feature: graph curvature First, we warp the unweighted adjacency matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A$$\end{document}A using the network-related feature. We choose to use the augmented Forman–Ricci curvature [16, 17] \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa _{e}$$\end{document}κe, which can be simply computed as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \kappa _{e} = 4 - \|{N}_{\sigma _{e}}\| - \|{N}_{\delta _{e}}\| + 3\|{N}_{\sigma _{e}}\cap {N}_{\delta _{e}}\|, \end{aligned}$$\end{document}κe=4-\|Nσe\|-\|Nδe\|+3\|Nσe∩Nδe\|,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\|{S}\|$$\end{document}\|S\| is the number of elements in the set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S$$\end{document}S. Then, we construct our first warped adjacency matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K=\mathbb {R}_{+}^{n\times n}$$\end{document}K=R+n×n by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} K_{ji} = {\left\{ \begin{array}{ll} \textrm{Sigmoid}\left(\beta \left(\kappa _{(i,j)}-\text{mean}(\kappa)\right)/\textrm{sd}(\kappa)\right) &{} \text {if }A_{ji} = 1,\\ 0 &{} \text {otherwise,} \end{array}\right. } \end{aligned}$$\end{document}Kji=Sigmoidβκ(i,j)-mean(κ)/sd(κ)ifAji=1,0otherwise,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{mean}(\kappa)$$\end{document}mean(κ) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{sd}(\kappa)$$\end{document}sd(κ) are the sample mean and sample standard deviation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa _{e}$$\end{document}κe, respectively, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta \in \mathbb {R}$$\end{document}β∈R is a hyperparameter for controlling the effect of curvatures. Note that the range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa _{e}$$\end{document}κe may differ across various networks and can take extremely large or small values, so we first normalize the curvatures to prevent these potential problems. Additionally, note that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0$$\end{document}β=0 yields the original unweighted network. ## Warping via an external feature: prior knowledge Let the set of prior nodes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\subset V$$\end{document}P⊂V be given, where each node in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P$$\end{document}P is known to be related to a given task and independent of the network. We want to warp \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K again using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P$$\end{document}P to reflect the prior knowledge. We define \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi \in \mathbb {R}_{+}^{n}$$\end{document}ϕ∈R+n as\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \phi _{i} = {\left\{ \begin{array}{ll} 1 / \| {P}\| &{} \text {if }i\in {P},\\ 0 &{} \text {otherwise.} \end{array}\right. } \end{aligned}$$\end{document}ϕi=1/\|P\|ifi∈P,0otherwise. Then, we build a Markov kernel \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\in \mathbb {R}_{+}^{n\times n}$$\end{document}P∈R+n×n as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} P_{ji} = (1-\gamma)\frac{K_{ji}}{\sum _{$k = 1$}^{n}K_{ki}}+\gamma \phi _{j}, \end{aligned}$$\end{document}Pji=(1-γ)Kji∑$k = 1$nKki+γϕj,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma \in [0,1]$$\end{document}γ∈[0,1] is a hyperparameter named the restart probability. From the kernel \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P$$\end{document}P, we compute a stationary distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi \in \mathbb {R}_{+}^{n}$$\end{document}π∈R+n and consider it as the smoothed prior knowledge. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma = 1$$\end{document}γ=1 implies the use of prior knowledge without smoothing, while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma = 0$$\end{document}γ=0 involves fully smoothing the prior knowledge. Recall that when performing a restart, the kernel jumps to a random prior node that is drawn from some conditionally uniform distribution. This means that the method guarantees the equal use of the prior information. Finally, we compute the final weighted adjacency matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A^{}\in \mathbb {R}_{+}^{n\times n}$$\end{document}A∗∈R+n×n by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} A^{}_{ji} = {\left\{ \begin{array}{ll} K_{ji}\pi _{j} &{} \text {if }(i,j)\in {E},\\ 0 &{} \text {otherwise.} \end{array}\right. } \end{aligned}$$\end{document}Aji∗=Kjiπjif(i,j)∈E,0otherwise. Note that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A^{}$$\end{document}A∗ is asymmetric, although \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{ji}^{}> 0 \iff A_{ij}^{} > 0$$\end{document}Aji∗>0⟺Aij∗>0 is still satisfied. In other words, PWN assigns different weights to the same edge but in different directions, which implies that PWN converts undirected graphs to implicitly directed graphs. ## Score diffusion with a warped network Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v^{[0]}\in \mathbb {R}^{n}$$\end{document}v[0]∈Rn be the initial gene scores obtained from the given omics data. We want to enhance \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v^{[0]}$$\end{document}v[0] to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v^{}$$\end{document}v∗ by injecting more information via the warped network \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A^{}$$\end{document}A∗ so that we can obtain more accurate and reliable scores. We choose to use an RWR as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} v^{(m+1)} = (1-q)A^{}{D^{}}^{-1}v^{(m)}+qv^{[0]} \quad \forall $m = 1$,2,\dots,\qquad v^{} = \lim _{m\rightarrow \infty }v^{(m)}, \end{aligned}$$\end{document}v(m+1)=(1-q)A∗D∗-1v(m)+qv[0]∀$m = 1$,2,⋯,v∗=limm→∞v(m),where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q\in [0,1]$$\end{document}q∈[0,1] is the restart probability and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D^{}$$\end{document}D∗ is the degree matrix of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A^$$\end{document}A∗. ## Variants of PWN For qualitative analysis purposes, we also consider the following variants of PWN. The first version is an RWR with curvatures, which considers the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K in “Warping via an internal feature: graph curvature” section as a weighted adjacency matrix and applies an RWR on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K. The second variant is uKIN with curvatures, which applies curvatures to uKIN as in PWN. The last version is PWN without curvatures; i.e., it uses the original adjacency matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A$$\end{document}A instead of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K. Note that this is equivalent to PWN with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0$$\end{document}β=0. ## Computing the initial gene scores We then use unbiased high-throughput data to compute the initial gene scores. The high-throughput data are prepared from TCGA data portal.2 We download the transcriptome data containing cancer samples and normal samples for 12 cancer types (breast cancer, colon adenocarcinoma, head-neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, stomach adenocarcinoma, and thyroid cancer) while considering the balanced availability of the two sample types. Then, we identify differentially expressed genes by computing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p values using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document}t-test and combine them using Fisher’s method [35]. In contrast, since mND can handle multidimensional input scores, we do not merge the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p values for mND. Finally, we convert the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p values to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Z$$\end{document}Z-scores as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Z=\Phi (1-p)=-\Phi (p)$$\end{document}Z=Φ(1-p)=-Φ(p), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi$$\end{document}Φ is the cumulative distribution function of the standard normal distribution. ## Collecting PPI networks [36] performed a comprehensive quantitative study comparing the performance and usage of different PPI databases and quantified the agreement between curated interactions shared across 16 major public databases. Among these databases, we exclude 6 databases3 that cannot be downloaded and compare the statistics of 10 databases, which are listed in Additional file 3. We select BioGRID [37] and STRING [38] for our main experiments, since BioGRID and STRING provide the largest sets of PPIs among the available primary and secondary databases, respectively. Furthermore, we only include the edges in STRING that have experimental evidence and confidence scores that are larger than 0.7. We also include additional results using IID [39] in Additional file 4. ## Collecting the ground truth and constructing experiments For most of the experiments, we need both prior knowledge genes and genes to be uncovered, where the former are used by diffusion methods and the latter are required in performance measurements. We choose to randomly divide the ground truths to simulate this experimental design. If the experiments do not need any prior knowledge (see “Effectiveness of internal features” section), we simply use all ground truths as the genes to be uncovered. We crawled the CGC list from COSMIC [40] website4 as our ground truth. The CGC list contains 723 known cancer driver genes, and we remove 9 genes that are not available in the network. Then, we randomly divide the CGC genes into two subsets at a 2:8 ratio. The smaller train set is employed as prior knowledge for the tested methods, while the larger test set represents the relevant genes to be uncovered by the methods. To achieve robustness, we repeat this process 30 times. ## Performance measurement We choose average precision (AveP) and the precision at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}k (Prec@\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}k; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$k = 100$,200$$\end{document}$k = 100$,200) as the performance metrics, where the definitions are followed:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \text {AveP}= & {} \frac{\sum _{k} \text {Prec@}k\times \chi (k\text {-th item is relevant})}{\#\text { of total relevant items}},\\ \text {Prec@}k= & {} \frac{\#\text { of relevant items among the top-}k\text { items}}{k}, \end{aligned}$$\end{document}AveP=∑kPrec@k×χ(k-th item is relevant)#of total relevant items,Prec@k=#of relevant items among the top-kitemsk,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi$$\end{document}χ is an indicator function that returns 1 if the given condition is true, and 0 otherwise. Both are commonly used metrics for imbalanced cases in which the portion of the positive class is tiny. Notice that AveP is an estimator for the area under precision-recall curve (AUPRC) [41], and it’s more preferable than directly computing the area since the latter is often known to give overly-optimistic results due to the curve interpolation [42] while computing AveP does not depend on curve interpolation so there is no such problem. For a fair comparison, we use the exact same prior knowledge and the exact same set of relevant genes for every method in each trial. Additionally, we conduct one-sided paired \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document}t-tests on the performance metrics to verify that the achieved improvement is significant, and adjust the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p values via Benjamini-Hochberg correction [43]. ## Supplementary Information Additional file 1. The PPI network of the Homo sapiens, colored using the curvatures. Additional file 2. Supplementary information for post hoc analysis purposes. Additional file 3. Summary statistics for the primary/secondary PPIs. Additional file 4. 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--- title: 'Subclinical thyroid dysfunction and chronic kidney disease: a nationwide population-based study' authors: - Hye Jeong Kim - Sang Joon Park - Hyeong Kyu Park - Dong Won Byun - Kyoil Suh - Myung Hi Yoo journal: BMC Nephrology year: 2023 pmcid: PMC10031940 doi: 10.1186/s12882-023-03111-7 license: CC BY 4.0 --- # Subclinical thyroid dysfunction and chronic kidney disease: a nationwide population-based study ## Abstract ### Background Chronic kidney disease (CKD) has a significant impact on global health. Studies have shown that subclinical thyroid dysfunction may be related to CKD, but the association between subclinical thyroid dysfunction and CKD in the general population is unclear. We aimed to evaluate the risk of CKD according to thyroid function status in a large cohort. ### Methods We analyzed data from a nationwide, population-based, cross-sectional survey (KNHANES VI). A total of 3,257 participants aged ≥ 19 years who underwent thyroid and kidney function assessments were included in this study. CKD was defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2 and/or urine albumin-creatinine ratio ≥ 30 mg/g. The risk of CKD according to thyroid function status was assessed using logistic regression, adjusted for potential confounders. ### Results Overall, $6.7\%$ of the participants had CKD. There were no significant differences in thyroid-stimulating hormone and free thyroxine levels between the groups with and without CKD. The proportion of participants with CKD was significantly different among the thyroid function status groups ($$p \leq 0.012$$) and tended to increase significantly in the following order: subclinical hyperthyroidism ($1.5\%$), euthyroidism ($6.6\%$), and subclinical hypothyroidism ($12.6\%$) (p for trend < 0.001). Subclinical hypothyroidism was a significant risk factor for CKD, even after adjusting for sex, age, household income, education, smoking, alcohol consumption, walking activity, abdominal obesity, hypertension, low high-density lipoprotein cholesterol, elevated triglycerides, hyperglycemia, free thyroxine, and thyroid-peroxidase anibody (odds ratio 2.161, $95\%$ confidence interval 1.032–4.527, $$p \leq 0.041$$). ### Conclusion Subclinical hypothyroidism is an independent predictor of CKD in the general population. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12882-023-03111-7. ## Introduction Chronic kidney disease (CKD) is a complex and multifaceted disease that causes renal dysfunction and progression to end-stage kidney disease. CKD has a significant impact on global health as a direct cause of morbidity and mortality and is an important risk factor for cardiovascular disease (CVD) [1]. The prevalence of CKD is high and has increased in all age groups over the past few decades due to an increased aging population and prevalence of diabetes, obesity and hypertension [2–5]. This trend is particularly concerning given the high clinical and economic burden associated with the progression of CKD [1]. Thus, early detection and management of risk factors for CKD is very important. The kidney plays a role in the regulation of metabolism and elimination of thyroid hormones [6]. Thyroid hormones are also necessary for the growth and development of the kidney and for the maintenance of water and electrolyte homeostasis [6]. Both hypothyroidism and hyperthyroidism affect renal blood flow, glomerular filtration, tubular function, electrolytes homeostasis, electrolyte pump functions, and kidney structure [6] and lead to increased risk of CVD [7]. Although some studies found no association between subclinical thyroid dysfunction and CKD [8–11], other studies have identified subclinical thyroid dysfunction as a risk factor for CKD [12–15]. Data on subclinical thyroid dysfunction and the risk of CKD in the general population are inconclusive, and more research is needed to fully understand the potential impact of subclinical thyroid dysfunction on kidney health. In the present study we aimed to clarity the risk of CKD according to thyroid function status in a large cohort. ## Study population This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES) VI (2013–2014). The KNHANES is a nationwide, cross-sectional survey conducted by the Korean Centers for Disease Control and Prevention (KCDC) to assess the health and nutritional status of the Korean population [16]. The study participants were selected using stratified multistage cluster sampling and housing census data. Among the participants, approximately 2400 individuals ($\frac{1}{3}$ of the participants aged ≥ 10 years) were selected for laboratory tests of serum thyroid-stimulating hormone (TSH) and free thyroxine (fT4) using stratified subsampling according to sex and age in each year [16]. There were 15,568 study participants, of which 4,343 underwent both thyroid [TSH, fT4 and thyroid-peroxidase antibody (TPOAb)] and kidney (serum creatinine, urine albumin, and urine creatinine) function tests. Participants were excluded for the following reasons: [1] age < 19 years ($$n = 613$$); [2] missing data (questionnaires about household income, education, smoking, alcohol, or exercise; and history of diabetes, hypertension, thyroid disease, cancer, or liver cirrhosis) ($$n = 876$$); [3] history of severe chronic disease, such as any type of cancer or liver cirrhosis ($$n = 109$$); [4] history of thyroid disease, including hyperthyroidism, hypothyroidism, benign thyroid nodules, or Hashimoto’s thyroiditis ($$n = 33$$); [5] use of medication that could influence thyroid function, including radioactive iodine therapy, antithyroid drugs, and/or thyroid hormones ($$n = 24$$); [6] abnormal fT4 levels (< 0.89 ng/dL or > 1.76 ng/dL) ($$n = 93$$); and [7] pregnancy ($$n = 10$$). Several participants met more than two of the exclusion criteria. Finally, 3,257 participants were included in the analysis (Fig. 1). Fig. 1Flow chart of the study population. KNHANES, Korean National Health and Nutrition Examination Survey ## Clinical and anthropometric measurements Household income level, education level, smoking status, alcohol consumption, and walking activity were assessed using a self-reported questionnaire. Household income levels were categorized into four groups according to income quartiles: low, middle-low, middle-high, and high. Educational attainment was classified into four groups: elementary school or lower, middle school, high school, and college or higher. Regarding smoking status, participants were categorized as current, former, or never smokers [17]. For alcohol consumption, participants were classified as excessive (> 21 drinks/week in men and > 14 drinks/week in women) [18], moderate (≤ 21 drinks/week in men and ≤ 14 drinks/week in women), or never drinkers [19]. Walking activity was categorized as active or inactive. Participants were considered active if they walk least 5 days weekly for at least 10 min per day [20]. Physical examinations, including height, weight, waist circumference, and blood pressure, were performed as described in a previous study [21]. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (kg/m2). According to the World Health Organization standards for Asians, BMI was categorized into the following categories: underweight (< 18.5 kg/m2), normal weight (18.5–22.9 kg/m2), overweight (23-24.9 kg/m2) and obese (≥ 25 kg/m2) [22]. ## Laboratory assay Laboratory assays for triglycerides, high-density lipoprotein (HDL) cholesterol, fasting glucose, glycated hemoglobin (HbA1c), and thyroid function tests were performed as described in a previous study [20]. Serum and urine creatinine levels were measured using a Jaffe rate-blanked and compensated method with a Hitachi Automatic Analyzer 7600 − 210 (Hitachi Ltd, Tokyo, Japan). The estimated glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [23]: for females with a serum creatinine level ≤ 0.7 mg/dL, eGFR = 144 × (Serum creatinine/0.7)−0.329 × (0.993)Age; for females with a serum creatinine level > 0.7 mg/dL, eGFR = 144 × (Serum creatinine/0.7)−1.209 × (0.993)Age; for males with a serum creatinine level ≤ 0.9 mg/dL, eGFR = 141 × (Serum creatinine/0.9)−0.411 × (0.993)Age; for males with a serum creatinine level > 0.9 mg/dL, eGFR = 141 × (Serum creatinine/0.9)−1.209 × (0.993)Age. Urine albumin was measured in random urine samples using a turbidimetric assay with a Hitachi Automatic Analyzer 7600 (Hitachi Ltd). The urine albumin-creatinine ratio (ACR) was reported as milligrams of albumin per gram of creatinine (mg/g). ## Definitions Euthyroidism was defined as serum TSH (reference range, 0.62–6.68 mIU/L) [16] and fT4 (laboratory reference range, 0.89–1.76 ng/dL) levels within normal reference ranges. Subclinical hyperthyroidism was defined as TSH levels < 0.62 mIU/L and normal fT4 levels, and subclinical hypothyroidism was defined as TSH levels > 6.68 mIU/L and normal fT4 levels. CKD was defined as an eGFR < 60 mL/min/1.73m2 and/or ACR ≥ 30 mg/g [24]. ## Statistical analysis Weighted sample values were used for analysis to reflect the stratified multistage probability sampling design of the KNHANES VI. Owing to a skewed distribution, a logarithmic transformation of TSH values was used in the analysis. Continuous variables are reported as means (standard error), and categorical variables are presented as weighted percentages (%). The demographic and biochemical characteristics of the study population with respect to CKD were compared using a general linear model for continuous variables and the chi-square test for categorical variables. The prevalence of metabolic syndrome components and CKD markers according to thyroid function status was compared using a general linear model or the chi-square test. Complex sample logistic regression analyses were used to determine the risk of CKD based on thyroid function status. The results are expressed as odds ratios (ORs) with $95\%$ confidence intervals (CIs). All p values and $95\%$ CI for OR were corrected using the Bonferroni method due to multiple testing. Additional adjustments were made for confounding variables, such as age, sex, household income, education, smoking, alcohol consumption, walking activity, abdominal obesity, hypertension, low HDL cholesterol, elevated triglycerides, hyperglycemia, fT4, and TPOAb. All statistical analyses were performed using SPSS Statistics version 26.0 (IBM Corp., Chicago, IL, USA). All tests were two sided, and a p value of < 0.05 was considered statistically significant. ## Results The baseline clinical and biochemical characteristics of the 3,257 participants are presented in Table 1. In the cohort, $54.1\%$ of participants were men, with a mean age of 44.10 (0.28) years. Table 1Baseline characteristics of participants according to chronic kidney disease statusVariablesChronic kidney diseaseOverall(unweighted $$n = 3$$,257)(weighted $$n = 18$$,222,051)No($$n = 3$$,033, $93.3\%$)Yes($$n = 224$$, $6.7\%$)p valueSex0.773 Male (%)54.055.154.1 Female (%)46.044.945.9Age (years)43.34 (0.28)54.69 (1.02)< 0.00144.10 (0.28)Household income level< 0.001 Quartile 1 (lowest) (%)11.620.212.2 Quartile 2 (%)27.133.127.5 Quartile 3 (%)29.730.329.8 Quartile 4 (highest) (%)31.516.430.5Education level< 0.001 Elementary school or lower (%)13.230.014.3 Middle school (%)9.415.49.8 High school (%)40.934.040.4 College or higher (%)36.520.635.4Smoking0.042 Current (%)27.122.826.8 Former (%)18.626.219.1 Never (%)54.351.054.1Alcohol consumption0.149 Excessive (%)10.410.210.4 Moderate (%)82.078.081.7 Never (%)7.611.97.9Walking activity0.497 Active (%)55.552.555.3 Inactive (%)44.547.544.7BMIa (kg/m2)23.74 (0.08)24.92 (0.34)0.00123.82 (0.08) Underweight (%)4.02.90.0033.9 Normal weight (%)39.526.338.6 Overweight (%)24.627.224.8 Obese (%)31.943.632.6Waist circumference Male (cm)83.57 (0.25)88.19 (1.07)< 0.00183.88 (0.24) Female (cm)76.88 (0.34)80.44 (1.20)0.00377.11 (0.33)Abdominal obesityb (%)28.641.6< 0.00129.5Systolic BP (mmHg)115.36 (0.34)127.83 (1.63)< 0.001116.20 (0.35)Diastolic BP (mmHg)75.31 (0.23)78.89 (0.89)< 0.00175.55 (0.23)Hypertensionc29.865.1< 0.00132.2Total cholesterol (mg/dL)187.29 (0.79)201.49 (2.96)< 0.001188.24 (0.79)HDL cholesterol Male (mg/dL)48.14 (0.30)47.24 (1.15)0.44348.08 (0.29) Female (mg/dL)54.25 (0.35)50.73 (1.18)0.00654.02 (0.33)Low HDL cholesterold (%)29.840.40.00830.5Triglycerides (mg/dL)137.14 (2.49)184.92 (11.69)< 0.001140.34 (2.49)Elevated triglyceridese (%)29.450.2< 0.00430.8Fasting glucose (mg/dL)97.03 (0.42)111.06 (2.47)< 0.00197.97 (0.44)HbA1c (%)5.73 (0.02)6.41 (0.11)< 0.0015.78 (0.02)Hyperglycemiaf (%)28.256.2< 0.00130.1eGFR (mL/min/1.73 m2)98.85 (0.30)84.80 (1.95)< 0.00197.90 (0.33)ACR (mg/g)5.37 (0.11)153.07 (27.90)< 0.00115.26 (2.05)TSH (mIU/L)2.23 (0.04)2.22 (0.19)0.1782.23 (0.04)fT4 (ng/dL)1.24 (0.01)1.21 (0.01)0.0641.23 (0.01)TPOAb (IU/mL)26.90 (3.24)30.91 (8.43)0.65527.17 (3.08) Positivity of TPOAbg (%)6.18.50.2366.2BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; HbA1c, glycated hemoglobin; eGFR, estimated glomerular filtration rate; ACR, albumin-creatinine ratio; TSH, thyroid-stimulating hormone; fT4, free thyroxine; TPOAb, anti-thyroid peroxidase antibodyaUnderweight (< 18.5 kg/m2), normal weight (18.5–22.9 kg/m2), overweight (23-24.9 kg/m2) and obese (≥ 25 kg/m2); bWaist circumference ≥ 90 cm in men and ≥ 80 cm in women; cBP ≥ $\frac{130}{85}$ mmHg or undergoing treatment with antihypertensive medication; dHDL cholesterol < 40 mg/dL in men and < 50 mg/dL in women; eTriglycerides ≥ 150 mg/d; fFasting glucose levels ≥ 100 mg/dL, HbA1c ≥ $6.5\%$ or currently on antidiabetic medication; gTPOAb ≥ 34.0 IU/mL. Among the study participants, $6.7\%$ were diagnosed with CKD. Participants with CKD were older, had relatively low household income and education levels, and were more likely to be former smokers than those without CKD. They also had higher BMI, waist circumference, BP, total cholesterol, triglycerides, fasting glucose, HbA1c, and ACR, but lower HDL cholesterol in women and eGFR. However, there were no significant differences between the groups in terms of sex, alcohol consumption, walking activity, and levels of HDL cholesterol in men, TSH, fT4, and TPOAb. We further investigated the metabolic syndrome components and CKD markers according to thyroid function status (Table 2). For metabolic syndrome components, the proportion of participants with low HDL cholesterol levels was significantly different among the three groups ($$p \leq 0.003$$). There were no significant differences among the groups with respect to abdominal obesity, hypertension, elevated triglycerides and hyperglycemia. Table 2Comparison of the prevalence of metabolic syndrome components and chronic kidney disease markers according to thyroid function statusVariablesThyroid function statusp valueSubclinical hyperthyroidism($$n = 77$$, $2.5\%$)Euthyroidism($$n = 3$$,069, $94.0\%$)Subclinical hypothyroidism($$n = 111$$, $3.5\%$)Abdominal obesitya (%)37.729.135.10.206Hypertensionb (%)29.232.234.60.815Low HDL cholesterolc (%)47.029.740.00.003Elevated triglyceridesd (%)30.030.635.90.578Hyperglycemiae (%)27.930.130.70.928eGFR (mL/min/1.73 m2)101.21 (2.13)97.90 (0.35)95.61 (1.49)0.107eGFR < 60 mL/min/1.73 m2 (%)1.51.51.50.999ACR (mg/g)5.92 (0.68)15.66 (2.17)11.16 (1.80)< 0.001ACR ≥ 30 mg/g (%)NA5.811.10.020CKDf (%)1.56.612.60.012MetS, metabolic syndrome; HDL, high-density lipoprotein; BP, blood pressure; eGFR, estimated glomerular filtration rate; ACR, albumin-creatinine ratio; CKD, chronic kidney diseaseaWaist circumference ≥ 90 cm in men and ≥ 80 cm in women; bBlood pressure ≥ $\frac{130}{85}$ mmHg or antihypertensive medication; cHDL cholesterol < 40 mg/dL in men and < 50 mg/dL in women; dTriglycerides ≥ 150 mg/dL; eFasting glucose ≥ 100 mg/dL, glycated hemoglobin (HbA1c) ≥ $6.5\%$ or antidiabetic medication; feGFR < 60 mL/min/1.73 m2 or ACR ≥ 30 mg/g. Regarding CKD markers, ACR levels ($p \leq 0.001$) and the proportion of participants with ACR ≥ 30 mg/g ($$p \leq 0.020$$) were significantly different among the three groups. The proportion of participants with CKD differed significantly among the three groups and tended to increase significantly in the following order: subclinical hyperthyroidism ($1.5\%$), euthyroidism ($6.6\%$) and subclinical hypothyroidism ($12.6\%$) ($$p \leq 0.012$$, p for trend < 0.001). There were no significant differences among the three groups with respect to eGFR and the proportion of participants with eGFR < 60 mL/min/1.73 m2. The results of the logistic regression analyses for the CKD risk based on thyroid function status are shown in Table 3. Participants with subclinical hypothyroidism had a significantly greater risk of CKD than those with euthyroidism (OR 2.039, $95\%$ CI 1.041–3.993, $$p \leq 0.038$$). Additional adjustments were made for confounding variables such as sex, age, household income, education, smoking, alcohol consumption, walking activity, abdominal obesity, hypertension, low HDL cholesterol, elevated triglycerides, hyperglycemia, fT4, and TPOAb. Subclinical hypothyroidism remained a significant risk factor for CKD, even after adjustments (OR 2.161, $95\%$ CI 1.032–4.527, $$p \leq 0.041$$). Table 3Odds ratios (ORs) and $95\%$ confidence intervals (CIs) for chronic kidney disease based on thyroid function statusSubclinical hyperthyroidism($$n = 77$$, $2.5\%$)Euthyroidism($$n = 3$$,069, $94.0\%$)Subclinical hypothyroidism($$n = 111$$, $3.5\%$)Chronic kidney disease Model 10.211 (0.044–1.014)1.0002.039 (1.041–3.993)* Model 20.216 (0.044–1.047)1.0002.178 (1.030–4.604)* Model 30.218 (0.046–1.036)1.0002.161 (1.032–4.527)Model 1, unadjusted; Model 2, with adjustment for age and sex; Model 3 as model 2, with additional adjustment for household income, education, smoking, alcohol consumption, walking activity, abdominal obesity, hypertension, low high-density lipoprotein cholesterol, elevated triglycerides, hyperglycemia, free thyroxine and thyroid-peroxidase antibody$p \leq 0.05$ ## Discussion In the present study, we found that subclinical hypothyroidism independently predicted CKD in the general population and was associated with increased probability of CKD after adjusting for sex, age, household income, education, smoking, alcohol consumption, walking activity, abdominal obesity, hypertension, low HDL cholesterol, elevated triglycerides, hyperglycemia, fT4, and TPOAb. Several studies have reported an association between subclinical thyroid dysfunction, particularly subclinical hypothyroidism, and CKD in the general population [8–15]. Some studies have identified subclinical thyroid hypothyroidism as a risk factor for CKD [12–15]. In a Norwegian population-based study of adults aged ≥ 40 years, CKD was more common in people with subclinical hypothyroidism, and increase in serum TSH levels within the reference range was negatively associated with eGFR [12]. A Taipei City-based cohort study of elderly adults showed that subclinical hypothyroidism was associated with a greater risk of incident CKD [13]. In a cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health of adults aged 35–74 years, subclinical hypothyroidism was associated with an increased risk of CKD [14]. In a large cohort of Taiwanese individuals aged ≥ 20 years, subclinical hypothyroidism was independently associated with reduced eGFR in a dose-dependent manner [15]. However, other studies found no association between subclinical thyroid dysfunction and CKD [8–11]. In an Australian study of community-dwelling older adults aged ≥ 60 years, increasing serum TSH levels were associated with a greater likelihood of prevalent CKD, but no significant association was observed between subclinical hypothyroidism and prevalent CKD [8]. A cross-sectional study including male participants in China showed that TSH was negatively associated with eGFR, but the prevalence of CKD was only significantly higher in participants with TSH level exceeding 7.0 mIU/L [9]. A recent study in a US community-based population of middle-aged adults demonstrated no statistically significant association between subclinical hypothyroidism and the prevalence of CKD [11]. These inconsistent results among studies may be due to differences in the definition of subclinical thyroid dysfunction and CKD as well as differences in the characteristics of the population analyzed, such as age, sex, and region, and adjustments for covariates. Although the prevalence of subclinical hypothyroidism is highly dependent on the applied TSH cut-off, subclinical hypothyroidism was defined differently in each study as TSH levels exceeding 3.50 [12], 4.00 [8–10, 14], or 5.00 mIU/L [11, 13, 15]. Albuminuria is a strong predictor of CKD [24]; however, in most studies [8–12], CKD was defined by eGFR alone using the Modification of Diet in Renal Disease formula or CKD-EPI formula. Two other studies [13, 15] defined CKD as eGFR and/or semi-quantitative measurement of proteinuria using dipstick grading. The main advantage of our study over previous studies is that we used TSH reference ranges for Koreans and quantitative measurements of proteinuria, although not a 24-hr urine collection, and adjusted for confounding factors, including socioeconomic, medical and laboratory factors. Consequently, we found an association between subclinical hypothyroidism and CKD similar to previous studies [12–15]. On the other hand, when we divided CKD into albuminuria and eGFR in this study, we found a statistically significant difference in albuminuria among the three groups according to thyroid function status, but no statistically significant difference in eGFR. And only $9.6\%$ of the participants with albuminuria had overt proteinuria. Since changes in albuminuria is a sensitive measure that can detect kidney damage in its early stage, even before a significant decline in eGFR < 60 mL/min/1.73m2 [25], it is possible that only albuminuria showed a significant difference according to subclinical thyroid dysfunction. Both CKD and subclinical hypothyroidism are known to be associated with CVD risk and increased mortality [1, 26]. Therefore, we adjusted for several metabolic risk factors in this study, and subclinical hypothyroidism was found to be independently associated with CKD. The mechanisms underlying the association between subclinical hypothyroidism and CKD remain to be elucidated. Subclinical hypothyroidism may worsen kidney function through direct and indirect effects such as reductions in cardiac output and renal blood flow, increases in systemic vascular resistance, intrarenal vasoconstriction and alterations in glomerular structure [27]. Thyroid hormone replacement therapy in CKD patients with subclinical hypothyroidism attenuates the rate of eGFR decline [28]. In addition, endothelial dysfunction has been consistently observed in CKD patients of all age groups [29] and patients with subclinical hypothyroidism have been reported to exhibit endothelial dysfunction [30]. Therefore, it seems reasonable to suggest an association between subclinical hypothyroidism and CKD. Further investigation is required in this field. On the other hand, eGFR and renal blood flow are known to increase in patients with hyperthyroidism [6]. However, similar to previous studies [8–12], no significant association between subclinical hyperthyroidism and CKD was observed in this study. Despite the strength of a nationally representative large cohort from the KNHANES and the control of extensive data on several potential confounding factors, including socioeconomic status indicators and medical co-morbidities, our study has some limitations. Due to the cross-sectional design, a causal relationship between subclinical thyroid dysfunction and CKD could not be inferred. Although we comprehensively adjusted for possible confounding factors, a longitudinal study is required to address this issue. Subclinical thyroid dysfunction and alteration in kidney function may be temporary, and repeated measurements of thyroid and kidney functions could provide reliable results. A single measurement may have resulted in the inclusion of transient subclinical thyroid dysfunction or transient decline in kidney function. Due to the lack of a detailed medical history in the KNHANES, secondary causes of CKD, such as polycystic kidney disease or glomerular disease, could not be considered. In addition, several types of medication, such as non-steroidal anti-inflammatory drugs, may interfere with kidney function; however, data regarding medication history are limited. ## Conclusion Our findings demonstrated that subclinical hypothyroidism is associated with CKD, independent of known metabolic risk factors, in the general population. From a CVD prevention perspective, some patients with subclinical hypothyroidism and CKD may benefit from replacement therapy. Further studies on subclinical hypothyroidism and CKD are warranted. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 Supporting information files_data set1 Supplementary Material 2 Supporting information files_data set2 ## References 1. Global regional. 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet (London England) (2020.0) 395 709-33. DOI: 10.1016/S0140-6736(20)30045-3 2. 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--- title: Stellate ganglion block ameliorated central post-stroke pain with comorbid anxiety and depression through inhibiting HIF-1α/NLRP3 signaling following thalamic hemorrhagic stroke authors: - Zhong-Mou Shi - Jun-Jie Jing - Zheng-Jie Xue - Wen-Jun Chen - Yan-Bin Tang - Du-Juan Chen - Xin-Yi Qi - Li Huang - Yi-Qing Zou - Xiao-Zhi Wu - Fei Yang journal: Journal of Neuroinflammation year: 2023 pmcid: PMC10031944 doi: 10.1186/s12974-023-02765-2 license: CC BY 4.0 --- # Stellate ganglion block ameliorated central post-stroke pain with comorbid anxiety and depression through inhibiting HIF-1α/NLRP3 signaling following thalamic hemorrhagic stroke ## Abstract ### Background Central post-stroke pain (CPSP) is an intractable and disabling central neuropathic pain that severely affects patients’ lives, well-being, and socialization abilities. However, CPSP has been poorly studied mechanistically and its treatment remains challenging. Here, we used a rat model of CPSP induced by thalamic hemorrhage to investigate its underlying mechanisms and the effect of stellate ganglion block (SGB) on CPSP and emotional comorbidities. ### Methods Thalamic hemorrhage was produced by injecting collagenase IV into the ventral-posterolateral nucleus (VPL) of the right thalamus. The up-and-down method with von Frey hairs was used to measure the mechanical allodynia. Behavioral tests were carried out to examine depressive and anxiety-like behaviors including the open field test (OFT), elevated plus maze test (EPMT), novelty-suppressed feeding test (NSFT), and forced swim test (FST). The peri-thalamic lesion tissues were collected for immunofluorescence, western blotting, and enzyme-linked immunosorbent assay (ELISA). Genetic knockdown of thalamic hypoxia-inducible factor-1α (HIF-1α) and NOD-like receptor thermal protein domain associated protein 3 (NLRP3) with microinjection of HIF-1α siRNA and NLRP3 siRNA into the VPL of thalamus were performed 3 days before collagenase injection into the same regions. Microinjection of lificiguat (YC-1) and MCC950 into the VPL of thalamus were administrated 30 min before the collagenase injection in order to inhibited HIF-1α and NLRP3 pharmacologically. Repetitive right SGB was performed daily for 5 days and laser speckle contrast imaging (LSCI) was conducted to examine cerebral blood flow. ### Results Thalamic hemorrhage caused persistent mechanical allodynia and anxiety- and depression-like behaviors. Accompanying the persistent mechanical allodynia, the expression of HIF-1α and NLRP3, as well as the activities of microglia and astrocytes in the peri-thalamic lesion sites, were significantly increased. Genetic knockdown of thalamic HIF-1α and NLRP3 significantly attenuated mechanical allodynia and anxiety- and depression-like behaviors following thalamic hemorrhage. Further studies revealed that intra-thalamic injection of YC-1, or MCC950 significantly suppressed the activation of microglia and astrocytes, the release of pro-inflammatory cytokines, the upregulation of malondialdehyde (MDA), and the downregulation of superoxide dismutase (SOD), as well as mechanical allodynia and anxiety- and depression-like behaviors following thalamic hemorrhage. In addition, repetitive ipsilateral SGB significantly restored the upregulated HIF-1α/NLRP3 signaling and the hyperactivated microglia and astrocytes following thalamic hemorrhage. The enhanced expression of pro-inflammatory cytokines and the oxidative stress in the peri-thalamic lesion sites were also reversed by SGB. Moreover, LSCI showed that repetitive SGB significantly increased cerebral blood flow following thalamic hemorrhage. Most strikingly, SGB not only prevented, but also reversed the development of mechanical allodynia and anxiety- and depression-like behaviors induced by thalamic hemorrhage. However, pharmacological activation of thalamic HIF-1α and NLRP3 with specific agonists significantly eliminated the therapeutic effects of SGB on mechanical allodynia and anxiety- and depression-like behaviors following thalamic hemorrhage. ### Conclusion This study demonstrated for the first time that SGB could improve CPSP with comorbid anxiety and depression by increasing cerebral blood flow and inhibiting HIF-1α/NLRP3 inflammatory signaling. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12974-023-02765-2. ## Introduction Central post-stroke pain (CPSP), occurring after hemorrhagic or ischemic stroke concerning the somatosensory system, is categorized as central neuropathic pain syndrome. The prevalence rate of CPSP is reported at 8–$46\%$ following a hemorrhagic stroke due to the heterogeneity of affected brain areas, with a higher chance in patients who suffer a thalamic stroke [1, 2]. This intolerable and persistent pain condition severely affects the patient's quality of life and rehabilitation and imposes enormous medical and financial burdens. However, current pharmacological and non-pharmacological regimens for CPSP are far from satisfactory and often result in undesirable side effects [3–5]. The pathophysiology of CPSP remains largely unknown, which hinders the development of novel therapeutic strategies. More seriously, CPSP is frequently comorbid with psychiatric disorders, in particular anxiety and depression, which exacerbate the duration and severity of pain and drive a vicious cycle between pain and negative emotion, ultimately making CPSP more resistant to therapy [6–8]. Thus, there is an urgent need to understand the mechanisms underlying CPSP with comorbid anxiety and depression and to explore a new and effective treatment for these conditions. Accumulating evidence indicates that inflammatory responses involving the immunologically active glial cells and the pro-inflammatory cytokines and chemokines play an essential role in ischemic or hemorrhagic stroke. It has been demonstrated that the maladaptive neuroinflammation following stroke contributes to the disease process in CPSP. Systemic depletion of microglia with PLX3397 effectively prevents mechanical allodynia caused by thalamic hemorrhagic stroke [9]. Inhibition of local inflammatory cytokines (IL-1β, TNF-α) and chemokines (CXCL12/CXCR4) signaling as well as glial cell activation in the damaged thalamus also markedly ameliorated CPSP symptoms transiently [10–14]. Notably, microglia activation has also been implicated in thalamic hemorrhage-induced depression [15], suggesting that targeting microglia-mediated inflammatory cascades may be rational for treating CPSP and emotional comorbidity. Our previous study revealed that hypoxia-inducible factor-1 alpha (HIF-1α), an oxygen-dependent transcriptional activator, was the initiator of neuroinflammation following thalamic hemorrhagic stroke and was involved in the genesis of CPSP by boosting glial cell activation and driving the expression of pro-inflammatory cytokines [14]. Recent studies showed that the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome, an essential downstream target of HIF-1α after stroke, was extremely upregulated in the damaged thalamus of the CPSP model, and the NLRP3 inflammasome inhibitor was able to attenuate CPSP syndrome [16–19]. Moreover, inhibiting HIF-1α/NLRP3 pathway could improve inflammation-induced depressive-like behaviors [20]. Thus, exploiting a novel and effective approach targeting the HIF-1α/NLRP3 signaling-mediated neuroinflammation may open a new avenue for treating CPSP and emotional comorbidity. Stellate ganglion block (SGB) is a commonly used and effective method for temporarily blocking the cervical sympathetic trunk. A large body of data suggests that SGB significantly improves the prognosis of cerebrovascular events by alleviating cerebral vascular spasm, increasing brain oxygen supply, reducing the inflammatory response, and decreasing oxidative stress [21–24]. Recently, SGB has emerged as a novel treatment for various pathological pain, such as complex regional pain syndrome, postoperative pain, and orofacial pain [25–27]. For CPSP, case studies showed that a single SGB treatment considerably alleviated somatic pain and decreased the usage of analgesic medicines in CPSP patients and that the analgesic effects of a single SGB therapy lasted at least one month [28, 29]. However, the underlying analgesic mechanisms of SGB on CPSP remain unclear. In addition, experimental and clinical studies have shown that SGB exhibits a significant protective effect against anxiety and depression-like behaviors [30–33]. In the light of these findings, we are therefore interested to know whether SGB could improve emotional comorbidities associated with CPSP, and if so, what the cellular and molecular mechanisms are. In this study, we provide preclinical evidence that addresses the CPSP and its anxious and depressive consequences using a reproducibly established thalamic hemorrhagic stroke model and investigate the role of SGB in inhibiting HIF-1α/NLRP3 signaling to improve CPSP with comorbid anxiety and depression. ## Animals A total of 260 male Sprague-Dawley rats weighing 200–250 g were used for this study. Among the 260 rats, 13 were excluded from the study due to death during the experiment. All rats were provided by the Laboratory Animal Center of 900th Hospital. Rats were housed five per cage under controlled temperature (23 ± 1 °C) and humidity (50 ± $5\%$) with a 12 h light/12 h dark cycle and free access to food and water. All rats were acclimated in the test room for 5–7 days before behavioral experiments and all behavioral tests were conducted between 9:00 and 16:00 by using blind methods. Rats were randomly assigned throughout the whole trial. This study was conducted following the Animal Care and Use Committee of 900th Hospital (Authorization No.: 2021-006) and the National Institutes of Health guide for the care and use of laboratory animals (NIH Publications No. 8023, revised 1978). The number of rats used was minimized, as was their suffering. ## CPSP surgery Surgery was performed as previously described [14, 34, 35]. Rats were anesthetized with $3\%$ isoflurane and placed on a stereotaxic apparatus (RWD Life Technology, Shenzhen, China), and maintained with 1.5–$2.0\%$ isoflurane via nose cone at a speed of 0.5 L/min. After cutting the scalp, swab the periosteum with a sterile dry cotton ball to separate the periosteum to expose the skull. Craniotomies were performed with a pneumatic drill to cause minimal damage to cortical tissue. Collagenase type IV (0.025 U/0.25 μl, Sigma-Aldrich China, Shanghai) or saline (0.25 μl) were microinjected into the ventral-posterolateral nucleus (VPL) of the right thalamus using a 1-µl syringe and 33-gauge needle. The stereotaxic coordinates of the injections were 3.48 mm anterior–posterior to the bregma, 3.6 mm lateral to the midline, and 6.2 mm ventral to the brain surface to target the VPL. After each injection, the need was left for approximately 5 min to prevent the agent from spreading to the surface of the brain. The needle was then slowly withdrawn, and the skin was sutured with 4.0 sutures. During the surgery, the rats were placed on a thermal blanket maintained at 37 °C to prevent hypothermia. After the surgery, all rats were allowed to recover in individual cages for 7 days with free access to food and water. Naïve rats were fed under the same conditions in a parallel manner. ## Intra-thalamic drug injections Rats were intra-thalamic injected with lificiguat (YC-1, Selleck, 0.2 mM) or MCC950 (Selleck, 0.2 mM) in a total volume of 1 μl over 10 min and which was performed 30 min before collagenase injection. The doses used in this study were based on previous studies [14, 36]. YC-1, a well-established HIF-1α inhibitor, was used to confirm the regulatory effects of HIF-1α on the development of CPSP. MCC950, which is an NLRP3 selective inflammasome-specific inhibitor, was used to evaluate the roles of NLRP3 in the development of CPSP. Both YC-1 and MCC950 were dissolved in $1\%$ dimethyl sulfoxide (DMSO). The control group received an equal volume intra-thalamic injection of the DMSO vehicle. To confirm that thalamic HIF-1α/ NLRP3 signaling mediated the therapeutic effect of SGB, we pharmacologically activated thalamic HIF-1α and NLRP3 by intra-thalamic injection of nigericin (Selleck, 0.2 mM, 1 μl) and DMOG (Selleck, 0.2 mM, 1 μl) at 9, 11, and 13 days in rats given repetitive SGB after intra-thalamic collagenase injection. The intra-thalamic injection methods were identical to the above-mentioned and the injection locations were examined histologically. ## siRNA preparation and microinjection The sequence of HIF-1α siRNA (sense: 5′-GGAAACGAGUGAAAGGAUATT-3′; anti-sense: 5′-UAUCCUUUCACUCGUUUCCAA-3′) and NLRP3 siRNA (sense, 5′ -GCAUGCACGUCUAAUCUCUTT-3′; anti-sense, 5’ -AGAGAUUAGACGUGCAUGCAT-3′) and negative control scrambled siRNA (sense, 5′-UUCUCCGAACGAGUCACGUTT-3′; anti-sense, 5′-ACGUGACUCGUUCGGAGAATT-3′) was designed and chemically synthesized by SunYa Biotechnology (Fuzhou, China). The RNase-free water and in vivo transfection reagent was used to dissolve siRNA reaching the final concentration of 2 μmol/L. 10 μL of the diluted siRNA solution was microinjected into the ventral-posterolateral nucleus (VPL) of the right thalamus in the same manner as the Collagenase type IV microinjection described above. ## Stellate ganglion block Rats were anesthetized by inhalation of $3\%$ isoflurane and then placed prone on an operating table. The cartilaginous process of the spinous process of the seventh cervical vertebra was palpated, and a 1-ml syringe needle was inserted forward along the right sagittal position of the seventh cervical vertebra. When the needle tip lost contact with the vertebral body, it was slightly retracted about 0.5 mm. After confirming that no blood or cerebrospinal fluid is seen in the retracted syringe, 0.2 ml of $0.25\%$ ropivacaine was injected, while the control group received an equal volume of saline injection. After awakening from anesthesia, the typical Horner syndromes such as sagging eyelids, narrowed eye fissures, and constricted pupils on the block side in SGB rats were the signs of a successful block. Otherwise, we judged the SGB was unsuccessful, and these rats were excluded from the study. ## Mechanical pain sensitivity testing To evaluate mechanical allodynia, mechanical stimuli were applied using a series of von Frey monofilaments with varying bending forces. The rats were placed on a metal mesh floor covered by a 20 × 20 × 20 cm transparent plastic box and acclimatized for 30 min. Von Frey filaments were applied from underneath the metal mesh floor to the plantar surface of the hind paws vertically. The mechanical stimulus began with the smallest bending force and progressively increased in intensity. Each von Frey filament was applied 10 times (once every several seconds) to elicit the withdrawal response. Paw withdrawal mechanical threshold (PWMT, g) was defined as the lowest von Frey filament bending force value at which paw withdrawal occurred at a frequency of $50\%$. The PWMT of bilateral hindpaws was measured. ## Open field test (OFT) The rats were acclimated to the test room for at least 1 h before the OFT. During the experiment, the test room was kept at a constant temperature and humidity (24 °C and $55\%$ relative humidity) with dim lighting and no noise. The bottom of the 100 × 100 × 40 cm black open field box was divided into 16 grids, with the center 4 grids being the central area and the other 12 grids being the surrounding regions. The rats were placed gently in the center area of the open field box and allowed to move freely for 5 min. The time spent and the traveled distance in the center area as well as the total traveled distance were recorded and analyzed by video tracking software (SMART v.3.0 software, RWD Life Science). The less time and distance in the central area indicated the higher the anxiety level. After each experiment, clean the box carefully with $75\%$ ethanol to remove rat excrement and odor. The following experiment will be conducted once the ethanol has evaporated. ## Elevated plus maze test (EPMT) The elevated plus-maze apparatus consists of 4 arms (10 × 50 cm) and a central platform (10 × 10 cm) elevated 50 cm above the floor. Two closed arms were enclosed with 30-cm-high walls crossing with two open arms (with no walls). The rats were placed in the central area facing an open arm and were allowed to freely explore the maze for 5 min. The time spent and the traveled distance in the open arm as well as the total traveled distance were recorded and analyzed by video tracking software (SMART v.3.0 software, RWD Life Science). The less time and distance in the open arm indicated the higher the anxiety level. After each experiment, clean the box carefully with $75\%$ ethanol to remove rat excrement and odor. The following experiment will be conducted once the ethanol has evaporated. ## Novelty-suppressed feeding test (NSFT) The was performed as described previously [37]. Briefly, 24 h before testing, the rats were food-deprived, and only water was available. During testing, each rat was placed in one corner of a plastic box (100 × 100 × 40 cm) with wooden bedding covered floor and allowed to explore for a maximum of 10 min. Three food pellets were placed on a circular filter paper in the center of the arena. The rats were placed in one corner of the box and the time taken to bite food, not simply sniff or touch the pellet was recorded as feeding latency. If a rat did not bite food within 10 min, the feeding latency was recorded as 10 min. ## Forced swim test (FST) The rats were placed in a plastic cylinder (30 cm diameter, 50 cm height) containing 30 cm of water at a temperature of 25 ± 1 °C. The activities of rats during a 5-min test were recorded by the video system, and the accumulated immobility time was counted. Immobility was defined as the rats floating with no active movements other than those necessary to maintain the head and nose above the water. The duration of immobility was strongly linked with the severity of depression in rats. The rats were dried with a towel immediately and returned to their home cages after the test. ## Western blotting Ipsilateral thalamus tissue around the hemorrhagic lesion sites was dissected from the brain of rats and lysed using RIPA lysis buffer containing protease and phosphatase inhibitors, and centrifuged at 12,000 rpm for 10 min. The total protein concentration of the lysate was measured using a BCA protein assay kit (Solarbio, Beijing, China), and heated at 100 °C for 10 min. Equal amounts of proteins were resolved by SDS-PAGE and then transferred to PVDF membranes (Millipore, Molsheim, France). Membranes were blocked with $5\%$ skim milk (Solarbio, Beijing, China) at room temperature for 1 h and then incubated with primary antibodies overnight at 4 °C. Primary antibodies included rabbit anti-HIF-1α (1:1000, Bioswamp, Wuhan, China), rabbit anti-Caspase 1 (1:1000, Bioswamp, Wuhan, China), rabbit anti-NLRP3 (1:1000, Bioswamp, Wuhan, China), and rabbit anti-Iba-1 (1:1000, Bioswamp, Wuhan, China), rabbit anti-GFAP (1:1000, Bioswamp, Wuhan, China). On the following day, membranes were washed three times with PBST and then incubated with HRP-conjugated secondary antibody (Goat anti-Rabbit IgG, 1:20,000, Bioswamp, Wuhan, China) for 1 h at room temperature. Membranes were visualized with a chemiluminescent solution (Millipore, Molsheim, France) and detected in an automatic chemiluminescence analyzer (Tanon, Shanghai, China) with TANON GIS software (Tanon, Shanghai, China). The scanned images were quantified using ImageJ software. Specific bands for each protein were normalized to its respective β-actin control. ## Enzyme-linked immunosorbent assay (ELISA) Ipsilateral thalamus tissue around the hemorrhagic lesion sites was dissected from the brain of rats and lysed using RIPA lysis buffer containing protease and phosphatase inhibitors, and centrifuged at 12,000 rpm for 10 min. After quantitative measurement of the total protein concentrations with a BCA protein assay kit (Solarbio, Beijing, China), the homogenized thalamus tissue was assayed for HIF-1α, TNF-α, IL-1β, IL-6, IL-33, CCL-12, MDA and SOD by ELISA kits (Bioswamp, Wuhan, China). Each protein of interest was expressed as picograms or nanogram per milligram of total proteins and SOD-specific activity is represented as U/mg of protein. ## Immunofluorescence Rats were anesthetized with pentobarbital sodium (50 mg/ kg, i.p.) and transcardially perfused with saline followed by $4\%$ paraformaldehyde. Brain tissues were quickly removed, and postfixed overnight in $4\%$ paraformaldehyde at 4 °C. After tissue dehydration/rehydration, brains were paraffin-embedded. The thalamus region was sectioned into 10-μm-thick slices using a microtome, and the sections were deparaffined in xylene and hydrated through a series of graded ethanol. Antigens were activated by microwave heating in EDTA antigen repair buffer (pH = 8.0) for 10–30 min. After washing in PBS, sections were incubated with normal goat serum for 1 h at room temperature, followed by overnight incubation at 4 °C in a humid chamber with primary antibodies, rabbit anti-Iba-1 (1:200, Bioswamp, Wuhan, China) or rabbit anti-GFAP (1:200, Bioswamp, Wuhan, China). The sections were then washed and incubated for 1 h at room temperature with an anti-rabbit secondary antibody (1:300, GB21303, Servicebio, Wuhan, China). The images were examined and captured under a fluorescent microscope (Nikon, Japan), and the quantification of cell count was completed with ImageJ software. ## Morphological analysis of microglia As previously described, the morphology of microglia was examined by the skeleton and fractal analysis using the ImageJ software [38]. Briefly, the immunofluorescence images were first converted to binary images, followed by skeletonization using Image J software. The Analyze Skeleton (2D/3D) plugin was then used in all skeletonized images to identify elements of the microglial cytoskeleton, and the number of endpoints and the length of processes were examined automatically. ## Laser speckle contrast imaging (LSCI) The rats were anesthetized with $3\%$ isoflurane and placed on a stereotaxic apparatus (RWD Life Technology, Shenzhen, China), and maintained with 1.5–$2.0\%$ isoflurane via nose cone at a speed of 0.5 L/min. After making a 15-mm midline skin incision, a high-speed skull drill was used to thin the skull (10 mm × 10 mm) under a microscope until the blood veins were visible. The drill was cooled repeatedly with saline to avoid heat damage to brain tissue. The LSCI system (Wuhan SIM Optoelectronics Technology Co., Wuhan, China) includes an Olympus ZS61 microscope, a continuous wavelength laser diode (λ = 785 nm), and a charge-coupled device camera. The thinned skulls were illuminated vertically by the continuous wavelength (λ = 785 nm) laser source, and the images reflected by the biological tissue were captured by an in-system CCD imaging system. A random interference pattern is created due to the mutual interference of scattered light through different optical paths, and a pseudo-color image is generated by computer processing automatically. The configuration of blood vessels and the direction of blood flow on the surface of brain tissue were examined under a microscope. The blood flow index (BFI) reflected by the gray average value of the cortical blood flow image was examined and calculated by ImageJ software. All the rats were kept warm during the test and then housed in a single cage after awakening with access to food and water. ## Statistical analysis Data are expressed as mean ± SEM. All data were statistically analyzed using SPSS 22.0 and GraphPad Prism 8.0. The sample sizes were based on our previous knowledge and experience with this design. The normality test was performed by the Shapiro–Wilk test. The homogeneity of variance test was performed by Levene’s test. Data that met these two conditions were analyzed by one-way, two-way, or two-way RM ANOVA followed by Tukey’s post hoc test. Data with unequal variance were compared by Welch ANOVA followed by Tamhane’s T2 post hoc test. A level of $p \leq 0.05$ was considered statistically significant. ## Thalamic hemorrhagic stroke induced mechanical allodynia and anxiety- and depression-like behaviors in rats The thalamic hemorrhagic stroke model was established by injecting collagenase into the right thalamic ventral-posterolateral nucleus to mimic clinical CPSP. Consistent with our previous studies [14, 34, 35], thalamic hemorrhagic stroke induced by intra-thalamic collagenase injection (ITC) developed bilateral mechanical allodynia within 7 days and persisted at least for 28 days (Fig. 1A–D). During the whole observation period, intra-thalamic saline injection (ITS) did not affect bilateral paw withdrawal mechanical threshold compared to the naïve group (Fig. 1A–D).Fig. 1ITC rats exhibited mechanical allodynia and anxiodepressive-like behaviors. A Experimental timeline for surgical procedure and behavior tests. B Schematic diagram showing the injection site of ITC (top) and representative photomicrograph of brain slice showing the hemorrhagic lesion location following ITC (bottom). Scale bar = 1 mm. C The PWMT of the contralateral hindpaw was significantly decreased 7 days after ITC and persisted throughout the testing period, as did the area under curve of the contralateral hindpaw PWMT ($$n = 8$$, PWMT: group, F2, 21 = 15.53, day, F4, 84 = 25.63, group × day, F8, 84 = 16.45, ###$p \leq 0.001$ vs Naive; AUC: F2, 21 = 27.71, ##$p \leq 0.01$). D The PWMT of the ipsilateral hindpaw was significantly decreased 7 days after ITC and persisted throughout the testing period, as did the area under curve of the ipsilateral hindpaw PWMT ($$n = 8$$, PWMT: group, F2, 21 = 22.95, day, F4, 84 = 29.24, group × day, F4, 84 = 19.49, ###$p \leq 0.001$ vs Naive; AUC: F2, 21 = 19.18, ##$p \leq 0.01$). E Representative track plot in the EPMT. F ITC decreased the time spent and traveled distance in the open arms, but had no effect on the total traveled distance in EPMT ($$n = 8$$, time in open arm: F2, 21 = 61.14, ###$p \leq 0.001$; distance in open arm: F2, 21 = 32.31, ###$p \leq 0.001$; total distance: ns, no significance). G Representative track plot in the OFT. H ITC decreased the time spent and traveled distance in central area, but had no effect on the total traveled distance in the OFT ($$n = 8$$, time in central area: F2, 21 = 28.00, ###$p \leq 0.001$; distance in central area: F2, 21 = 35.95, ###$p \leq 0.001$; total distance: ns, no significance). I ITC increased the latency to feed in the NSFT ($$n = 8$$, F2, 21 = 38.23, ###$p \leq 0.001$). J ITC increased the immobility time in the FST ($$n = 8$$, F2, 21 = 97.32, ###$p \leq 0.001$). Data are expressed as mean ± SEM, one-way ANOVA followed by the Tukey test Neuropathic pain is frequently accompanied by psychiatric disorders, such as anxiety and depression [39–41]. Thus, on day 14 after intra-thalamic collagenase injection, when the mechanical allodynia is steady, we conducted the elevated plus maze test and open field test to evaluate anxiety-like behaviors (Fig. 1A). In the elevated plus maze test, ITC significantly reduced the time and traveled distance in open arms with no effect on the total traveled distance compared with naïve group. In accordance with the elevated plus maze test result, ITC markedly reduced the time and traveled distance in the central area in the open field test, but had no effect on the total traveled distance compared with the naïve group (Fig. 1E–H). There was no significant difference between the naive group and the ITS group in the elevated plus maze test and open field test (Fig. 1E–H). Next, we performed the novelty-suppressed feeding test and forced swim test 16 and 18 days after intra-thalamic injection to evaluate depressive-like behaviors, respectively (Fig. 1A). In the novelty-suppressed feeding test, ITC significantly increased the latency to feed, while ITS had no effect compared with the naïve group (Fig. 1I). In parallel, ITC significantly increased the immobility time, while ITS had no effect compared with naïve group in the forced swim test (Fig. 1J). These results suggest that ITC-induced CPSP rats exhibited anxiodepressive-like behaviors. ## ITC activated microglia and astrocytes and upregulated HIF-1α and NLRP3 in the peri-thalamic lesion sites To investigate the activation of microglia and astrocyte in the peri-thalamic lesion site induced by ITC, we performed immunofluorescent staining for glial fibrillary acidic protein (GFAP, an astrocyte marker) and ionized calcium binding adapter molecule 1Iba-1(Iba-1, a microglia marker) 7 and 21 days after intra-thalamic injection. As shown in Fig. 2A–D, ITC substantially enhanced the number of Iba-1 and GFAP-positive cells compared to the ITS group at 7 and 21 days. We further examine the morphological characterization of Iba-1-labeled cells in ITS and ITC group (Fig. 2B). At 7 and 21 days, the endpoints and process lengths of Iba-1-labeled cells were dramatically reduced in the ITC group relative to the ITS group (Fig. 2E and F). In conformity with the immunofluorescent results, western blotting showed the expression level of Iba-1 and GFAP in the peri-thalamic lesion site were highly increased in the ITC group at 7 and 21 days compared to the naïve group, whereas ITS had no influence on the expression level of Iba-1 and GFAP (Fig. 2G–I). These results suggest that microglia and astrocytes were significantly activated in the peri-thalamic lesion site in the CPSP rats. Our previous studies have demonstrated that HIF-1α is involved in the development of CPSP [14]. Here, we examined the expression of HIF-1α and NLRP3 by the western blotting approach. Compared to the naïve group, ITS did not affect the expression level of HIF-1α and NLRP3 at 7 and 21 days, whereas HIF-1α and NLRP3 were extremely upregulated in the peri-thalamic lesion site in the ITC group (Fig. 2J–L).Fig. 2ITC activated microglia and astrocytes and upregulated HIF-1α and NLRP3 in the peri-thalamic lesion sites of rats. A Representative immunofluorescence images showing the time course expressions of GFAP and Iba-1, in the peri-thalamic lesion sites. Scale bar = 100 μm. B Representative magnified images of microglia (top) and the corresponding black-and-white, skeletonized images (bottom) in the peri-thalamic lesion sites. Scale bar = 25 μm. C, D Quantification of cell number showed ITC increased Iba-1 (C) and GFAP (D) positive cells in in the peri-thalamic lesion sites at 7 and 21 d $$n = 5$$, Iba-1: F3, 16 = 167.2, *$p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d; $$n = 5$$, GFAP: F3, 16 = 243.1, $p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d). E Quantification of process length showed ITC reduced the process length of microglia at 7 and 21 d ($$n = 5$$, F3, 16 = 20.71, $p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d). F Quantification of endpoint showed ITC decreased the endpoints in microglia at 7 and 21 d ($$n = 5$$, F3, 16 = 22.08, $p \leq 0.01$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d). G Representative western blots of Iba-1 and GFAP expression in total proteins of the peri-thalamic lesion sites in naïve, ITS, and ITC rats. H Quantitative summary result showed ITC increased Iba-1 expression at 7 and 21 d, while ITS had no effect ($$n = 3$$, F4, 10 = 46.93, $p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.01$ vs ITS-21 d). I Quantitative summary result showed ITC increased GFAP expression at 7 and 21d, while ITS had no effect ($$n = 3$$, F4, 10 = 33.41, $p \leq 0.001$ vs ITS- 7d, ###$p \leq 0.001$ vs ITS-21 d). J Representative western blots of HIF-1α and NLRP3 expression in total proteins of the peri-thalamic lesion sites in naïve, ITS, and ITC rats. K Quantitative summary result showed ITC increased HIF-1α expression at 7 and 21 d, while ITS had no effect ($$n = 3$$, F4, 10 = 91.24, $p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d). L Quantitative summary result showed ITC increased NLRP3 expression at 7 and 21 d, while ITS had no effect ($$n = 3$$, F4, 10 = 67.22, $p \leq 0.001$ vs ITS-7 d, ###$p \leq 0.001$ vs ITS-21 d). Data are expressed as mean ± SEM, one-way ANOVA followed by Tukey test ## Genetic knockdown or pharmacological inhibition of HIF-1α and NLRP3 prevented mechanical allodynia and anxiety- and depression-like behaviors in CPSP rats To confirm the involvement of HIF-1α and NLRP3 in CPSP and associated anxiety and depression, we knocked down thalamic HIF-1α and NLRP3 through microinjection of HIF-1α siRNA or NLRP3 siRNA into the VPL of thalamus 3 days before microinjection of collagenase into the same regions (Fig. 3A). As expected, the amount of HIF-1α and NLRP3 protein in the thalamus of ITC rats was decreased by pre-microinjection with HIF-1α siRNA or NLRP3 siRNA (Fig. 3B). Compared with the scrambled siRNA microinjection, pre-microinjection with HIF-1α siRNA or NLRP3 siRNA significantly prevented the bilateral mechanical allodynia at 7 to 28 days post-collagenase injection (Fig. 3C and D). In the open field test, pre-microinjection with HIF-1α siRNA or NLRP3 siRNA significantly increased the time and traveled distance in the central area compared to the control group, but had no effect on the total distance (Fig. 3E and F). Compared with the scrambled siRNA microinjection, pre-microinjection with HIF-1α siRNA or NLRP3 siRNA also reduced the feeding latency in novelty-suppressed feeding test (Fig. 3G) and the immobility in forced swim test (Fig. 3H).Fig. 3Intra-thalamic injection of HIF-1α siRNA or NLRP3 siRNA significantly prevented ITC-induced mechanical allodynia and anxiodepressive-like behaviors in rats. A The experimental timeline of surgical procedure and behavior tests. B Both HIF-1α siRNA and NLRP3 siRNA silenced the expression of HIF-1α and NLRP3 in total proteins of the peri-thalamic lesion sites in ITC rats. ( $$n = 5$$, HIF-1α: F2, 12 = 143.9, ###$p \leq 0.001$ vs ITC + siRNA-NC; NLRP3: F2, 12 = 248.2, ###$p \leq 0.001$ vs ITC + siRNA-NC). C Temporal changes of PWMT in contralateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the contralateral hindpaw PWMT ($$n = 5$$, PWMT: group, F2, 12 = 13.49, day, F4, 48 = 14.54, group × day, F8, 48 = 2.182, #$p \leq 0.05$, ##$p \leq 0.01$, ###$p \leq 0.001$ ITC + siRNA-NC vs ITC + HIF-1α-siRNA, $p \leq 0.05$, **$p \leq 0.001$, ITC + siRNA-NC vs ITC + NLRP3-siRNA; AUC: F2, 12 = 20.89, ###$p \leq 0.001$). D Temporal changes of PWMT in ipsilateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the ipsilateral hindpaw PWMT ($$n = 5$$, PWMT: group, F2, 12 = 11.54, day, F4, 48 = 21.28, group × day, F12, 48 = 2.796, #$p \leq 0.05$, ##$p \leq 0.01$, ###$p \leq 0.001$, ITC + siRNA-NC vs ITC + HIF-1α-siRNA, $p \leq 0.05$, $p \leq 0.01$, ITC + siRNA-NC vs ITC + NL; RP3-siRNA; AUC: F2, 12 = 16.86, ##$p \leq 0.01$, ###$p \leq 0.001$). E Representative track plot in the OFT. F HIF-1α siRNA and NLRP3 siRNA increased the time spent and traveled distance traveled in central area, and but had no effect on the total traveled distance in the OFT ($$n = 5$$, time in central area: F2, 12 = 10.51, ##$p \leq 0.01$; distance in central area: F2, 21 = 9.466, #$p \leq 0.05$; total distance: ns, no significance). G HIF-1α siRNA and NLRP3 siRNA decreased the latency to feed in the NSFT ($$n = 5$$, F2, 12 = 24.43, ###$p \leq 0.001$). H HIF-1α siRNA and NLRP3 siRNA decreased the immobility time in the FST ($$n = 5$$, F2, 12 = 5.035, #$p \leq 0.05$). Data are expressed as mean ± SEM, one-way ANOVA followed by Tukey test We next inhibited HIF-1α and NLRP3 by intra-thalamic injection of YC-1 (HIF-1α inhibitor) and MCC950 (NLRP3 inhibitor) and assessed mechanical pain sensitivity and anxiety- and depression-like behaviors (Fig. 4A). As shown in Fig. 4B and C, a single intra-thalamic injection of YC-1 and MCC950 prevented the development of bilateral mechanical allodynia compared to the control group, with the analgesic effect lasting for 14 days. In the open field test, intra-thalamic injection of YC-1 and MCC950 in ITC rats significantly increased the time and traveled distance in the central area compared to the control group, but had no effect on the total distance (Fig. 4D and E). In contrast to the control group, intra-thalamic injection of YC-1 and MCC950 remarkably reduced the feeding latency in novelty-suppressed feeding test (Fig. 4F) and the immobility in forced swim test (Fig. 4G). All these results suggest that inhibiting HIF-1α and NLRP3 prevented CPSP-related anxiety and depression. Fig. 4Intra-thalamic injection of YC-1 or MCC950 significantly prevented ITC-induced mechanical allodynia and anxiodepressive-like behaviors in rats. A The experimental timeline of surgical procedure and behavior tests. B Intra-thalamic injections of YC-1 and MCC950 increased the PWMT of the contralateral hindpaw and the area under the curve of the PWMT following ITC ($$n = 8$$, PWMT: group, F2, 21 = 6.30, day, F4, 84 = 94.81, group × day, F8, 84 = 7.687, ###$p \leq 0.001$, ITC + YC-1 vs ITC + Veh, $p \leq 0.001$, ITC + Veh vs ITC + MCC950; AUC: F2, 21 = 10.48, ##$p \leq 0.01$). C Intra-thalamic injections of YC-1 and MCC950 increased the PWMT of the ipsilateral hindpaw and the area under the curve of the PWMT following ITC ($$n = 8$$, PWMT: group, F2, 21 = 6.485, day, F4, 84 = 81.73, group × day, F8, 84 = 8.051, ###$p \leq 0.001$, ITC + YC-1 vs ITC + Veh, $p \leq 0.01$, $p \leq 0.001$, ITC + Veh vs ITC + MCC950; AUC: F2, 21 = 10.70, ##$p \leq 0.01$). D Representative track plot in the OFT. E YC-1 and MCC950 increased the time spent and traveled distance traveled in central area, and but had no effect on the total traveled distance in the OFT ($$n = 8$$, time in central area: F2, 21 = 21.06, ###$p \leq 0.001$; distance in central area: F2, 21 = 13.42, ###$p \leq 0.001$; total distance: ns, no significance). F YC-1 and MCC950 decreased the latency to feed in the NSFT ($$n = 8$$, W2, 11.78 = 31.52, ###$p \leq 0.001$). G YC-1 and MCC950 decreased the immobility time in the FST ($$n = 8$$, F2, 21 = 7.689, #$p \leq 0.05$, ##$p \leq 0.01$). Data are expressed as mean ± SEM, one-way ANOVA followed by Tukey test; Welch ANOVA followed by Tamhane’s T2 test ## Inhibiting HIF-1α and NLRP3 decreased ITC-induced glial activation, neuroinflammation, and oxidative stress in the peri-thalamic lesion site We also examined the effect of inhibiting HIF-1α and NLRP3 on ITC-induced glial activation and neuroinflammatory reaction. As shown in Fig. 5A–C, the number of Iba-1 and GFAP-positive cells in the peri-thalamic lesion site in ITC rats was significantly reduced by intra-thalamic injection of YC-1 and MCC950. The morphological analysis showed that intra-thalamic injection of YC-1 and MCC950 markedly reduced the process length and endpoints in Iba-1-positive cells in the peri-thalamic lesion site of ITC rats (Fig. 5D–F). In line with this immunofluorescent result, the western blotting showed the expression level of Iba-1 and GFAP in the peri-thalamic lesion site of ITC rats were significantly lowered by intra-thalamic injection of YC-1 and MCC950 (Fig. 5G–I). These results suggest blocking HIF-1α and NLRP3 inhibited the hyperactivation of microglia and astrocytes after thalamic hemorrhagic stroke. Fig. 5Intra-thalamic injection of YC-1 or MCC950 suppressed ITC-induced activation of microglia and astrocytes in peri-thalamic lesion sites. A Representative immunofluorescence images showing the expressions of GFAP and Iba-1 in the peri-thalamic lesion sites. Scale bar = 100 μm. B Quantification of cell number showed YC-1 or MCC950 decreased Iba-1 positive cells in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, F2,12 = 56.07, ###$p \leq 0.001$ vs ITC + Veh). C Quantification of cell number showed YC-1 or MCC950 decreased GFAP-positive cells in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, F2,12 = 44.62, ###$p \leq 0.001$ vs ITC + Veh). D Representative magnified images of microglia (top) and the corresponding black-and-white, skeletonized images (bottom) in the peri-thalamic lesion sites. Scale bar = 25 μm. E Quantification of process length showed YC-1 or MCC950 increased the process length of microglia in the peri-thalamic lesion sites ($$n = 5$$, F2,12 = 44.37, ###$p \leq 0.001$ vs ITC + Veh). F Quantification of endpoint showed YC-1 or MCC950 increased the endpoints in microglia in the peri-thalamic lesion sites ($$n = 5$$, F2,12 = 22.58, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). G Representative western blots of Iba-1 and GFAP expression in total proteins of the peri-thalamic lesion sites in ITC rats. H Quantitative summary result showed YC-1 or MCC950 decreased Iba-1 expression in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, F2,12 = 16.17, ###$p \leq 0.01$ vs ITC + Veh). I Quantitative summary result showed YC-1 or MCC950 decreased GFAP expression in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, F2,12 = 37.29, ###$p \leq 0.001$ vs ITC + Veh). Data are expressed as mean ± SEM, one-way ANOVA followed by the Tukey test Next, we investigated the role of HIF-1α and NLRP3 in the inflammatory cascade and oxidative stress after thalamic hemorrhagic stroke by analyzing the expression of several pro-inflammatory cytokines and two key oxidative stress markers, MDA and SOD. Western blotting analysis showed that YC-1 and MCC950 significantly reduced the expression level of HIF-1α, NLRP3, Caspase-1 and cleaved caspase-1 the peri-thalamic lesion site of ITC rats compared to the control group (Fig. 6A–E). ELISA of HIF-1α also indicated that the expression level of HIF-1α in ITC rats was lowered by YC-1 and MCC950 (Fig. 6F). Compared to the control group, the content of pro-inflammatory cytokines TNF-α, IL-1β, IL-6, and IL-33 in the peri-thalamic lesion site of ITC rats were lowered by YC-1 and MCC950, while the expression of chemokine CCL-12 was unaffected (Fig. 6G–K). The oxidative stress indicators in the peri-thalamic lesion site of ITC rats were also measured using ELISA. As shown in Fig. 6L and M, the MDA contents were significantly reduced and SOD activities were significantly increased after intra-thalamic injection of YC-1 and MCC950, implying that blocking HIF-1α and NLRP3 suppressed the oxidative stress induced by thalamic hemorrhagic stroke. Fig. 6Intra-thalamic injection of YC-1 or MCC950 reduced ITC-induced local inflammation and oxidative stress in the peri-thalamic lesion sites in rats. A Representative western blots of HIF-1α, NLRP3, and Caspase-1, and cleaved caspase-1 expression in total proteins of the peri-thalamic lesion sites in ITC rats. B–E Quantitative summary result showed intra-thalamic injection of YC-1 or MCC950 reduced HIF-1α (B), NLRP3 (C), Caspase-1 (D), and cleaved caspase-1 (E) expression in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, HIF-1α: F2,12 = 48.89, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; NLRP3: F2,12 = 18.71, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; $$n = 4$$, Caspase-1: F2,9 = 20.56, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; cleaved caspase-1 p20: F2,9 = 19.36, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). F ELISA result showed intra-thalamic injection of YC-1 or MCC950 reduced HIF-1α in the peri-thalamic lesion sites in ITC rats ($$n = 5$$, F2,12 = 38.82, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). G–K ELISA results showed intra-thalamic injection of YC-1 or MCC950 reduced TNF-α, IL-1β, IL-6 and IL-33 expression, but had no effect on CCL-12 expression in the peri-thalamic lesion sites ($$n = 5$$, TNF-α: F2,12 = 5.292, #$p \leq 0.05$ vs ITC + Veh; IL-1β: F2,12 = 23.33, ##$p \leq 0.01$ vs ITC + Veh; IL-6: F2,12 = 9.249, #$p \leq 0.05$, ##$p \leq 0.01$ vs ITC + Veh; IL-33: F2,12 = 5.978, #$p \leq 0.05$ vs ITC + Veh). L ELISA result showed intra-thalamic injection of YC-1 or MCC950 reduced the expression of antioxidant factor MDA in the peri-thalamic lesion sites ($$n = 5$$, F2,12 = 16.69, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). M ELISA result showed intra-thalamic injection of YC-1 or MCC950 increased the expression of oxidative factor SOD in the peri-thalamic lesion sites ($$n = 5$$, F2,12 = 21.45, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). Data are expressed as mean ± SEM, one-way ANOVA followed by the Tukey test ## Post-treatment with repetitive SGB reversed the maintenance of mechanical allodynia and attenuated anxiety and depression in CPSP rats To evaluate the therapeutic effect of SGB on CPSP, SGB was conducted daily from day 9 to day 13 after thalamic hemorrhagic stroke (Fig. 7A). As shown in Fig. 7B and C, repetitive SGB significantly rescued the bilateral mechanical allodynia in CPSP rats compared to the control group, the analgesic effect persisted throughout 14 to 28 days after thalamic hemorrhagic stroke (Fig. 7B and C). During the whole observational period, repetitive SGB had no effect on the paw withdrawal mechanical threshold in ITS rats (Fig. 7B and C). We also investigated the effect of SGB on CPSP-related anxiety and depression with several behavioral paradigms (Fig. 7A). In the elevated plus maze test, CPSP rats exhibited less time and traveled distance in open arms compared with the ITS group, and these reductions were reversed by repetitive SGB, while in the ITS rats repetitive SGB had no effect (Fig. 7D and E). In accordance with the elevated plus maze test result, CPSP rats exhibited less time and traveled distance in the central area in open field test compared with the ITS group, and these reductions were rescued by repetitive SGB, while in the ITS rats repetitive SGB had no effect (Fig. 7F and G). The total traveled distance in the elevated plus maze test and open field test was unchanged by ITC or repetitive SGB (Fig. 7E and G). In addition, novelty-suppressed feeding test and forced swim test were conducted to assess the anti-depressive effect of SGB. CPSP increased the feeding latency in the novelty-suppressed feeding test and immobility time in the forced swim test compared with the ITS group, these increments were reduced by repetitive SGB, while in the ITS rats repetitive SGB had no significant effect (Fig. 7H and I). These findings imply that post-treatment with repetitive SGB could reverse the mechanical allodynia and anxiety and depression in CPSP rats. Fig. 7Post-treatment with repetitive SGB reversed the development of mechanical allodynia and anxiodepressive-like behaviors in CPSP rats. A The experimental timeline of surgical procedure and behavior tests. B Temporal changes of PWMT in contralateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the contralateral hindpaw PWMT ($$n = 8$$, PWMT: group, F3,28 = 14.78, day, F4,112 = 29.22, group × day, F12,112 = 7.669, $p \leq 0.01$ vs ITC + Veh, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITS + Veh; AUC: F3,28 = 18.74, ##$p \leq 0.01$, ###$p \leq 0.001$). C Temporal changes of PWMT in ipsilateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the ipsilateral hindpaw PWMT ($$n = 8$$, PWMT: group, F3,28 = 15.01, day, F4,122 = 26.73, group × day, F12,112 = 9.919, $p \leq 0.01$ vs ITC + Veh, ###$p \leq 0.001$ vs ITS + Veh; AUC: F2,21 = 19.21, ##$p \leq 0.01$, ###$p \leq 0.001$). D Representative track plot in the EPMT. E Repetitive SGB increased the time spent and traveled distance in the open arm in ITC group, but had no effect on ITS group ($$n = 8$$, time in open arm; F3,28 = 32.45, ###$p \leq 0.001$; distance in open arm; W3.0,13.63 = 38.47, ###$p \leq 0.001$, total distance: ns, no significance). F Representative track plot in the OFT. G Repetitive SGB increased the time spent and traveled distance in the central area in ITC group, but had no effect on ITS group ($$n = 8$$, time in central area: F3,28 = 58.17, ###$p \leq 0.001$; distance in central F3,28 = 17.36, ###$p \leq 0.001$; total distance: ns, no significance). H Repetitive SGB decreased the latency to feed in the NSFT in ITC group, but had no effect on ITS group ($$n = 8$$, F3,28 = 60.17, ###$p \leq 0.001$). I Repetitive SGB decreased the immobility time in the FST in ITC group, but had no effect on ITS group ($$n = 8$$, F3,28 = 8.058, ##$p \leq 0.01$). Data are expressed as mean ± SEM, one-way or two-way ANOVA followed by Tukey test, Welch ANOVA followed by Tamhane’s T2 test ## Repetitive SGB improved cerebral blood flow and suppressed the hyperactivation of microglia and astrocytes in CPSP rats Changes in regional cerebral blood flow (rCBF) and insufficient blood and oxygen delivery to the brain may result from a stroke. Thus, on day 14 after thalamic hemorrhagic stroke, we applied LSCI, a well-established technique for monitoring rCBF with high spatial and temporal resolution and real-time imaging, to investigate the effect of SGB on cerebral blood flow in CPSP rats. As shown in Fig. 8A and B, ITC significantly reduced bilateral cortical blood flow compared with ITS group, this reduction was rescued by repetitive SGB in which the cerebral vascular continuity is improved. Moreover, repetitive SGB also remarkably enhanced the cortical blood flow in ITS rats, as seen by the increased visibility of tiny blood vessels (Fig. 8A and B), which is consistent with the sympathetic blocking action of SGB, which may enhance cerebral blood flow under normal condition. Fig. 8Repetitive SGB increased the cerebral blood flow of CPSP rats. A Representative images of cerebral blood flow. B Quantitative summary result showed ITC decreased the cerebral blood flow, while repetitive SGB enhanced cerebral blood flow in ITC rats ($$n = 5$$, F3, 16 = 112.2, #$p \leq 0.05$, ##$p \leq 0.01$, ###$p \leq 0.001$) We next examine the effect of SGB on glial activation in CPSP rats. As shown in Fig. 9A–C, the number of Iba-1 and GFAP-positive cells in the peri-thalamic lesion site of CPSP rats was increased compared to the ITS group, and this increment was significantly inhibited by repetitive SGB compared to the ITC + Veh group. The morphological analysis of Iba-1-positive cells showed the reduction of process length and endpoints in the CPSP group were significantly rescued by repetitive SGB (Fig. 9D–F). In line with our immunofluorescent results, western blotting showed the enhanced expression level of Iba-1 and GFAP in the ITC group were significantly inhibited by SGB (Fig. 9G–I). These results suggest repetitive SGB was able to suppress the hyperactivation of microglia and astrocytes in the peri-thalamic lesion site of CPSP rats. Fig. 9Post-treatment with repetitive SGB inhibited the activation of microglia and astrocytes in peri-thalamic lesion sites of CPSP rats. A Representative immunofluorescence images showing the expressions of GFAP and Iba-1 in the peri-thalamic lesion sites. Scale bar = 100 μm. B, C Quantification of cell number showed repetitive SGB decreased the total number of GFAP (B) and Iba-1 (C) positive cells in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, Iba-1 F2,12 = 40.67, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; GFAP F2,12 = 99.46, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). D Representative magnified images of microglia (top) and the corresponding black-and-white, skeletonized images (bottom) in the peri-thalamic lesion sites. Scale bar = 25 μm. E Quantification of process length showed repetitive SGB increased process length of microglia in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, F2,12 = 59.03, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). F Quantification of endpoint showed repetitive SGB increased endpoint of microglia in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, F2,12 = 15.62, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). g Representative western blots of Iba-1 and GFAP expression in total proteins of the peri-thalamic lesion sites, H, I Quantitative summary result showed repetitive SGB decreased Iba-1 (H) and GFAP (I) expression in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, Iba-1: F2,12 = 28.08, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; GFAP; F2,12 = 62.45, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). Data are expressed as mean ± SEM, one-way ANOVA followed by the Tukey test ## SGB inhibited HIF-1α/NLRP3 signaling as well as its downstream inflammatory response and oxidative stress in CPSP rats As mentioned above, we found that HIF-1α/NLRP3 signaling was critical for the development of CPSP and its related anxiety and depression. Thus, we examined the influence of SGB on HIF-1α/NLRP3 signaling and the downstream inflammatory cascade using western blotting and ELISA. Consistently, the enhanced expression of HIF-1α, NLRP3, Caspase-1, and cleaved caspase-1 in the peri-thalamic lesion sites in CPSP rats was dramatically inhibited by SGB (Fig. 10A–E). ELISA results also showed ITC enhanced HIF-1α expression compared to the ITS group, while SGB rescued this upregulation (Fig. 10F). In contrast to the ITS group, ITC considerably enhanced the expression level of TNF-α, IL-1β, IL-6, IL-33, and CCL-12, whereas SGB significantly reduced the elevation of TNF-α, IL-1β, and IL-6, but had no influence on the expression of IL-33 and CCL-12 (Fig. 10G–K). Additionally, the increased MDA and reduced SOD in the peri-thalamic lesion site of CPSP rats compared to the ITS group were rescued by repetitive SGB (Fig. 10L and M). These findings indicate that SGB exhibited anti-inflammatory and anti-oxidative effects by inhibiting HIF-1α/NLRP3 signaling in the CPSP rats. Fig. 10Post-treatment with repetitive SGB reversed local inflammation and oxidative stress in the peri-thalamic lesion sites of CPSP rats. A Representative western blotting of HIF-1α, NLRP3, Caspase-1, and cleaved caspase-1 expression in the peri-thalamic lesion sites. B–E Quantitative summary result showed repetitive SGB decreased HIF-1α (B), NLRP3 (C), Caspase-1 (D), and cleaved caspase-1 (E) expression in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, HIF-1α: F2,12 = 9.896, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; NLRP3: F2,12 = 10.40, #$p \leq 0.05$, ##$p \leq 0.01$ vs ITC + Veh; $$n = 4$$, Caspase-1: F2,9 = 38.72, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; cleaved caspase-1 p20: F2,9 = 28.21, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). F ELISA result showed repetitive SGB decreased HIF-1α expression in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, F2,12 = 42.10, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh). G–K ELISA results showed repetitive SGB decreased TNF-α, IL-1β and IL-6 in the peri-thalamic lesion sites of CPSP rats, but had no effect on IL-33 and CCL-12 ($$n = 5$$, TNF-α: F2,12 = 72.48, ###$p \leq 0.001$ vs ITC + Veh; IL-1β: F2,12 = 26.98, ##$p \leq 0.01$, ###$p \leq 0.001$ vs ITC + Veh; IL-6: F2,12 = 20.58, #$p \leq 0.05$, #$p \leq 0.05$, ###$p \leq 0.001$ vs ITC + Veh; IL-33: F2,12 = 12.82, ###$p \leq 0.001$ vs ITC + Veh; CCL-12: F2,12 = 5.570, #$p \leq 0.05$ vs ITC + Veh). L ELISA result showed repetitive SGB decreased the expression of antioxidant factor MDA in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, F2,12 = 15.28, #$p \leq 0.05$, ##$p \leq 0.01$ vs ITC + Veh). M ELISA result showed repetitive SGB increased the expression level of oxidative factor SOD in the peri-thalamic lesion sites of CPSP rats ($$n = 5$$, F2,12 = 17.52, #$p \leq 0.05$, ###$p \leq 0.001$ vs ITC + Veh). Data are expressed as mean ± SEM, one-way ANOVA followed by the Tukey test ## Pharmacological activation of HIF-1α/NLRP3 signaling eliminated the protective effect of SGB on mechanical allodynia and anxiety and depression in CPSP rats To further confirm that thalamic HIF-1α/NLRP3 signaling mediated the protective effects of SGB on CPSP and comorbid anxiety and depression following thalamic hemorrhage, we pharmacologically activated thalamic HIF-1α and NLRP3 by intra-thalamic injection of nigericin and DMOG at 9, 11, and 13 days in rats given repetitive SGB after intra-thalamic collagenase injection (Fig. 11A). As shown in Fig. 11B and C, compared to the control group, pharmacological activation of thalamic HIF-1α or NLRP3 signaling with nigericin or DMOG in ITC rats significantly eliminated the analgesic effect of repetitive SGB on bilateral mechanical allodynia at 14, 21, and 28 days (Fig. 11B and C). In the open field test, pharmacological activation of thalamic HIF-1α or NLRP3 signaling with nigericin or DMOG significantly reduced the increased time and traveled distance in the central area in ITC rats given repetitive SGB, but had no effect on the total distance (Fig. 11D and E). Additionally, intra-thalamic injection of nigericin or DMOG remarkably increased the feeding latency in novelty-suppressed feeding test (Fig. 11F) and the immobility in forced swim test (Fig. 11G) in ITC rats given repetitive SGB.Fig. 11Intra-thalamic injection of DMOG or Nigericin eliminated the therapeutic effects of SGB on mechanical allodynia and anxiety and depression in CPSP rats. A The experimental timeline of surgical procedure and behavior tests. B Intra-thalamic injections of DMOG or Nigericin decreased the PWMT of the contralateral hindpaw and the area under the curve of the PWMT in ITC rats given SGB ($$n = 5$$, PWMT: group, F3,16 = 9.517, day, F4,64 = 59.90, group × day, F12,64 = 3.019, #$p \leq 0.05$, ##$p \leq 0.01$, ITC + SGB + DSMO vs ITC + SGB + DMOG, $p \leq 0.05$, $p \leq 0.001$, ITC + SGB + DMSO vs ITC + SGB + Nig; AUC: F3,16 = 14.57, ##$p \leq 0.01$). C Intra-thalamic injections of DMOG or Nigericin decreased the PWMT of the ipsilateral hindpaw and the area under the curve of the PWMT in ITC rats given SGB ($$n = 5$$, PWMT: group, F3,16 = 17.76, day, F4,64 = 60.99, group × day, F12,64 = 2.905, #$p \leq 0.05$, ###$p \leq 0.001$, ITC + SGB + DMSO vs ITC + SGB + DMOG, $p \leq 0.05$, $p \leq 0.01$,*$p \leq 0.001$, ITC + SGB + DMSO vs ITC + SGB + Nig; AUC: F3,16 = 29.48 ###$p \leq 0.001$). D Representative track plot in the OFT. E DMOG and Nigericin decreased the time spent and traveled distance traveled in central area, and but had no effect on the total traveled distance in ITC rats given SGB ($$n = 5$$, time in central area: F3,16 = 12.60, ##$p \leq 0.01$; distance in central area: F3,16 = 8.491, ##$p \leq 0.01$; total distance: ns, no significance). F DMOG and Nigericin increased the latency to feed in the NSFT in ITC rats given SGB ($$n = 5$$, F3,16 = 27.42, ###$p \leq 0.001$). G DMOG and Nigericin increased the immobility time in the FST in ITC rats given SGB ($$n = 5$$, F3,16 = 8.085, ##$p \leq 0.01$). Data are expressed as mean ± SEM, one-way ANOVA followed by Tukey test ## Pre-treatment with repetitive SGB prevented the development of mechanical allodynia and anxiety and depression in CPSP rats Next, we conducted SGB daily from the day of surgery until the fourth postsurgical day to investigate the preventive effect of SGB on CPSP and the related anxiety and depression (Fig. 12A). Compare to the control group, pre-treatment with repetitive SGB significantly prevented the development of bilateral mechanical allodynia in ITC rats, manifested as the increased paw withdrawal mechanical threshold on 7, 14, 21, and 28 days after surgery (Fig. 12B and C). However, pre-treatment with repetitive SGB had no influence on the bilateral paw withdrawal mechanical threshold in ITS group during the whole observation period (Fig. 12B and C). In the elevated plus maze test, repetitive SGB prevented the reduced time and distance traveled in open arms in the ITC group, while SGB had no influence on the ITS group (Fig. 12D and E). Compared to the ITS group, the time and traveled distance in the central area in open field test were decreased in the ITC group, and these reductions were significantly prevented by repetitive SGB, but repetitive SGB had no effect on the ITS group (Fig. 12F and G). The total traveled distance in the elevated plus maze test and open field test was unaffected by ITC or pre-treatment with repetitive SGB (Fig. 12E and G). Consistently, ITC increased the feeding latency of novelty-suppressed feeding test and the immobility time of forced swim test compared with the ITS group, but repetitive SGB prevented these increases (Fig. 12H and I). However, repetitive SGB had no effects on the ITS group (Fig. 12H and I). These findings suggest that pre-treatment with repetitive SGB was sufficient to prevent the development of CPSP and related anxiety and depression. Fig. 12Pretreatment with repetitive SGB prevents the development of mechanical allodynia and anxiodepressive-like behaviors in ITC rats. A The experimental timeline of surgical procedure and behavior tests. B Temporal changes of PWMT in contralateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the contralateral hindpaw PWMT ($$n = 8$$, PWMT: group, F3,28 = 9.268, day, F4,112 = 10.48, group × day, F12,112 = 6.246, $p \leq 0.05$, *$p \leq 0.01$ vs ITC + Veh, ###$p \leq 0.001$ vs ITS + Veh; AUC: F3,28 = 18.74, #$p \leq 0.05$, ##$p \leq 0.01$). C Temporal changes of PWMT in ipsilateral hindpaw after thalamic hemorrhagic stroke and the area under curve of the ipsilateral hindpaw PWMT ($$n = 8$$, PWMT: group, F3,28 = 10.48, day, F4,122 = 17.65, group × day, F12,112 = 7.005, $p \leq 0.05$, $p \leq 0.01$ vs ITC + Veh, ###$p \leq 0.001$ vs ITS + Veh; AUC: F2,21 = 19.21, ##$p \leq 0.01$). D Representative track plot in the EPMT. E Pretreatment with repetitive SGB increased the time spent and traveled distance in the open arm in ITC group, but had no effect on ITS group ($$n = 8$$, time in open arm: F3,28 = 35.13, ###$p \leq 0.001$; distance in open arm: F3,28 = 51.39, ###$p \leq 0.001$; total distance: ns, no significance). F Representative track plot in the OFT. G Pretreatment with repetitive SGB increased the time spent and traveled distance in central area in ITC group, but had no effect on ITS group ($$n = 8$$, time in central area: F3,28 = 11.38, ##$p \leq 0.01$, ###$p \leq 0.001$; distance in central area: F3,28 = 14.80, ###$p \leq 0.001$; total distance: ns, no significance). H Pretreatment with repetitive SGB decreased the latency to feed in the NSFT in ITC group, but had no effect on ITS group ($$n = 8$$, W3.0,15.05 = 23.77, #$p \leq 0.05$, ###$p \leq 0.001$). I Pretreatment with repetitive SGB decreased the immobility time in the FST in ITC group, but had no effect on ITS group ($$n = 8$$, F2,21 = 8.253, ##$p \leq 0.01$). Data are expressed as mean ± SEM, one-way or two-way ANOVA followed by Tukey test, Welch ANOVA followed by Tamhane’s T2 test ## Discussion We found that thalamic hemorrhagic stroke causes mechanical allodynia, anxiety- and depression-like behaviors, and peri-thalamic lesion site HIF-1α/NLRP3 signaling upregulation and glial cell activation. Inhibiting thalamic HIF-1α/NLRP3 signaling prevented thalamic hemorrhage stroke-induced CPSP, anxiety, depression, and peri-thalamic lesion site neuroinflammation. Repetitive SGB post-treatment alleviated CPSP with comorbid anxiety and depression. In CPSP rats, SGB reduced thalamic HIF-1α/NLRP3 signaling upregulation, microglial and astrocytic hyperactivation, inflammatory cytokines, and oxidative stress. Furthermore, repetitive SGB improved cerebral blood flow in CPSP rats. However, pharmacological activation of thalamic HIF-1α/NLRP3 signaling eliminated the therapeutic effect of SGB on CPSP with comorbid anxiety and depression. Finally, repeated SGB also prevented thalamic hemorrhage stroke-induced CPSP with comorbid anxiety and depression. Our study showed that HIF-1α/NLRP3 inflammatory signaling contributed to the CPSP and comorbid anxiety and depression. SGB inhibited HIF-1α/NLRP3 inflammatory signaling and could treat CPSP and comorbid anxiety and depression. ## HIF-1α/NLRP3 inflammatory signaling mediated the development of CPSP and comorbid anxiety and depression Consistent with our previous studies [14, 34, 35], we confirmed that microinjection of collagenase into unilateral VPL regions of thalamus resulted in long-lasting mechanical pain hypersensitivity, which closely resembles CPSP, a central neuropathic pain caused by thalamic hemorrhagic stroke in patients. It is noteworthy that psychiatric disorders, such as depression and anxiety, are frequently comorbid with neuropathic pain, and depression/anxiety may worsen pain and vice versa [42–45]. In contrast to studies on core pain symptoms, few studies have investigated CPSP’s comorbid emotion in the recent decade. Using a variety of behavior paradigms, we identified anxiety- and depression-like behaviors in CPSP rats, consistent with findings from two very recent studies [15, 46]. Accompanied by the persistent mechanical allodynia, the microglia and astrocytes in the peri-thalamic lesion sites were extremely activated, and HIF-1α and NLRP3 were highly upregulated. Our intra-thalamic siRNA injection assay revealed the functional nature of upregulated HIF-1 and NLRP3, demonstrating that genetic knockdown of thalamic HIF-1 and NLRP3 could prevent mechanical allodynia after thalamic stroke. The pharmacological inhibition of thalamic HIF-1α and NLRP3 with selective inhibitor also confirmed that thalamic HIF-1α/NLRP3 signaling plays a critical role in the development of CPSP. We further found that pharmacological inhibition of thalamic HIF-1α and NLRP3 prevented peri-thalamic lesion site glial activation and inflammatory cytokine upregulation, demonstrating that HIF-1α/NLRP3 signaling causes the inflammatory response after thalamic hemorrhagic stroke. Actually, the HIF-1α/NLRP3 pathway has been recognized as a potential molecular target for treating ischemic stroke and traumatic brain injury because it regulates microglia activation and inflammatory cytokines release in the inflammatory cascade after brain injury [17, 47, 48]. Oxidative stress, a change in the pro-oxidant/antioxidant balance that promotes oxidation, also leads to stroke-related brain injury [49, 50]. Our results showed that inhibiting HIF-1α/NLRP3 signaling resolved the MDA/SOD imbalance after thalamic hemorrhagic stroke, which was corroborated by previous studies showing that HIF-1α and NLRP3 inflammasome activation increases brain oxidative stress after cerebral ischemia in rats [51, 52]. We have demonstrated HIF-1α-primed neuroinflammation promoted CPSP in our earlier work [14]. Consistent with our results, a recent study showed that NLRP3/ASC/caspase-1 dramatically increased following collagenase-induced thalamic pain, while NLRP3 siRNA intra-thalamic microinjection could reduce pain [16]. The most striking finding of this study is that inhibiting thalamic HIF-1α/NLRP3 inflammatory signaling was effective to prevent the anxiety and depression related to CPSP. Current studies of the role thalamus in mood disorders have mostly focused on structural and functional connections, whereas research on its cellular-molecular mechanisms is rare [53–56]. The thalamus, particularly the medial thalamus, is anatomically connected to the anterior cingulate cortex and plays an important role in the expression and experience of emotion, as is well documented [57–60]. In thalamic hemorrhage-induced CPSP, emerging studies have demonstrated that dysregulated local neuroinflammation caused the aberrant excitability and synaptic plasticity in the thalamus and anterior cingulate cortex [9, 11, 13, 14, 61]. Thus, we determined the causal relationship between local neuroinflammation and CPSP-related anxiety and depression by inhibiting thalamic HIF-1α/NLRP3 inflammatory signaling. Our data demonstrated that thalamic HIF-1α/NLRP3 inflammatory signaling was responsible for the comorbid anxiety and depression in CPSP rats. A very recent study confirms our findings that decreasing HIF-1α/NLRP3 in the brain ameliorates lipopolysaccharide-induced depressive-like behavior [20]. The neurocircuit mechanisms through which thalamic HIF-1α/NLRP3 inflammatory signaling causes CPSP-related anxiety and depression are unclear. We hypothesized that neuroinflammatory events could alter neural and synaptic plasticity in the thalamus, which caused functional changes in the output brain regions, such as anterior cingulate cortex and prefrontal cortex, and eventually led to anxiety and depression, since transcranial direct current stimulation of the dorsolateral prefrontal cortex was also shown to improve CPSP and negative mood [62]. Further revealing thalamus-organized neural circuits and molecular mechanisms may be the focus of future research on CPSP with comorbid anxiety and depression, which might pave the way for the precise treatment of CPSP. ## SGB improved CPSP and comorbid anxiety and depression by inhibiting HIF-1α/NLRP3 signaling SGB, a well-known sympathetic modulation approach, could improve cerebral blood flow and hypoxia and is an effective treatment for several cerebrovascular diseases and pathological pain [26, 27, 63, 64]. Using laser speckle contrast imaging, we have for the first time visualized that SGB improves cerebral blood flow following experimental thalamic hemorrhagic stroke, which were corroborated by other study showing that SGB could alleviate cerebral vasospasm and induce dilation of intracerebral blood vessels in an experimental rat model of subarachnoid hemorrhage [21]. Given the theoretical possibility that it could increase the area of hemorrhage, SGB seemed unsuitable for immediate use in cerebral hemorrhage. However, a previous study has reported that patients with fresh cerebral hemorrhages, immediately upon entrance to the hospital, received SGB without any apparent harm resulting from the block, suggesting that SGB might be an optional and safe therapy for cerebral hemorrhage [65, 66]. Due to the limitations of LSCI technology, we could only monitor changes in blood flow in the superficial cerebral areas. However, a previous study has shown that unilateral SGB was also able to boost oxygenation in deep cerebral regions on the blocked side [67]. Our findings showed that SGB dramatically reduced thalamic HIF-1α overexpression, also indicated that SGB could improve thalamus oxygen supply. Repetitive unilateral SGB restored the increased NLRP3 inflammasome, activated microglia and astrocytes, upregulated pro-inflammatory cytokines, and disrupted MDA and SOD in the peri-thalamic lesion sites, proving that SGB had a significant anti-inflammatory and anti-oxidative role in thalamic stroke. Similar to our findings, Li et al. showed that SGB inhibited Toll-like receptor 4/nuclear factor kappa B signaling pathway and reduced inflammatory response during the ischemic stroke [68]. In addition to suppressing HIF-1α/NLRP3 inflammatory signaling, SGB significantly attenuated thalamic hemorrhage-induced CPSP and comorbid anxiety and depression. However, pharmacological activation of thalamic HIF-1α and NLRP3 significantly eliminated the therapeutic effects of SGB on mechanical allodynia and anxiety- and depression-like behaviors following thalamic hemorrhage, which confirmed that SGB improved CPSP and comorbid anxiety and depression through inhibiting HIF-1α/NLRP3 inflammatory signaling. SGB may have other mechanisms to reduce anxiety and depression. SGB reduced depression-like behaviors in an unexpected chronic moderate stress model owing to an anti-apoptotic mechanism of two stress pathways, the autonomic system and the HPA axis [69]. Anyway, this study broadens our understanding of SGB's action mechanism and informs treatment decisions for CPSP based on increasing the oxygen supply and reducing neuroinflammation. Given the difficulty in translating medications targeting specific molecules in the neuroinflammatory cascade to the clinic, SGB targeting and affecting whole physiological networks is a promising approach for the treatment of CPSP. Actually, previous case studies have shown that SGB offers an effective intervention for CPSP, as the pain subsided rapidly in both intensity and frequency after SGB, and the quality of life was markedly improved [28, 29]. Likewise, SGB was beneficial in the treatment of anxiety symptoms from post-traumatic stress disorder [70–72]. Even though, more clinical studies with larger sample sizes and alternate protocols are needed to further explore the therapeutic potential of SGB for CPSP and related psychiatric disorders. ## Conclusions In conclusion, our findings demonstrated that a thalamic hemorrhagic stroke resulted in CPSP and comorbid anxiety- and depression-like behaviors. Upregulated HIF-1α/NLRP3 signaling in the peri-thalamic sites activated microglia and astrocytes, releasing pro-inflammatory cytokines and oxidative stress, leading to CPSP and comorbid anxiety and depression. SGB increases cerebral blood flow to suppress HIF-1α/NLRP3 inflammatory signaling, improving the CPSP and comorbid anxiety and depression. Thus, SGB could be used as a promising therapeutic strategy for CPSP and comorbid anxiety and depression symptoms. ## Supplementary Information Additional file 1: Figure S1. Uncropped blots images of Figs. 2, 3, 5, 6, 9 and 10 ## References 1. Delpont B, Blanc C, Osseby GV, Hervieu-Begue M, Giroud M, Bejot Y. Pain after stroke: a review*. Rev Neurol (Paris)* (2018.0) 174 671-674. DOI: 10.1016/j.neurol.2017.11.011 2. Oh H, Seo W. A comprehensive review of central post-stroke pain. Pain Manag Nurs (2015.0) 16 804-818. DOI: 10.1016/j.pmn.2015.03.002 3. Choi HR, Aktas A, Bottros MM. Pharmacotherapy to manage central post-stroke pain. 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--- title: Validation of the questionnaire for medical checkup of old-old (QMCOO) score cutoff to diagnose frailty authors: - Mitsutaka Yakabe - Koji Shibasaki - Tatsuya Hosoi - Shoya Matsumoto - Kazuhiro Hoshi - Masahiro Akishita - Sumito Ogawa journal: BMC Geriatrics year: 2023 pmcid: PMC10031947 doi: 10.1186/s12877-023-03885-3 license: CC BY 4.0 --- # Validation of the questionnaire for medical checkup of old-old (QMCOO) score cutoff to diagnose frailty ## Abstract ### Background Frailty is a state of increased vulnerability to poor resolution of homeostasis following a stress. Early diagnosis and intervention of frailty are essential to prevent its adverse outcomes. However, simple diagnostic criteria have not been established. The Questionnaire for Medical Checkup of Old-Old (QMCOO) is widely used for medical checkups of older adults in Japan. In our previous report, we developed a method to score the QMCOO and showed that frailty can be diagnosed with the highest accuracy when the score cutoff was set at $\frac{3}{4}$ points. We aimed to validate the criteria in a larger cohort. ### Methods Participants aged 65 years or over were recruited in the western region of Japan. They answered all the items of the Kihon Checklist (KCL) and the QMCOO. Based on the KCL score, they were diagnosed as robust (3 or lower), prefrail (4 to 7), or frail (8 or over). Then we tested the effectiveness to diagnose frailty using the QMCOO cutoff of $\frac{3}{4}$ points. We also aimed to determine the score cutoff to separate robust and prefrail. ### Results 7,605 participants (3,458 males and 4,147 females, age 77.4 ± 6.9 years) were recruited. 3,665 participants were diagnosed as robust, 2,448 were prefrail, and 1,492 were frail based on the KCL score. The diagnosis of frailty had a sensitivity of $84.0\%$, specificity of $82.5\%$, and accuracy of $82.8\%$ with a QMCOO score cutoff of $\frac{3}{4}$ points, suggesting its validity. To separate robust and prefrail, both the accuracy and the Youden index were the highest with the QMCOO cutoff of $\frac{2}{3}$ points (sensitivity, specificity, and accuracy were $63.9\%$, $83.4\%$, and $75.6\%$, respectively). All the questions of the QMCOO except Q12 (about smoking) were significantly related to prefrailty status after a logistic regression analysis. ### Conclusion Diagnosis of frailty using the QMCOO score cutoff of $\frac{3}{4}$ points was validated. Prefrailty could be diagnosed using the score cutoff of $\frac{2}{3}$ points. ## Backgrounds Frailty is a state of increased vulnerability to poor resolution of homeostasis following a stress. Frailty is commonly observed in older adults, supposed to be a disorder of multiple interrelated physiological systems due to an accelerated decline in physiological reserve with aging [1]. Frailty increases adverse outcomes including falls, disability, hospitalization, and mortality [1]. As the world population ages, frailty is an urgent issue. Exercise-based interventions could delay or improve frailty [2–6], and multicomponent exercise could be especially effective [7, 8]. Early diagnosis is essential to intervene in frail patients and reduce adverse events. The Cardiovascular Health Study (CHS) criteria by Fried et al. define frailty as having three or more of the following phenotypes: unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity [9]. In a Japanese version of the CHS (J-CHS) criteria, the phenotypes are (i) Shrinking: “Have you unintentionally lost 2 or more kg in the past 6 months?” ( yes = 1); (ii) Weakness: grip strength < 28 kg in men or < 18 kg in women (yes = 1); (iii) “In the past 2 weeks, have you felt tired without a reason?” ( yes = 1); (iv) Gait speed < 1.0 m/s (yes = 1); and (v) “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?” ( no to both questions = 1): frailty, prefrailty and robust were defined as having 3–5, 1–2, and 0 points, respectively [10]. The criteria are supposed to be the standard but require a grip strength tester and a 4–6 m course to measure grip strength and walking speed, consuming time to diagnose. Diagnosis of frailty using questionnaires has been attempted. One is the Kihon Checklist (KCL), a self-reported questionnaire consisting of 25 items to screen the health and life status of older adults. The English version has been established, and all the items are described elsewhere [11]. When a KCL score of 4 to 7 points is diagnosed as prefrail and a KCL score of 8 or higher is diagnosed as frail, the best sensitivity and specificity are achieved, and the usefulness of KCL has been validated based on the frailty status diagnosed by the J-CHS criteria [12]. However, it takes time to complete the 25 items. The Questionnaire for Medical Checkup of Old-Old (QMCOO) was established by the Ministry of Health, Labour and Welfare in Japan and has been officially used in the medical checkup of older adults in Japan. The QMCOO is self-reported by older adults. The QMCOO is aimed to assess the general health status of older adults, having 15 questions about 10 domains: health condition, mental health, eating behavior, oral function, body weight loss, physical function and falls, cognitive function, smoking, social participation, and social support. All the items of the QMCOO are described elsewhere [13]. It has been decided that the QMCOO will be used as a platform for frailty checkups for older adults in Japan. However, the QMCOO is not intended to diagnose frailty and no diagnostic criteria using the QMCOO have been established. The QMCOO has seven questions in common with the KCL (Q4: Do you have any difficulties eating tough foods compared to 6 months ago?; Q5: Have you choked on your tea or soup recently?; Q6: Have you lost 2 kg or more in the past 6 months?; Q8: Have you experienced a fall in the past year?; Q10: Do your family or your friends point out your memory loss? e.g. “You ask the same question over and over again.”; Q11: Do you find yourself not knowing today’s date?; Q13: Do you go out at least once a week?). The QMCOO has several other questions that are not identical but similar to those in the KCL. The QMCOO has fewer items than the KCL, taking less time and burden to complete for older adults. Since the usefulness of the KCL in diagnosing frailty has been validated, the QMCOO could be used to assess frailty, but the evidence is currently insufficient. In a previous cross-sectional study, we diagnosed frailty in community-dwelling older adults using the QMCOO. The cutoff value of $\frac{3}{4}$ points was determined to maximize the Youden index; sensitivity, specificity, and accuracy were $76.3\%$, $88.1\%$, and $86.1\%$, respectively [14]. However, the number of participants in the study was 223, which is relatively small. To diagnose frailty at the same time as medical checkups using QMCOO would be useful for early intervention, and the cutoff should be validated in another larger cohort for its widespread use. In the present study, therefore, we regarded the participants as the derivation cohort and aimed to validate the cutoff in a newly established validation cohort, establish the QMCOO as a screening tool, and increase options for diagnosing frailty. We also diagnosed robust and prefrail based on the KCL score and attempted to determine the cutoff for diagnosing prefrail using the QMCOO. ## Study design and the participants This is a cross-sectional study of community-dwelling older adults. Participants were recruited in the western region of Japan: Yonago City (Tottori Prefecture), Kurayoshi City (Tottori Prefecture), Masuda City (Shimane Prefecture), and Taka Town (Hyogo Prefecture). Candidate participants were those aged 65 or over who had not been certified as requiring support or care by the long-term care insurance. We mailed the candidates a paper survey that included all of the QMCOO and KCL items, and participants answered all of them and returned them. Those who had participated in our previous study [14] were excluded. ## The QMCOO and scoring The scoring of the QMCOO was conducted as in the previous study [14]. Each question was scored as 0 or 1, and the total was the score (0–15). ## The KCL-based frailty evaluation Each question of the KCL was scored as 0 or 1, and the total was used as the score (0–25). Based on the previous study [12], a score of 8 or higher was diagnosed as frail, a score of 4 to 7 as prefrail, and a score of 3 or lower as robust. ## Validation of the QMCOO cutoff of 3/4 points The group of 223 participants analyzed in our previous report [13] was regarded as the derivation cohort. The group of those who agreed to participate in the present study was set as the validation cohort. The QMCOO cutoff of $\frac{3}{4}$ points in our previous report was adopted to the validation cohort, then sensitivity, specificity, and accuracy were calculated. They were also calculated for the “75 years old or over,“ “74 years old or under,“ “males,“ and “females” groups. The validation cohort consisted of 7,605 people that agreed to participate and were recruited for the present study. The cohort consisted of 3,458 males and 4,147 females, and the sex ratio did not significantly differ from the derivation cohort of 103 males and 120 females ($$p \leq 0.900$$). The average age was 76.3 ± 6.9 years old in the validation cohort and significantly different from 77.4 ± 6.9 years old in the derivation cohort ($$p \leq 0.018$$). The average KCL score was 4.6 ± 3.9 in the validation cohort, and 4.2 ± 3.6 in the derivation cohort ($$p \leq 0.08$$). The average QMCOO score was 2.7 ± 2.1 in the validation cohort and 2.4 ± 2.1 in the derivation cohort ($$p \leq 0.012$$). The characteristics of the validation cohort are shown in Table 1. Based on the KCL score, 3,665 participants were diagnosed as robust, 2,448 were prefrail, and 1,492 were frail. The average age was higher in the order of the frail group, prefrail group, and robust group, with significant differences. The average QMCOO score was also higher in the order of the frail group, prefrail group, and robust group, with significant differences. 651 of 3,458 males ($18.8\%$) and 841 of 4,147 females ($20.3\%$) were frail, with no significant difference in the ratio ($$p \leq 0.112$$). Height and body weight were significantly different between the robust, prefrail, and frail groups, but BMI was not. Table 1The characteristics of the validation cohortRobust($$n = 3$$,665)Prefrail($$n = 2$$,448)Frail($$n = 1$$,492)p-valueSex(M/F)1,$\frac{725}{1}$,9401,$\frac{082}{1}$,$\frac{366651}{8410.025}$Age74.5+-6.177.0+-6.979.6+-7.2< 0.001QMCOO score1.4+-1.23.1+-1.55.5+-2.1< 0.001Height (cm)158.8+-8.7157.1+-9.0156.1+-9.5< 0.001Body weight (kg)57.1+-10.056.0+-10.354.7+-11.3< 0.001BMI22.6+-2.922.6+-3.122.4+-3.60.060 The correlation coefficient between the KCL score and QMCOO score in the validation cohort was 0.800, which was significant ($p \leq 0.001$) (Fig. 1). In the derivation cohort, the diagnosis of frailty had a sensitivity of $76.3\%$, specificity of $88.1\%$, and accuracy of $86.1\%$ with a cutoff of $\frac{3}{4}$ points [13]. In all the participants in the validation cohort, sensitivity was $84.0\%$, specificity was $82.5\%$, and accuracy was $82.8\%$. Sensitivity, specificity, and accuracy were also good for the “75 years old or over,“ “74 years old or under,“ “males,“ and “females” groups (Table 2). Fig. 1The correlation between the KCL score and the QMCOO score Table 2Validation of diagnosing frailty by the $\frac{3}{4}$ cutoff score of QMCOO.SensitivitySpecificityAccuracyTotal$84.0\%$$82.5\%$$82.8\%$75 years old or over$84.1\%$$81.9\%$$82.5\%$74 years old or under$84.0\%$$83.1\%$$83.2\%$Males$86.5\%$$78.8\%$$80.2\%$Females$82.2\%$$85.7\%$$85.0\%$ ## The relationship between body weight and the frailty status We divided the participants into three groups based on the body mass index (BMI): “lean” (BMI < 18.5 kg/m2), “standard” (18.5 ≤ BMI < 25.0 kg/m2), and “obese” (BMI ≥ 25.0 kg/m2). Then we examined the relationship between body weight and the frailty status diagnosed by the QMCOO score. The ratio of frailty was also compared in the male and female groups. Furthermore, the participants were divided into three age groups (74 or under, 75–84, and 85 or over), then the ratio of frailty was compared in the age groups. A logistic regression analysis was performed to examine the relationship between BMI and the frailty status. In total participants, the ratio of frailty was significantly higher in the lean group and the obese group than in the standard group (Fig. 2A). When analyzed by sex, the ratio of frailty was lower in women than in men in all the groups: lean, standard, and obese (Fig. 2B). The rate of frailty was higher with age in all groups (Fig. 2C). To examine the effects of each factor on frailty status, we performed a logistic regression analysis. Age was a continuous variable, sex was a qualitative variable, and “lean” and “obese” in BMI were converted to dummy variables. After logistic regression analysis, age, sex, and BMI (both “lean” and “obese”) still significantly affected the frailty status (Fig. 2D). The variance inflation factors (VIFs) of all the explanatory variables were below 2.0, suggesting that they did not have statistical collinearity. Fig. 2 The relationship between BMI and frailty status Frailty was diagnosed by the QMCOO cutoff of 4 scores or over. The participants into three groups based on the body mass index (BMI): “lean” (BMI < 18.5 kg/m2), “standard” (18.5 ≤ BMI < 25.0 kg/m2), and “obese” (BMI ≥ 25.0 kg/m2). ( A) The ratio of the frailty of lean, standard, and obese groups in total participants. ( B) The ratio when the participants were divided into males and females. ( C) The ratio when the participants were divided into three groups of “74 or under”, ”75–84”, and ”85 or over” (D) Logistic regression analysis to evaluate factors on frailty ## Setting a new cutoff for diagnosing prefrail Robust (the KCL score is three or less) and prefrail (the KCL score is 4–7) participants were extracted from the validation cohort. The cutoff score of the QMCOO for diagnosing prefrail was determined using a receiver operating characteristic (ROC) curve. The point that maximized the Youden index was adopted as the cutoff. Subgroups of age and sex were also tested for QMCOO cutoff values. A logistic regression analysis was performed to examine which of the QMCOO items determined the prefrailty status. Robust (the KCL score is three or less, $$n = 3$$,665) and prefrail (the KCL score is 4–7, $$n = 2$$,448) participants were extracted from the validation cohort ($$n = 6$$,113 in total). The KCL score and the QMCOO score showed a significant positive correlation (Fig. 3A). The area under the curve was 0.818 (Fig. 3B). When the QMCOO cutoff was set $\frac{2}{3}$ points, both the Youden index and the accuracy were the highest (sensitivity, specificity, and accuracy were $63.9\%$, $83.4\%$, and $75.6\%$, respectively). For the subgroups aged 74 or under, aged 75 or over, and males, the accuracy and the Youden index were also the highest when the cutoff was set to $\frac{2}{3.}$ However, in the subgroup of females, the accuracy was highest when the cutoff was set at $\frac{2}{3}$, and the Youden index was highest when the cutoff was set at $\frac{1}{2.}$ Fig. 3 The ROC curve and cutoff for the diagnosis of prefrailty A. Correlation between KCL score and QMCOO score in the non-frail participants. $p \leq 0.001$ is considered significant B. The ROC curve was used to determine the cutoff for a diagnosis of frailty according to the QMCOO score We determined the cutoff as $\frac{2}{3}$ points and assessed its validity. The kappa statistics was 0.483 ($p \leq 0.001$), suggesting that those diagnosed as prefrail by the KCL tend to be diagnosed as prefrail by the QMCOO and that the cutoff has moderate reliability. We examined which of the QMCOO questionnaire was related to the prefrailty status. Odds ratios for prefrailty in 1-score compared with 0-score are shown in Table 3. The odds ratios were significantly > 1.0 in all the questions except for Q12 (“Do you smoke?”). Then we performed a logistic regression analysis to evaluate the factors determining prefrailty. Age, sex, BMI, and scores of the questions except for Q12 were set as the explanatory variables. Low BMI was significantly related to increased risk of prefrailty, but high BMI was not (Table 4). All the other variables significantly affected the diagnosis of prefrailty. The VIFs of these variables were below 2.0. Table 3The odds ratio for prefrailty by each QMCOO questionQuestionOdds ratioPercentage of those obtaining 1-scoreTotal($$n = 6$$,113)>=75 years old($$n = 2$$,985)<=74 years old($$n = 3$$,128)Male($$n = 2$$,807)Female($$n = 3$$,306)Q15.30$6.31\%$$6.97\%$$5.69\%$$7.37\%$$5.41\%$Q23.53$7.84\%$$6.20\%$$9.40\%$$9.16\%$$6.72\%$Q31.82$4.74\%$$2.91\%$$6.49\%$$5.88\%$$3.78\%$Q43.78$18.81\%$$21.44\%$$16.30\%$$19.67\%$$18.09\%$Q53.38$17.86\%$$18.99\%$$16.78\%$$18.03\%$$17.73\%$Q63.27$9.28\%$$8.81\%$$9.72\%$$10.30\%$$8.41\%$Q74.11$46.56\%$$57.45\%$$36.16\%$$45.71\%$$47.28\%$Q83.52$16.83\%$$18.26\%$$15.47\%$$16.99\%$$16.70\%$Q91.87$38.41\%$$36.92\%$$39.83\%$$37.26\%$$39.38\%$Q105.07$7.17\%$$7.71\%$$6.65\%$$7.73\%$$6.68\%$Q113.38$14.43\%$$16.78\%$$12.18\%$$15.46\%$$13.55\%$Q121.00 ($$p \leq 0.989$$)$8.74\%$$4.92\%$$12.37\%$$15.96\%$$2.60\%$Q132.84$3.52\%$$4.32\%$$2.75\%$$2.92\%$$4.02\%$Q143.57$2.44\%$$1.94\%$$2.91\%$$3.85\%$$1.24\%$Q152.45$3.47\%$$3.28\%$$3.64\%$$4.85\%$$2.30\%$Odds ratios for prefrailty in 1-score compared with 0-score are shown. * $p \leq 0.05$ is considered significant Table 4Logistic regression analysis to evaluate factors on prefrailtyVariableOR [$95\%$ CI]p-valueage1.052 [1.041, 1.063]< 0.001sex1.343 [1.176, 1.534]< 0.001BMI (lean)2.195 [1.705, 2.825]< 0.001BMI (obese)1.082 [0.919, 1.274]0.341Q13.156 [2.375, 4.195]< 0.001Q22.738 [2.135, 3.512]< 0.001Q31.748 [1.293, 2.364]< 0.001Q43.953 [3.355, 4.658]< 0.001Q53.626 [3.067, 4.286]< 0.001Q63.956 [3.166, 4.943]< 0.001Q72.993 [2.613, 3.427]< 0.001Q83.545 [2.991, 4.202]< 0.001Q91.658 [1.451, 1.896]< 0.001Q105.174 [3.971, 6.742]< 0.001Q113.326 [2.773, 3.989]< 0.001Q134.874 [3.459, 6.870]< 0.001Q143.820 [2.430, 6.003]< 0.001Q151.619 [1.102, 2.379]0.014(Intercept)0.001 [0.001, 0.003]< 0.001 ## Statistical analysis A t-test was used to compare the means of two groups, and a one-way analysis of variance (ANOVA) was used to compare the means of multiple groups. Comparisons of proportions were made with a chi-square test. The Pearson test was used to calculate and test the correlation coefficient between KCL and QMCOO. Logistic regression analysis was used to analyze the factors that affect frailty or prefrailty status. To examine the relationship between body weight and the frailty status, age, sex, and BMI were the explanatory variables, and the frailty status was the outcome. To examine which of the QMCOO items determine the prefrailty status, age, sex, BMI, and QMCOO items were the explanatory variables, and the prefrailty status was the outcome. P-values < 0.05 were considered significant. All the statistical analyses were performed using R 3.3.3 software (R Foundation for Statistical Computing, Vienna, Austria). ## Discussion In the present study, we have demonstrated the validity of the diagnosis of frailty with a QMCOO score cutoff of $\frac{3}{4}$ points. The QMCOO includes a question about weight loss (Q6), but unlike the KCL, does not include BMI itself. When considering the relationship between BMI and physical function, sarcopenia should also be considered. Sarcopenia is a progressive and generalized skeletal muscle disorder typically observed in older adults, requiring lower appendicular muscle mass or lower muscle quality for diagnosis in the EWGSOP2 criteria [15]. Lower BMI was related to an increased risk of sarcopenia [16]. Sarcopenia is associated with functional decline and increased risk of frailty [17]. Thus it is plausible that lower BMI was associated with frailty in the present study, but higher BMI was also associated with frailty (Fig. 2). The relationship between BMI and the prevalence of frailty is suggested to form a U-shape. A study of British people showed that the BMI range of the lowest prevalence of frailty was 25.0-29.9 kg/m2 [18], but in another study, the range was 18.5–24.9 kg/m2 [19]. In a study of community-dwelling Japanese older people, the prevalence of frailty was lowest in the BMI range of 21.4–25.7 kg/m2 [20]. In the present study, the prevalence of frailty diagnosed using the QMCOO was the lowest in the BMI range of 18.5–25 kg/m2, compatible with previous findings. Prefrailty was significantly associated with lower BMI but not with higher BMI (Table 4). The score of Q6 (weight loss) affected the prefrailty status after a logistic regression analysis. Therefore, the experience of body weight loss itself might be the risk of prefrailty, independently of BMI. This suggests that maintaining an appropriate BMI might be important to prevent prefrailty, thus avoiding frailty. However, as little is known about the background of the participants in the study, some diseases (e.g., malignancy, infections, etc.) other than natural aging could result in body weight loss, developing prefrailty or frailty. We also demonstrated that a QMCOO score cutoff of $\frac{2}{3}$ points might help diagnose prefrailty. By picking up patients with a QMCOO score of 3 or more, it might be possible to diagnose and intervene in frailty at an earlier stage. All questions except Q12 (smoking) were significantly associated with the diagnosis (Table 3). In our previous report including 223 participants, only Q1, Q6, Q7, Q10, and Q11 were related to the diagnosis of frailty [14]. In the present study, the number of participants ($$n = 6$$,113) might have sufficient statistical power. Identifying aspects of frailty and prefrailty is essential to establish their diagnostic methods. Q1 (subjective health status) and Q2 (subjective satisfaction with daily life) are unique to the QMCOO, not included in the J-CHS, the KCL, and the five-item frailty screening index [21]. The scores of both questions were significantly related to prefrailty status after the multiple linear regression analysis (Table 4). These straightforward questions about subjective health status and satisfaction could be considered to be included in a new questionnaire. Furthermore, other QMCOO items, such as Q6 (body weight loss), Q7 (loss of walking speed), and Q13 (habits of walking), significantly affected prefrailty and frailty status. By picking appropriate items from the QMCOO, a new frailty questionnaire could be developed. An important limitation of our study is that we had very limited information about the participants. We used only data about the participants’ age, sex, height, body weight, and answers to the questionnaires, but other data were missing. We included age, sex, and BMI as the explanatory variables in the logistic regression analysis but could not consider other confounding factors that might affect the frailty/prefrailty status. Only those who had not been certified as requiring support or care by the long-term care insurance were recruited. However, older adults in general tend to have multiple comorbidities even if they are independent. As stated earlier, sarcopenia and diseases could cause body weight loss and lower gait speed, which are characteristics of frailty/prefrailty. Furthermore, other factors (medication, past medical history, protein and calorie intake, exercise habits, social status, etc.) should also be considered as explanatory variables in the analysis. Since the QMCOO will be used as a platform for frailty checkups for older adults in Japan, diagnosing frailty at the same time as medical checkups can contribute to medical care for older adults. The QMCOO could be used for screening, then older adults would be formally diagnosed as frail according to the J-CHS, which is supposed to be the standard. However, the present study has limitations. To establish the QMCOO as a diagnostic tool, further studies are needed on older adults with more information about their background. In addition, the KCL was used instead of the J-CHS criteria for the diagnosis of frail and prefrail in the present study, but further research using the J-CHS is needed. Furthermore, this is a cross-sectional study in four limited areas, and the QMCOO should be validated in other regions. Thus by accumulating evidence, the QMCOO might contribute to early diagnosis and intervention of frailty and prefrailty in the future. ## Conclusion Diagnosis of frailty using the QMCOO score cutoff of $\frac{3}{4}$ points was validated. Prefrailty could be diagnosed using the QMCOO score cutoff of $\frac{2}{3}$ points. The QMCOO could be a screening tool for early diagnosis of frailty. ## References 1. Clegg A, Young J, Iliffe S. Frailty in elderly people. Lancet (2013) 381 752-62. DOI: 10.1016/S0140-6736(12)62167-9 2. Bray NW, Smart RR, Jakobi JM. Exercise prescription to reverse frailty. Appl Physiol Nutr Metab (2016) 41 1112-6. DOI: 10.1139/apnm-2016-0226 3. 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--- title: Comparison of Framingham 10-year cardiovascular risks in Sweden- and foreign-born primary health care patients in Sweden authors: - Marina Taloyan - Victor Wågström - Kristin Hjörleifdottir Steiner - Danial Yarbakht - Claes-Göran Östenson - Helena Salminen journal: BMC Public Health year: 2023 pmcid: PMC10031948 doi: 10.1186/s12889-023-15449-6 license: CC BY 4.0 --- # Comparison of Framingham 10-year cardiovascular risks in Sweden- and foreign-born primary health care patients in Sweden ## Abstract ### Background The prevalence of cardiovascular disease around the world varies by ethnicity and region of birth. Immigrants living in Sweden may have a higher prevalence of cardiovascular diseases than native-born Swedes, but little is known about their actual cardiovascular risk. This study aimed to examine the relationship in Sweden between 10-year cardiovascular risk and birthplace. ### Method This cross-sectional study was based on cardiovascular risk factor data obtained from the 4D Diabetes Project, a Programme 4D subproject in Sweden. Participants were recruited from two primary healthcare centres in Stockholm without a history of diabetes or pre-diabetes. The outcome variable was 10-year cardiovascular risk based on the calculation of a Framingham Risk Score with six risk factors: age, sex, LDL, HDL, BP, diabetes and smoking for each participant. Multiple linear regression was performed to generate β-coefficients for the outcome. ### Results There was an average of $8.86\%$ cardiovascular risk over 10 years in Sweden-born participants and a $5.45\%$ 10-year risk in foreign-born, ($P \leq 0.0001$). Foreign-born participants were about 10 years younger (mean age 46 years vs. 56 years, $P \leq 0.001$), with a significantly higher proportion of smokers ($23.9\%$ vs. $13.7\%$; $$P \leq 0.001$$). To be born in Sweden (with parents born in Sweden) was significantly associated with a 10-year cardiovascular risk in the crude model (β- coefficient = 3.40, $95\%$ CI 2.59–4.22; $P \leq 0.0001$) and when adjusted for education and alcohol consumption (β- coefficient = 2.70 $95\%$ CI 1.86–3.54; $P \leq 0.0001$). Regardless of the birthplace, 10-year cardiovascular risk was lower for those with higher education compared to those with less than 10 years of education. ### Conclusion This study found a relationship between 10-year calculated cardiovascular risk and place of birth. Sweden-born participants had a higher association with 10-year cardiovascular risk than foreign-born participants. These results contradict previous reports of higher rates of CVD in residents of Middle-Eastern countries and Middle-Eastern immigrants living in Sweden. ## Introduction Cardiovascular disease (CVD) is the leading cause of death globally, responsible for an estimated 17.9 million deaths annually, and contributing to one-third of all deaths in the world [1, 2]. An important non-modifiable risk factor for CVD is a genetic preposition, which includes sex [3]. Nevertheless, modifiable risk factors also play a huge role in CVD, as demonstrated by the fact that they are estimated to collectively cause more than half of all cardiovascular deaths [4]. In a study of patients from 52 countries, more than $90\%$ of all first-time myocardial infarctions were attributable to one or more of nine modifiable risk factors, including tobacco smoking, hypertension, dyslipidaemia, diabetes, obesity, psychosocial factors, inadequate physical activity, alcohol consumption, and poor diet [3, 5]. Various combinations of risk factors have been used to calculate CVD risk scores, including the Systematic Coronary Risk Evaluation (SCORE2) which is recommended for use in the Swedish population, Prospective Cardiovascular Münster (PROCAM), Turkish Adult Risk Factor Study (TEKHARF), and Framingham (FRS) risk scores. The scores are then used to estimate the risk of a patient having cardiovascular events (SVE) within a designated period. The FRS combines several well-established cardiovascular risk factors to determine an estimation of the risk of having a CVE within 10 years [6–8]. It can be used to guide whether to initiate suitable risk-reducing therapies, such as a statin or anti-hypertensive medications, as part of clinical care. A particular strength of the FRS is that it considers both systolic and diastolic blood pressure (BP), as well as both LDL-C and high-density lipoprotein (HDL) levels [8], whereas, for example, SCORE2 considers only systolic BP and non-HDL cholesterol levels [9]. The prevalence of CVD, around the world, measured in a variety of ways, varies by ethnic background and region of birth [10–12]. Within Sweden, the prevalence of CVD in different immigrant populations also varies substantially [13–15], but most of the studies reporting those results are now more than a decade old and showed contradictory higher or lower CVD mortality. One of those observed lower mortality risk of Swedish residents born in countries with high CVD mortality (for ex. Finland and Hungary) and a higher risk for immigrants born in low-risk CVD countries in South Europe than in their countries of birth [16]. However, another study noted that immigrants living in Sweden had a lower prevalence of CVD than comparable populations in their countries of birth [17]. In contrast, a 2016 publication reported that non-western immigrants with type 2 diabetes living in Sweden had a significantly lower risk of CVD-related mortality than native Swedes [13]. These studies suggest that immigrant populations living in Sweden may have a higher prevalence of CVD, but a lower risk of CVD-related death, than the native-born Swedish population. With these contradictory observations in mind, there appears to be a need for an updated evaluation of CVD and 10-year cardiovascular risk estimates in immigrant populations living in Sweden. The primary aim of this study was to examine the relationship between having a 10-year cardiovascular risk and birthplace (Sweden or foreign), in primary healthcare patients living in Sweden. The secondary aim was to determine whether there were any significant associations in these populations between 10-year cardiovascular risk and other potential CVD risk factors that are not part of the FRS, such as level of education, physical activity, alcohol consumption, and hs-CRP levels. ## Study population This cross-sectional study was based on data obtained from the ‘4D Diabetes Project: Screening and Treatment of Prediabetes and Diabetes in Primary Care – A Pilot Study’, a Programme 4D sub-project conducted by the Karolinska Institutet and Region Stockholm (former Stockholm County Council). Participants were recruited for the study from two primary healthcare centres (PHCC) in Stockholm. Participants were offered inclusion in the study if they were between 18 and 74 years old and had no previous history of diabetes or pre-diabetes. The Jakobsberg and Flemingsberg PHCCs were selected because large portions of their patient populations were born outside of Sweden, particularly outside of Europe. ## Data acquisition Data were collected from participants at the two PHCCs from 2013 through 2015. At the first visit, each participant was interviewed to obtain information about demography, country of birth, level of education, and self-reported health and lifestyle habits (physical exercise, smoking, and alcohol consumption). During that visit, BP was measured, and a capillary haemoglobin A1c (HbA1c) test was performed. When participants returned for a second visit, fasting blood samples were obtained, and these were analysed for serum levels of total cholesterol, HDL, calculated LDL-C, triglycerides, and hs-CRP. Also, another BP measurement was taken. For our study, the mean values of the two BP readings were used. Each participant with an HbA1c ≥ 39 mmol/mol at the first visit was offered an oral glucose tolerance test (OGTT). ## Participant characteristics For participant characteristics that represented non-Framingham CVD risk factors, we converted some of the raw data into categorical variables. Level of education categories were 0 to 9 years, 10 to 12 years, and more than 12 years. Physical exercise initially had five response alternatives, which we collapsed into three categories: no physical activity at all, more than 0 min and up to 60 min per week, and more than 60 min per week. Alcohol consumption categories were less than 4 glasses a week and 4 glasses or more per week. Smoking initially had seven response options, which we collapsed into two categories: quit or never smoked and any active smoking. Diabetes had two categories: yes and no. ## Framingham risk factors We calculated a Framingham Risk Score (FRS) for each participant, based on six risk factors, including age, sex, LDL-C, HDL, BP, diabetes, and smoking. According to FRC calculation total points for females and males were based on different points: for females: from -2 to 14 and for males: from -3 to 14. Using the system described by Viera and Sheridan, points were either awarded or subtracted for each risk factor based on being above or below cut-offs, which included the following: (a) age 35–39 years in men, 40–44 years in women; (b) LDL-C of 2.59 mmol/L to 4.13 mmol/L for both men and women; (c) HDL of 1.16 mmol/L to 1.54 mmol/L in men and 1.29 mmol/L to 1.54 mmol/L in women; (d) BP of $\frac{120}{80}$ to $\frac{129}{84}$ in men and $\frac{120}{80}$ to $\frac{139}{89}$ in women; (e) presence of diabetes; and f) smoking [7]. A 10-year cardiovascular risk was then determined using total points. We did a calculation of the total points of FRC for the entire study population and by sex separately (Table 3). ## Outcome characteristics For this study, we used the 10-year cardiovascular risk based on FRC in a regression analysis as a continuous variable. ## Birthplace groups As an independent variable, we divided the participants in this study into two groups based on birthplace: [1] Sweden-born, comprised of those who were born in Sweden, with both parents also born in Sweden; and [2] Foreign-born, comprised of those who were born outside of Sweden or had at least one parent born outside of Sweden. This was based on the official definition of foreign-born used in Sweden [18]. ## Statistical methods Descriptive statistics involving the prevalence of demographic, clinical, and outcome characteristics are presented as frequencies and proportions, and those involving Framingham risk factor characteristics are presented as means and standard deviations (SD). Comparisons were performed using Chi-square tests for categorical variables, T-tests for continuous variables following a normal distribution, and Wilcoxon-Rank sum tests for continuous variables not following a normal distribution. For continuous variables, $95\%$ Confidence intervals (CI) were calculated when means were compared. Multiple linear regression analyses were performed to generate unadjusted β-coefficients and $95\%$ CI for the outcome of 10-year cardiovascular risk and to generate adjusted β-coefficients for education and alcohol consumption. Additionally, were β-coefficients and $95\%$ CI generated stratified separately for age groups, males and females and are presented in three tables. A P value of 0.05 or less was considered significant. Calculations were performed using STATA statistical software version 17.0 [19]. ## Results A total of 830 participants were included in the study. These participants were born in Sweden with Swedish-born parents ($$n = 278$$) and outside Sweden ($$n = 552$$) with the largest portion born in the Middle East, including 86 in Turkey, 52 in Iran, and 48 in Iraq. ## Participant and outcome characteristics Table 1 presents descriptive statistics for participant characteristics and outcome measures. Foreign-born participants had significantly lower 10-year cardiovascular risk compared to Sweden-born participants (mean 5.45 vs. 8.86 respectively ($P \leq 0.0001$). Additional significant differences were found between the studied groups in age, education, and alcohol consumption. Table 1Participant and outcome characteristics of 830 participants, by birthplace, Stockholm, Sweden, 2013–2015CharacteristicsSweden-born n (%)Foreign-born n (%)Participants278 (33.5)552 (66.5)Age,* years ≤ 4047 (16.9)206 (37.3) 41–5041 (14.8)130 (23.6) 51–6056 (20.1)138 (25.0) > 60134 (48.2)78 (14.1)Sex Male122 (43.9)233 (42.2) Female156 (56.1)319 (57.8)Education, years < 943 (15.5)105 (19.3) > 9 to 12102 (36.7)128 (23.5) > 12133 (47.8)312 (57.3)Physical Activity, minutes per week None147 (53.1)295(53.9) 0 to 6056 (20.2)126 (23.0) > 6074 (26.7)126 (23.1)hs-CRPa, mmol/L ≤ 3232 (83.5)466 (84.4) > 346 (16.5)86 (15.6)Alcohol consumption, glasses per week < 4199 (71.6)511 (95.6) ≥ 479 (28.4)41 (7.4)Framingham 10-year elevated cardiovascular risk* Mean, SD $95\%$8.86 (8.00—9.72)5.45 (5.07—5.84)Framingham 10-year elevated cardiovascular risk (cut off 10), n (%) ≥ 1099 (35.6)71 (12.9) < 10179 (64.4)481 (87.1)$P \leq 0.0001$ (chi-square or T-test)ahigh-sensitivity C-reactive protein ## Framingham risk characteristics In total, there were statistically significant differences in all variables included in Framingham risk score between the two studied groups. Compared to Sweden-born participants, estimated mean or median values for foreign-born participants were significantly lower for age, LDL, HDL and systolic and diastolic blood pressure levels (Table 2). Conversely, a significantly higher percentage of foreign-born participants were smokers. Table 2Framingham risk factor characteristics for all 830 participants and by birthplace, Stockholm, Sweden, 2013–2015Risk factor characteristicsaTotal ($$n = 830$$)Sweden-born ($$n = 278$$)Foreign-born ($$n = 552$$)Age, years,mean (SD)49.0 ± 14.655.8 ± 15.045.6 ± 13.3LDL-C, mg/dL, mean (SD)3.0 ± 0.93.1 ± 0.93.0 ± 0.9**HDL, mg/dL, mean (SD)1.4 ± 0.41.5 ± 0.41.3 ± 0.4Systolic BP, mm Hg mean (SD)121.4 ± 17.4128.4 ± 18.3117.8 ± 15.8Diastolic BP, mm Hg mean (SD)76.6 ± 10.278.2 ± 10.475.8 ± 10.0Smokingb, n (%)170 (20.5)38 (13.7)132 (23.9)Abbreviations: LDL-C Low-density lipoprotein cholesterol, HDL High-density lipoprotein, BP Blood pressure, SD Standard deviation$P \leq 0.0001$$P \leq 0.001$$P \leq 0.05$aDiabetes not listed because no participants had diabetesbChi2-test ## Framingham risk characteristics by sex and place of birth Significant differences in total Framingham risk score components between females and males in the entire study population were observed (Table 3). Females had higher mean levels of HDL and lower mean levels of both diastolic and systolic BP than males. Table 3Framingham risk factor characteristics for study population by sex, Stockholm, Sweden, 2013–2015Risk factor characteristicsaTotal ($$n = 830$$)Female ($$n = 475$$)Male ($$n = 355$$)Age, years mean (SD)49.0 ± 14.648.6 ± 14.949.5 ± 14.2LDL-C, mg/dL mean (SD)3.0 ± 0.93.0 ± 0.83.1 ± 0.9HDL, mg/dL mean (SD)1.4 ± 0.41.5 ± 0.41.2 ± 0.3Systolic BP, mm Hg mean (SD)121.4 ± 17.4119.4 ± 18.3124.0 ± 15.7*Diastolic BP, mm Hg mean (SD)76.6 ± 10.274.9 ± 9.778.8 ± 10.5Smoking, n (%)170 (20.5)89 (18.7)81 (22.8)Total Framingham risk, mean, SD $95\%$6.60 (6.20—6.96)4.95 (4.59—5.31)8.80 (8.06—9.55)Abbreviations: LDL-C Low-density lipoprotein cholesterol, HDL High-density lipoprotein, BP Blood pressure, SD Standard deviation$P \leq .0001$*$P \leq .001$aDiabetes not listed because no participants had diabetes In the Sweden-born population, females had significantly higher mean levels of HDL and lower mean levels of diastolic BP than males (Table 4). In the foreign-born population, females had significantly higher mean levels of HDL and they had lower mean levels of systolic and diastolic BP than males (Table 5). In this population, a significantly lower percentage of females were smokers. Table 4Framingham risk factor characteristics for 278 Sweden-born participants, by sex, Stockholm, Sweden, 2013–2015Risk factor characteristicsaTotal Sweden-born ($$n = 278$$)Female ($$n = 156$$)Male ($$n = 122$$)Age, years mean (SD)55.8 ± 15.054.9 ± 15.556.8 ± 14.3LDL-C, mg/dL mean (SD)3.1 ± 0.93.2 ± 0.93.1 ± 0.9HDL, mg/dL mean (SD)1.5 ± 0.41.6 ± 0.41.3 ± 0.3Systolic BP, mm Hg mean (SD)128.4 ± 18.3126.6 ± 19.4130.8 ± 16.5Diastolic BP, mm Hg78.2 ± 10.476.4 ± 10.180.6 ± 10.4*Smoking, n (%)38 (13.7)26 (16.7)12 (9.8)Abbreviations: LDL-C Low-density lipoprotein cholesterol, HDL High-density lipoprotein, BP Blood pressure, SD Standard deviation$P \leq .0001$*$P \leq .001$aDiabetes not listed because no participants had diabetesTable 5Framingham risk factor characteristics for 552 foreign-born participants, by sex, Stockholm, Sweden, 2013–2015Risk factor characteristicsaTotalForeign-born($$n = 552$$)Female($$n = 319$$)Male($$n = 233$$)Age, years mean (SD)45.6 ± 13.345.5 ± 13.745.7 ± 12.7LDL-C, mg/dL mean (SD)3.0 ± 0.93.0 ± 0.83.0 ± 0.9HDL, mg/dL mean (SD)1.3 ± 0.41.5 ± 0.41.2 ± 0.3Systolic BP, mm Hg mean (SD)117.8 ± 15.8115.8 ± 16.7120.6 ± 14.1*Diastolic BP, mm Hg mean (SD)75.8 ± 10.074.3 ± 9.577.9 ± 10.4Smoking, n (%)132 (23.9)63 (19.8)69 (29.6)**Abbreviations: LDL-C Low-density lipoprotein cholesterol, HDL High-density lipoprotein, BP Blood pressure, SD Standard deviation$P \leq .0001$$P \leq .0004$$P \leq .007$aDiabetes not listed because no participants had diabetes ## Risk factors for 10-year cardiovascular risk Results of regression analyses showed that Sweden-born participants had a significant association of risk of 10-year cardiovascular risk in a crude model (β-coefficient = 3.40 $95\%$ CI 2.59–4.22; $P \leq 0.0001$) and when adjusted for education and alcohol consumption (β- coefficient = 2.70 $95\%$ CI 1.86–3.54; $P \leq 0.0001$) compared to foreign-born participants (Table 6). Regardless of the birthplace, 10-year cardiovascular risk was lower for those with higher education than for those with less than 10 years of education. Finally, those who consumed more than 4 glasses of alcohol had a higher association than those with less alcohol consumption (β-coefficient = 3.27, $95\%$ CI 2.14–4.40; $P \leq 0.0001$).Table 6β- coefficientsa for 10-year cardiovascular risk with cardiovascular risk with $95\%$ Confidence Intervals (CI), Stockholm, Sweden, 2013–2015VariablesUnadjustedb + education + alcoholPlace of birth Foreign-born0.00 (Ref)0.00 (Ref)0.00 (Ref) Sweden-born3.40 (2.59—4.22)3.42 (2.61—4.24)2.70 (1.86—3.54)Education, years < 90.00 (Ref)0.00 (Ref) 9 to12-2.04 (-3.20—.88)-2.00 (-3.12—.85) > 12-2.41 (-3.45—-1.37)-2.44 (-3.46—-1.42)Alcohol consumption, glasses a week ≤ 40.00 (Ref) > 43.27 (2.14—4.40)$P \leq .0001$$P \leq .001$aLinear regression analysis done using stepwise adjusting for independent variables, only covariates with statistically significant prevalence (see Table 1) and coefficients included in this TablebUnadjusted (crude) β-coefficients; all other results in column are adjusted coefficients Tables 7 and 8 present β-coefficients for 10-year cardiovascular risks, by place of birth with foreign-born as reference stratified for males and females separately. Significant associations were observed in the Sweden-born group when adjusted for education and alcohol in both males (β-coefficient = 4.18, $95\%$ CI 2.64–5.72; $P \leq 0.0001$) and females (β-coefficient 1.92, $95\%$ CI 1.16–2.70; $P \leq 0.0001$) compared to foreign-born participants. Table 7β-coefficientsa for 10-year cardiovascular risk with $95\%$ Confidence Intervals (CI), by place of birth with foreign-born as reference for males only, $$n = 355$$VariablesUnadjustedb + education + education, alcoholPlace of birth Foreign-born0.00 (Ref)0.00 (Ref)0.00 (Ref) Sweden-born4.98 (3.50—6.46)4.93 (3.45—6.40)4.18 (2.64—5.72)Education, years < 90.00 (Ref)0.00 (Ref) 9 to12-3.43 (-5.65—-1.20)-3.33 (-5.54—-1.13)* > 12-3.69 (-5.69—-1.70)-3.64 (-5.62—-1.67)Alcohol consumption, glasses a week ≤ 40.00 (Ref) > 42.63 (0.87—4.39)*$P \leq .0001$$P \leq .001$aLinear regression analysis done using stepwise adjusting for independent variables, only covariates with statistically significant prevalence (see Table 1) and coefficients included in this TablebUnadjusted (crude) β-coefficients; all other results in column are adjusted coefficientsTable 8β-coefficientsa for 10-year cardiovascular risk with $95\%$ Confidence Intervals (CI), by place of birth with foreign-born as reference for females only, $$n = 475$$VariablesUnadjustedb + education + education, alcoholPlace of birth Foreign-born0.00 (Ref)0.00 (Ref)0.00 (Ref) Sweden-born2.08 (1.34—2.82)2.09 (1.35—2.83)1.92 (1.16—2.70)Education, years < 90.00 (Ref)0.00 (Ref) 9 to12-1.34 (-2.34—.32)-1.33 (-2.34—.32)* > 12-2.11 (-3.01—-1.20)-2.12 (-3.03—-1.22)Alcohol consumption, glasses a week ≤ 40.00 (Ref) > 40.97 (-.33—-2.29)$P \leq .0001$$P \leq .05$aLinear regression analysis done using stepwise adjusting for independent variables, only covariates with statistically significant prevalence (see Table 1) and coefficients included in this TablebUnadjusted (crude) β-coefficients; all other results in column are adjusted coefficients Stratification for age groups generated β-coefficients for 10-year cardiovascular risks, by place of birth with foreign-born as reference is shown in Table 9. Regardless of the birthplace, significantly lower associations were observed in the group aged 41–50 with the highest education compared to lower education (β-coefficient = -1.38 $95\%$ CI -2.65-0.12; $P \leq 0.05$) and association with higher estimated 10-year cardiovascular risk in the group aged 51–60 who consumed at least 4 glasses of alcohol a week compared to those who consumed fewer than 4 glasses weekly (β-coefficient = 3.30 $95\%$ CI 1.18–5.40; $P \leq 0.05$). There were no significant differences between Swedish- and foreign-born participants in cardiovascular risk in age-stratified analyses. Table 9β-coefficients for 10-year cardiovascular risk with $95\%$ Confidence Intervals (CI), by place of birth with foreign-born as reference stratified for age groupsVariablesAge 18–40N = 253Age 41–50N = 171Age 51–60N = 194Age > 61N = 212Place of birth Foreign-born0.00 (Ref)0.00 (Ref)0.00 (Ref)0.00 (Ref) Sweden-born-.21 (-.64—.21)0.92 (-.22—2.06).49 (-1.21—2.20)1.81 (-.30—3.93)Education, years < 90.00 (Ref)0.00 (Ref)0.00 (Ref)0.00 (Ref) 9 to12.01 (-.66—.68)-1.12 (-2.63—.40).27 (-1.83—2.36)-1.56 (-4.16—1.04) > 12.10 (-.53—.73)-1.38 (-2.65—-.12)-.46 (-2.33—1.42)-.73 (-3.16—1.71)Alcohol consumption, glasses a week ≤ 40.00 (Ref)0.00 (Ref)0.00 (Ref)0.00 (Ref) > 4.10 (-.74—.77)0.67 (-1.13—2.47)3.30 (1.18—5.40)1.52 (-.73—3.78)$P \leq .05$ The results of regression in four groups by birthplace showed a similar pattern: those born in Iraq, Iran or Turkey had a significantly lower association of a 10-year cardiovascular risk than Sweden-born (Table 10).Table 10β-coefficientsa for 10-year cardiovascular risk with $95\%$ Confidence Intervals (CI), by place of birth in Iraq, Iran, and Turkey with Sweden-born as referenceVariablesUnadjusted + education, alcoholPlace of birth Sweden-born0.00 (Ref)0.00 (Ref) Born in Iraq- 4.22 (-6.01—-2.43)-3.00 (-4.85—-1.15)* Born in Iran-3.24 (-5.08—-1.41)-1.92 (-3.82—-.007)* Born in Turkey-3.38 (-4.90—-1.88)*-3.67 (-5.41—-2.10)Education, years < 90.00 (Ref) 9 to12-2.70 (-4.40—-.99)*** > 12-3.10 (-4.72—-1.48)Alcohol consumption, glasses a week ≤ 40.00 (Ref) > 43.02 (1.48—4.56)$P \leq .0001$$P \leq .001$*$P \leq .05$ aLinear regression with unadjusted (crude) β-coefficients and all adjusted coefficients (in column + education and alcohol) ## Discussion In this cross-sectional study, we examined the relationship among patients in Swedish PHC without a diagnosis of diabetes between 10-year cardiovascular risk and birthplace. The main finding was that after controlling for other variables, participants born in Sweden with parents born in Sweden had an association with higher 10-year cardiovascular risk compared with foreign-born participants. In addition, lower levels of education and higher alcohol consumption were significantly associated with the 10-year cardiovascular risk, regardless of birthplace. The finding in our study was inconsistent with other published studies, which have reported higher rates of CVD in the Middle-East countries than in countries in Western Europe, including Sweden [10–15, 20]. In our study, foreign-born participants had significantly lower systolic and diastolic BP levels than Sweden-born participants, which is similar to findings in other studies [14, 15]. Foreign-born participants also had significantly lower LDL-C levels, and they were on average 10 years younger than the Sweden-born participants. In* general, it has been shown in a study performed in 52 countries that there are nine modifiable risk factors for first-time myocardial infarction including psychosocial factors and alcohol consumption [3, 5]. However, there are biological factors that have an impact on cardiovascular risks such as age. The fact that blood lipids (LDL) and blood pressure are highly age-dependent may explain the significant differences in average percentages of risk of 10 years cardiovascular risks between the studied groups. Performed stratified age analyses showed only two significant results: regardless of country of birth, higher education and less alcohol consumption a week were associated with lower 10-year cardiovascular risk in the groups aged 41–50 and 51—60 respectively. There were no significant differences between Swedish- and foreign-born participants in cardiovascular risk in age-stratified analyses. Thus, we can conclude that differences in age between the groups in cardiovascular risk can explain the differences in cardiovascular risks to a large extent. Previous studies in Sweden on cardiovascular health in foreign-born citizens have largely focused on single countries of birth, such as Iraq [14, 15, 20]. We thought that the inclusion of participants with a wider variety of places of birth might make our results more applicable to immigrant populations throughout Sweden. However, we also acknowledge that differences in the cardiovascular health and risk profiles of individual foreign-born participants, even between those from different countries within each region, may limit the generalizability of our results to all foreign-born people in Sweden. The issue of cardiovascular risk in immigrant populations in Sweden has received very little attention, despite several studies suggesting that the prevalence of CVD in these populations may be substantial [10–15, 20]. Studies comparing Middle Eastern and Western countries (including Sweden) have consistently found a higher prevalence of CVD in the Middle East [16, 17]. Other studies have reported a higher prevalence of CVD for immigrants with or without diabetes living in Sweden who are from the Middle East [15, 19, 20], though a lower prevalence of CVD when those immigrants are compared to populations in their native countries [21]. However, a more recent report of Swedish residents suggested that CVD-related mortality risk was lower for non-Western-born immigrants than native Swedes [18]. Our study showed that participants born in Sweden were more likely to have a higher average of 10-year cardiovascular risk than those who were foreign-born. Although these studies all looked at subtly different outcomes for example Bennet and colleagues explored CVD events and CVD mortality and thus not answering the same questions, these outcomes were all closely linked to cardiovascular health. As such, the contradictory results of these studies, including ours, call into question whether the cardiovascular risk scoring systems used for native populations in Western countries apply to immigrant populations from the Middle East. The initial Framingham Heart Study from 1948 looked at a predominantly white population of European descent but subsequent studies in 1994 and 2003 enrolled more ethnically diverse individuals [21]. The Framingham criteria used for estimating 10-year cardiovascular risk in our study was based on this more diverse data, but they still lack any adjustments for different countries, regions of birth, or ethnicities [7, 8]. The contradiction between our study results and the known high prevalence of CVD in Middle Eastern populations suggests that there may be considerable value in conducting larger, longitudinal, and multigenerational studies, similar to the Framingham Heart Study, either in the Middle East or of Middle-Eastern immigrants in Sweden, or both, to construct new cardiovascular risk assessment tools or thresholds for this population. It is worth emphasizing that our findings are not representative of the entire Swedish population of primary healthcare patients since data were collected only at two PHCCs. However, it would be informative to conduct multi-generational studies of immigrant populations to determine whether cardiovascular risk and prevalence become more like that of native-born populations over time. The healthy migrant effect might influence health advantages in immigrants, however, in our sample, we lack information on the duration of residence in Sweden and the participants’ health status in their countries of origin before they emigrated to Sweden [22]. Differences between the studied groups might additionally be explained by the fact that there were healthier immigrants and less healthy Sweden-born participants living in the selected settlements (Flemingsberg and Jakobsberg) than in the other areas of Stockholm or Sweden. Regardless, our results suggest that a unique cardiovascular risk scoring system modified for the region of birth or ethnicity may be needed in Sweden. It may be prudent to update for healthcare providers to either look more closely at individual risk factors or apply a different threshold when making decisions about initiating CVD prevention or treatment interventions in patients with an immigrant background in Sweden. For example, a closer look at the Framingham risk factor results in our study suggests that smoking cessation programs and education about ways to increase HDL could potentially benefit those not born in Sweden. Taking this type of approach may help improve awareness about CVD and expand access to preventative interventions for immigrant populations in Sweden. ## Limitations The data used in our study was dependent on the population specifically recruited for the 4D Diabetes Project in Sweden. This population involved two cluster samples, comprising all willing participants from two similar primary healthcare locations. Ultimately, the Sweden-born and foreign-born study population characteristics differed significantly, particularly in the mean ages of the participants. Given that age is particularly impactful on cardiovascular risk, we do not adjust for age in either linear or logistic regression analyses as age was included in the score. In addition, no drop-out analysis was conducted of those who chose not to accept recruitment into the project. Without information about the characteristics of those who chose not to participate, the possibility of other selection biases cannot be excluded. It is possible, for example, that Sweden-born and foreign-born patients had different reasons and thresholds for when they sought care at a PHCC. On a related note, another potential limitation of this study was that the data used was gathered from patients who were actively seeking care at a PHCC. These patients were more likely than the general public to be sick and/or have other underlying medical conditions, and this could affect the generalizability of our results to a broader population. Lastly, as this was a cross-sectional study, we were unable to ascertain causality in any of the associations. Therefore, the results and conclusions of our study can at most be used to deepen the knowledge base and generate new hypotheses for future studies. ## Conclusions In this study, there was a relationship between 10-year calculated cardiovascular risk and place of birth. Sweden-born participants had a significant association with an estimated 10-year cardiovascular risk compared to the foreign-born participants. These results contradict previous reports of higher rates of CVD in residents of Middle-Eastern countries and Middle-Eastern immigrants living in Sweden. Regardless of the birthplace, healthcare providers might need to look more closely at alcohol consumption in patients with cardiovascular risks. ## References 1. 1.WHO. Cardiovascular diseases. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_12020. 2. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP. 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--- title: Liquid chromatography-mass spectrometry-based metabolomic profiling reveals sex differences of lipid metabolism among the elderly from Southwest China authors: - Yuan-Jun Huang - Wei Ke - Ling Hu - You-Dong Wei - Mei-Xue Dong journal: BMC Geriatrics year: 2023 pmcid: PMC10031952 doi: 10.1186/s12877-023-03897-z license: CC BY 4.0 --- # Liquid chromatography-mass spectrometry-based metabolomic profiling reveals sex differences of lipid metabolism among the elderly from Southwest China ## Abstract ### Background The sexual dimorphism represents one of the triggers of the metabolic disparities while the identification of sex-specific metabolites in the elderly has not been achieved. ### Methods A group of aged healthy population from Southwest China were recruited and clinical characteristics were collected. Fasting plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomic analyses were performed. Differentially expressed metabolites between males and females were identified from the metabolomic analysis and metabolite sets enrichment analysis was employed. ### Results Sixteen males and fifteen females were finally enrolled. According to clinical characteristics, no significant differences can be found except for smoking history. There were thirty-six differentially expressed metabolites between different sexes, most of which were lipids and lipid-like molecules. Twenty-three metabolites of males were increased while thirteen were decreased compared with females. The top four classes of metabolites were fatty acids and conjugates ($30.6\%$), glycerophosphocholines ($22.2\%$), sphingomyelins ($11.1\%$), and flavonoids ($8.3\%$). Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins were significantly enriched in metabolite sets enrichment analysis. ### Conclusions Significant lipid metabolic differences were found between males and females among the elderly. Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins may partly account for sex differences and can be potential treatment targets for sex-specific diseases. ## Background Sexual dimorphism is a common biological phenomenon in the nature. Robust differences are found between males and females in disease incidence, disease severity, metabolism, and pharmacodynamics of interventions. The prevalence rates of coronary heart disease, heart failure, stroke, and various metabolic syndromes are significantly higher among the males while the occurrence and severity of knee osteoarthritis are also influenced by sex, with older females affected to a greater degree by the disease compared to age-matched males [1]. Sex difference is also found in lipid and cholesterol metabolism. At homeostasis, the female is prone to incorporate free fatty acids into triglycerides whereas the male likely oxidizes circulating free fatty acids [2]. It is reported that sexual dimorphism is due to sex chromosome and the following sex-specific hormone action. Recognition and identification of sex differences are important for researchers to develop new treatments and physicians to deal with sex-specific diseases. Metabolomic is a systematic analysis of all the metabolites in a biological sample. The metabolites identified by metabolomic include amino acids, peptides, oligonucleotides, carbohydrates, organic acids, ketones, aldehydes, lipids, steroids, alkaloids, xenobiotics, and any other small molecules deriving from biological processes. The plasma metabolome of healthy individuals has already been analyzed and it is reported that sex differences are mainly correlated with amino acids and acylcarnitines, including creatine [3]. Sex can also affect the metabolome of biological fluids in an age-dependent way and previous publications identified the interaction between sex and age [4]. However, little publications emphasized aged population, as metabolism changes following the sex-specific hormones in postmenopausal females. Furthermore, the metabolic fingerprint of sex differences also varies between different races [5]. The published researches are mainly from western countries including Caucasian, African-American, Hispanic, and so on [6]. Herein, we adopted untargeted liquid chromatography-mass spectrometry (LC–MS)-based metabolomics to analyze sex-specific metabolic changes in a Chinese aged population. Plasma is chosen in the metabolomic analysis as it is a relatively accessible, stable, and informative biofluid. ## Participants A group of aged healthy population were recruited in Department of Physical Examination, the First Affiliated Hospital of Chongqing Medical University, from April 2016 to February 2017. All the participants were more than 50 years old and all the included females should be postmenopausal. The participants were without any acute illness or in acute state of chronic diseases at the enrollment. This study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University and performed in accordance with Declaration of Helsinki. Statements of informed consent were obtained from all the participants prior to inclusion in this study. Clinical characteristics and metabolomic analysis were blindly collected or performed, separately [7]. ## Clinical characteristics Clinical characteristics of all the participants were collected, including age, smoking history, alcohol consumption, hypertension, diabetes mellitus, hypercholesterolemia, and coronary heart disease. Fasting plasma samples were obtained by puncture of the median cubital vein at 6:00 am. The levels of total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein a were also determined using a Cobas Integra 400 plus automatic biochemical analyzer with matched reagent kits (Roche, Basel, Switzerland) [8]. A total of sixteen males and fifteen females were finally included in this study. No significant difference can be found in the mean age of these participants (62.56 ± 2.43 versus 65.73 ± 1.96). The males had a significant higher rate of smoking history compared with the females. There were no significant differences in the other clinical characteristics, indicating the two groups of participants were comparable. No statistical significances were found in total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein a (Table 1).Table 1Clinical characteristics of all participants with different sexes included in this studyVariable (SEM/%)Male [16]Female [15]t valuep ValueAge (year)62.56 ± 2.4365.73 ± 1.96-1.0090.312Smoking (%)9 ($56.3\%$)011.8890.001Alcohol consumption (%)4 ($25\%$)02.3680.124Hypertension (%)8 ($50\%$)4 ($26.7\%$)1.7770.183Diabetes mellitus (%)1 ($6.3\%$)1 ($6.7\%$)0.0001.000Hypercholesterolemia (%)7 ($43.8\%$)2 ($13.3\%$)2.1570.142CHD (%)1 ($6.3\%$)2 ($13.3\%$))0.0030.953TC (mmol/L)4.516 ± 0.1964.333 ± 0.1150.7900.174TG (mmol/L)1.739 ± 0.1111.411 ± 0.1381.8600.477HDL-c (mmol/L)1.200 ± 0.0881.318 ± 0.085-0.9620.812LDL-c (mmol/L)2.984 ± 0.2032.826 ± 0.1190.6570.097Apo-A1 (g/L)1.297 ± 0.0551.366 ± 0.064-0.8200.738Apo-B (g/L)0.985 ± 0.0690.873 ± 0.0341.4230.054Lpa (mg/L)181.33 ± 62.97186.57 ± 75.35-0.0540.920SEM standard error of the mean, CHD coronary heart disease, TC total cholesterol, TG triglyceride, HDL-c high-density lipoprotein cholesterol, LDL-c low-density lipoprotein cholesterol, Apo-A1 apolipoprotein A1, Apo-B apolipoprotein, Lpa lipoprotein a ## Metabolomic analysis The detailed procedure of metabolomic analysis was described in the former research [9]. Firstly, plasma samples stored at -80℃ were gradually thawed on ice, 2-chloro-1-phenylalanine dissolved in methanol (0.3 mg/mL) was served as internal standard. In a 1.5 mL Eppendorf tube, 50μL sample and 10μL internal standard were added and then vortexed for 10 s. Subsequently, 150μL ice-cold mixture of methanol and acetonitrile ($\frac{2}{1}$, vol/vol) were added. The mixtures were vortexed for 1 min, ultrasonicated at ambient temperature (25℃) for 5 min, placed at -20℃ for 10 min, and centrifuged at 15000 rpm at 4℃ for 10 min. 100μL of the supernatants from each tube were collected, filtered through 0.22 μm microfilters, and transferred to LC vials. The vials were stored at -80℃ until LC–MS analysis. Quality control sample was obtained by mixing all the samples equally as a pooled sample, and then processed using the above method with the analytic reagents. Fifteen quality controls and the whole samples were randomly injected throughout the analytical run to provide a set of data from which repeatability can be assessed. We adopted a Waters UPLC I-class system equipped with a binary solvent delivery manager and a sample manager, coupled with a Waters VION IMS Q-TOF Mass Spectrometer equipped with an electrospray interface (Waters Corporation, Milford, USA) to perform the untargeted LC–MS metabolomics. Acquity BEH C18 column (100 mm × 2.1 mm i.d., 1.7 μm; Waters Corporation) was used and the column temperature was maintained at 45℃. The separation process was achieved using the following gradient: $5\%$ B—$25\%$ B over 1–1.5 min, $25\%$ B -$100\%$ B over 1.5–10.0 min, $100\%$ B – $100\%$ B over 10.0 – 13.0 min; $100\%$ B – $5\%$ B over 13.0 – 13.5 min, and 13.5 – 14.5 min holding at $5\%$ B at a flow rate of 0.4 mL/min, where B is acetonitrile ($0.1\%$ (vol/vol) formic acid) and A is aqueous formic acid ($0.1\%$ (vol/vol) formic acid). Injection volume was 3μL. The mass spectrometric data was collected using the Waters mass spectrometer operating in either positive or negative ion mode. The source temperature and desolvation temperature was set at 120℃ and 500℃, respectively, with a desolvation gas flow of 900 L/h. Centroid data was collected from 50 to 1,000 m/z with a scan time of 0.1 s and interscan delay of 0.02 s over a 13 min analysis time. Centroid data was collected from 50 to 1,000 m/z with a scan time of 0.1 s and interscan delay of 0.02 s over a 13 min analysis time. The obtained data were processed by baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization using the build-in metabolomic software Progenesis QI (Waters Corporation). Retention time ranged from 0.5 to 14.0 min, mass ranged from 50 to 1000 Da, and mass tolerance was 0.01 Da. Isotopic peaks were excluded for analysis, noise elimination level was set at 10.00, minimum intensity was set to $15\%$ of base peak intensity, and retention time tolerance was set at 0.01 min. After that, data sets including m/z, peak retention time, and peak intensity of each ion were obtained, and further reduced by removing any peaks with missing values in more than $60\%$ of the total samples. The internal standard was used for data quality control. Metabolite identification was performed based on the following two steps. First, we used our self-constructed metabolite databank, which contains chemical standards and a manually curated compound list based on accurate mass (m/z, ± 5 ppm), retention time, and spectral patterns. Second, further metabolites were identified based on accurate mass, isotope pattern and MS/MS spectra against public databanks, including Metlin (https://metlin.scripps.edu), Human Metabolome Database (HMDB, http://www.hmdb.ca) and so on. The peak intensity was deemed as expression level of a metabolite [10]. The positive and negative peak data were merged and multivariate statistical analyses were performed by the SIMCA-P 13.0 software package (Umetrics, Umea, Sweden). The quality control samples were used to validate the stability of the metabolomic analysis. The unsupervised principal component analysis (PCA) was used to observe the data distribution. The orthogonal partial least squares-discriminant analysis (OPLS-DA) model with sevenfold cross validation was constructed to show statistical differences and recognize differentially expressed metabolites between the two groups. The constructed model was validated by a response permutation test with 200 iterations. Metabolites with variable influence on projection values (obtained from the OPLS-DA model) of greater than 1.0, fold change values of greater than 1.5 or lower than 0.67, and p values (obtained from Student t test) of less than 0.05 were recognized to be differentially expressed. Partial least squares-discriminant analysis (PLS-DA) with sevenfold cross validation was further constructed based on the above differentially expressed metabolites to visualize their differential ability between the male and females. The differentially expressed metabolites and their quantities were then exhibited as clustering heatmap. Metabolite sets enrichment analysis was further performed based on the above metabolites using MetaboAnalyst 5.0 (metaboanalyst.ca) [11]. After excluding internal standards, a total of 10,403 individual peaks, including 6040 positive and 4363 negative peaks, were detected in approximately $98.8\%$ of samples in each group. Based on these peaks, score plots from PCA and OPLS-DA analysis were performed and the results showed separations between the two groups (R2X = 0.129, R2Y = 0.855, Q2 = 0.084) (Fig. 1).Fig. 1Multivariate statistical analyses of the liquid chromatography-mass spectrometry-based metabolomics between males and females included in this study. A PCA score plot of all the participants included seven components with a R2X value of 0.661 and a Q2 value of 0.219. B OPLS-DA score plot of all the participants included two components with a R2X value of 0.129, R2Y value of 0.855, and Q2 value of 0.084. PCA, principal component analysis; OPLS-DA, orthogonal partial least squares-discriminant analysis There were thirty-six differentially expressed metabolites between the two sexes, most of which were lipids and lipid-like molecules. Twenty-three metabolites were increased in the males while thirteen were decreased (Table 2). The differentially expressed metabolites and the corresponding quantities of each sample were exhibited in clustering heatmap (Fig. 2). PLS-DA score plot indicated clear separations between two sexes based on the above differentially expressed metabolites. The score plot included two components with a R2X value of 0.516, R2Y value of 0.748, and Q2 value of 0.65, indicating the model was stable and reliable. Meanwhile, the corresponding response permutation test indicated the PLS-DA model was not over-fitting (R2 = (0.0, 0.311), Q2 = (0.0, -0.237)) (Fig. 3).Table 2Differentially expressed metabolites between males and females based on the untargeted liquid chromatography-mass spectrometry-based metabolomic analysisClassCompound IDCommon namem/zRT (min)MSIon modeVIPFCp valueFatty Acids and ConjugatesLMFA011500043-carboxy-4-methyl-5-propyl-2-furanpropanoic acid503.1907.87036Positive2.1670.5160.026HMDB02231Eicosenoic acid309.2807.86538.7Negative2.1510.5220.027HMDB02226Adrenic acid377.2697.86536.7Negative2.1570.5340.027HMDB02068Erucic acid337.3128.30337.7Negative2.3080.4100.018LMFA010201022-methyl-2E-heptenoic acid283.1914.34338.7Negative2.0790.5760.034LMFA01170038Tricosanedioic acid405.3008.30337.8Negative2.3230.3180.018HMDB047049,10-DHOME313.2395.58337.3Negative2.0530.6640.035LMFA01050152methyl 4-[2-(2-formyl-vinyl)-3-hydroxy-5-oxo-cyclopentyl]-butanoate531.2208.30138.4Positive2.2430.3330.022LMFA08020098N-palmitoyl tyrosine837.6009.57036.3Negative2.1430.5200.029LMFA0103083126:5(11Z,14Z,17Z,20Z,23Z)431.3185.83035.7Negative2.3232.4010.021HMDB00207Oleic acid327.2546.78937.5Negative2.0718.6310.050GlycerophosphocholinesHMDB10391LysoPC(20:1(11Z))572.3706.32138.5Positive2.8570.6260.002HMDB10401LysoPC(22:4(7Z,10Z,13Z,16Z))616.3636.31658.5Negative2.8880.6400.002HMDB10393LysoPC(20:3(5Z,8Z,11Z))590.3476.24837.9Negative2.3120.5780.017HMDB07952PC(15:$\frac{0}{22}$:1(13Z))822.5999.28440.4Negative2.8190.6700.003HMDB07875PC(14:$\frac{0}{18}$:3(6Z,9Z,12Z))772.5148.31733.3Negative2.1841.6950.025LMGP01050001PC(13:$\frac{0}{0}$:0)452.2795.15156.9Negative2.5621.6190.010HMDB10379LysoPC(14:0)468.3085.14937.3Positive2.6401.5560.009HMDB07892PC(14:$\frac{0}{22}$:6(4Z,7Z,10Z,13Z,16Z,19Z))800.5208.35744.3Positive3.4131.7190.000SphingomyelinsLMSP03010046SM(d18:$\frac{0}{17}$:0)717.5929.40737.2Negative1.9300.5680.049LMSP03010034SM(d18:$\frac{2}{14}$:0)695.5088.19157.6Positive3.9161.5260.000LMSP03010002SM(d18:$\frac{1}{12}$:0)691.5058.15957.2Negative3.4131.6800.000LMSP03010036SM(d18:$\frac{2}{15}$:0)731.5358.38552.1Negative2.9511.7040.003FlavonoidsLMPK12130058Ambofuracin571.1807.87036Positive2.0500.6390.036LMPK12110281Vitexin 3''',4'''-Di-O-acetyl 2''-O-rhamnoside661.1767.87235.6Negative2.3400.3100.016LMPK120800467-O-Methyllicoricidin921.4818.19338.6Negative3.5432.0720.000OthersHMDB06117APGPR Enterostatin519.2648.30137.3Positive2.3460.2650.017HMDB07065DG(14:1(9Z)/24:1(15Z)/0:0)671.55910.02036.4Positive2.1080.5470.030LMST050100166alpha-Glucuronosylhyodeoxycholate284.6666.06037.8Positive1.9890.6500.042LMST030200731alpha-hydroxy-24-(dimethoxyphosphoryl)-25,26,27-trinorvitamin D3484.3205.30737.2Positive2.5552.6520.012LMGP06010417PI(18:4(6Z,9Z,12Z,15Z)/20:5(5Z,8Z,11Z,14Z,17Z))899.4698.19835.3Positive3.9022.5490.000LMPK12140228Flavaprenin 7,4'-diglucoside665.2463.00036.6Positive2.2140.4120.023LMPK04000042Troleandomycin831.4838.19135.8Positive3.7511.8280.000LMST040101923alpha-Hydroxy-7,12-dioxo-5beta-cholan-24-oic Acid449.2555.35839Negative3.2820.3170.000HMDB00226Orotic acid357.0320.82238.3Negative1.9450.6650.047LMGP03050014PS(22:4(7Z,10Z,13Z,16Z)/0:0)594.2825.94036.9Negative2.1080.5620.030Compound ID was mainly exhibited based on the Human Metabolome Database (www.hmdb.ca) and LIPID MAPS (www.lipidmaps.org); FC value was calculated as the ratio of the average mass response (area) between the two groups (FC value = Females/males)$P \leq 0.05$ indicates significantly differences between the two groups. RT, retention time; MS, Matching score; VIP, variable influence on projection; FC, fold changeFig. 2Clustering heatmap of differentially expressed metabolites between males and femalesFig. 3PLS-DA score plot and its corresponding response permutation test based on the differentially expressed metabolites between males and females in this study. A PLS-DA score plot of all the participants included two components with a R2X value of 0.516, R2Y value of 0.748, and Q2 value of 0.65, indicating the model was stable and reliable. B Response permutation test indicated the constructed PLS-DA model was not over-fitting (R2 = (0.0, 0.311), Q2 = (0.0, -0.237)). PLS-DA, partial least squares-discriminant analysis The differentially expressed metabolites can be categorized into fatty acids and conjugates, glycerophosphocholines (GPCs), sphingomyelins, macrolides and analogues, steroid conjugates, flavonoids, fatty amides, octadecanoids, docosanoids, glycerophosphoserines, glycerophosphoinositols, glycerophospholipids, glycerolipids, and so on. The top four main classes of metabolites were fatty acids and conjugates (11, $30.6\%$), GPCs (8, $22.2\%$), sphingomyelins (4, $11.1\%$), and flavonoids (3, $8.3\%$) (Fig. 4). According to the metabolite sets enrichment analysis, fatty acids and conjugates, GPCs, and sphingomyelins were significantly enriched in the over-representation analysis (Table 3).Fig. 4Pie chart depicting the classification of differentially expressed metabolitesTable 3Metabolite sets enrichment analysis of the differentially expressed metabolites between males and females of all participantsMain classTotal compoundsHitsp valueFDRFatty Acids and Conjugates3090112.53E-076.17E-05Glycerophosphocholines470084.12E-060.000503Sphingomyelins232040.0003470.0283Macrolides and analogues810.001170.0713Steroid conjugates12310.01780.87Flavonoids530030.04151Fatty amides40610.05771Octadecanoids49810.07031Docosanoids74010.1031Glycerophosphoserines414010.4581Glycerophosphoinositols436010.4751Glycerophospholipids36,40010.9971Glycerolipids41,40010.9991FDR false discovery rate ## Statistical analysis Statistical analyses were completed using a commercially available software package (IBM SPSS version 22.0, New York, USA). Continuous data were expressed as means ± standard deviation and compared using Student t tests. Categorical data were exhibited as absolute numbers and percentage (%), and analyzed using Pearson χ2-tests or Fisher exact tests. P values less than 0.05 were considered as statistical significances [12]. ## Discussion Lipid metabolic disturbances are found in various diseases, including metabolic syndrome, cardiovascular disease, heart failure, cerebrovascular disease, and Guillain–Barre syndrome [13]. Lipid metabolism is correlated with Parkinson’s disease and related neuropsychiatric symptoms according to our former research [14]. The occurrence and development of diabetic peripheral neuropathy can be delayed by regulating lipid metabolism [15]. Sex differences were also found in very-low-density lipoproteins triglyceride and low-density lipoprotein cholesterol with age dependence [1]. We were the first to perform LC–MS-based metabolomics in Chinese aged population to clarify sex differences and found some specific lipid changes as follows. ## Fatty acids and conjugates The group of fatty acids and conjugates is a subclass of fatty acyls, which is among the eight categories of lipids in current classification system. It is associated with the occurrence and development of many diseases. The plasma levels of long-chain omega-3 and long-chain omega-6 fatty acids were associated with a lower risk of schizophrenia while short-chain omega-3 and short-chain omega-6 fatty acids were associated with an increased risk of schizophrenia [16]. The supplementation of omega-3 fatty acid can reduce major adverse cardiovascular events, cardiovascular death, and myocardial infarction [17]. It is reported that in tambaqui (Colossoma macropomum) elongation of very long-chain fatty acids enzymes and fatty acid metabolism played important roles in sexual differentiation [18]. A former gas-chromatography mass-spectrometry-based metabolomic analysis with urine samples from healthy males and females indicated that saturated fatty acids were significantly correlated with sex [19]. In this research, eleven differentially expressed fatty acids and conjugates were found and nine were significantly increased in males. These nine metabolites varied in molecular structures without obvious characteristics, including dicarboxylic acid, aromatic compound, monounsaturated fatty acid, polyunsaturated fatty acid, and the carbon numbers of those molecules ranged from eight to twenty-three. The sex-specific expression mode of fatty acids and conjugates may contribute to the differential incidences of schizophrenia and cerebral vascular diseases in males and females. ## Glycerophosphocholines GPCs are glycerophospholipids in which a phosphorylcholine moiety occupies a glycerol substitution site. GPCs are mainly exogenous and abundant in egg, soybean, beef, shrimp, and any other foods. The preferences and consumption structures of food between the two sexes lead to the different content levels of GPCs. GPCs can cross the blood–brain barrier and serve as contributors of choline and phospholipid in central nervous system. GPCs are reportedly involved in depression, anxiety, dementia, and many other neurological disorders. They can be hydrolyzed by the enzyme phospholipase A2 into lysophosphatidylcholines (LysoPCs) while lysoPCs can specifically bind to the G protein-coupled receptor family (GPR119, GPR40, GPR55 and GPR4), and induce intracellular calcium mobilization leading to increased glucose-stimulated insulin secretion. LysoPCs also have several protective or anti-inflammatory effects and serve as dual-activity ligand molecules in the innate immune system [20]. A total of eight GPCs had been found differentially expressed between males and females. The contents of LysoPCs were significantly higher in males while the lipid chains of males were significantly longer than females. ## Sphingomyelins Sphingomyelins help form lipid rafts in cell membranes and are involved in signal transduction and transportation of lipids and proteins [21]. It can be hydrolyzed by sphingomyelinases into ceramides, which are important second messengers in cell proliferation, differentiation, proliferation, and apoptosis [22]. The balance of sphingomyelins is essential for normal neuronal function and the deficiencies in enzymes of sphingomyelins metabolism can lead to various severe brain disorders. Some researches indicated blood-based sphingomyelins played crucial roles in dementia but the conclusions were inconsistent [23]. A longitudinal cohort provided evidence for sex-specific associations between sphingomyelins and dementia, which might account for the above inconsistence [24]. The changes of plasma sphingomyelins with age were statistically different by sex, and sphingomyelins decreased in males but increased in females with age [25]. Sex differences of sphingomyelins were also found in the development of stress-induced depression [26]. There were several limitations to this study. Firstly, the sample number of included participants was relatively small and further confirmation is needed. Secondly, more experimental methods should be performed to extensively identify sex differences of metabolism in plasma, including gas-chromatography mass-spectrometry and nuclear magnetic resonance profiling. ## Conclusions Significant lipid metabolic differences were found between the two sexes among the elderly. Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins may partly account for sex differences and can be potential treatment targets for sex-specific diseases. ## References 1. Bell JA, Santos Ferreira DL, Fraser A, Soares A, Howe LD, Lawlor DA. 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--- title: Escherichia coli Nissle 1917 inhibits biofilm formation and mitigates virulence in Pseudomonas aeruginosa authors: - Ahmad M. Aljohani - Cecile El-Chami - Muna Alhubail - Ruth G. Ledder - Catherine A. O’Neill - Andrew J. McBain journal: Frontiers in Microbiology year: 2023 pmcid: PMC10031955 doi: 10.3389/fmicb.2023.1108273 license: CC BY 4.0 --- # Escherichia coli Nissle 1917 inhibits biofilm formation and mitigates virulence in Pseudomonas aeruginosa ## Abstract In the quest for mitigators of bacterial virulence, cell-free supernatants (CFS) from 25 human commensal and associated bacteria were tested for activity against Pseudomonas aeruginosa. Among these, Escherichia coli Nissle 1917 CFS significantly inhibited biofilm formation and dispersed extant pseudomonas biofilms without inhibiting planktonic bacterial growth. eDNA was reduced in biofilms following exposure to E. coli Nissle CFS, as visualized by confocal microscopy. E. coli Nissle CFS also showed a significant protective effect in a Galleria mellonella-based larval virulence assay when administrated 24 h before challenge with the P. aeruginosa. No inhibitory effects against P. aeruginosa were observed for other tested E. coli strains. According to proteomic analysis, E. coli Nissle CFS downregulated the expression of several P. aeruginosa proteins involved in motility (Flagellar secretion chaperone FliSB, B-type flagellin fliC, Type IV pilus assembly ATPase PilB), and quorum sensing (acyl-homoserine lactone synthase lasI and HTH-type quorum-sensing regulator rhlR), which are associated with biofilm formation. Physicochemical characterization of the putative antibiofilm compound(s) indicates the involvement of heat-labile proteinaceous factors of greater than 30 kDa molecular size. ## Introduction Various alternative approaches to combat infection are receiving research attention including targeting bacterial virulence. Virulence-associated phenotypes in bacteria include biofilm formation and other processes involved in colonization, invasion, and persistence (Theuretzbacher and Piddock, 2019). Factors involved include adhesions, toxins, and specialized secretion systems (Finlay and Falkow, 1989). Several strategies have been evaluated to inhibit bacterial adhesion to tissues and the formation of biofilms (Hall-Stoodley et al., 2004). It has been reported that inhibiting iron sequestration in Pseudomonas aeruginosa using gallium-mediated quenching of pyoverdine siderophores reduced virulence and proliferation in a larval infection model (Ross-Gillespie et al., 2014). Toxin-neutralization (Wei et al., 2017), and agents that interfere with quorum-sensing systems are also under evaluation (Defoirdt, 2018). Quorum sensing signals which in gram-negative bacteria, include N-acyl homoserine lactones (AHLs), and peptides in gram-positive bacteria play a significant role in biofilm formation and virulence (Hauser et al., 2016) and are therefore promising targets for novel therapeutics. The main drug principles against these signaling processes are either inhibitors of molecule-receptor interactions or inhibitors of the synthesis of the signaling molecules (Rampioni et al., 2014). Bacteria may be a sustainable and diverse source of compounds with potential anti-virulence activity. An exopolysaccharide of L. acidophilus has been reported to significantly decrease biofilm formation by enterohemorrhagic E. coli (EHEC) and a range of other bacteria, including Salmonella, Yersinia enterocolitica, Listeria monocytogenes by targeting autoaggregation and attachment (Kim et al., 2009). Supernatants of Lactobacillus species, including L. plantarum, L. reuteri, L. casei, and L. salivarius, are reported to inhibit the formation of biofilms by the dental caries-associated bacterium, Streptococcus mutans, with L. salivarius having the greatest potency (Wasfi et al., 2018), an effect that was ascribed to decreased expression of genes involved in exopolysaccharide synthesis, acid tolerance, and quorum sensing (Wasfi et al., 2018). Escherichia coli Nissle 1917 has been used as a probiotic for many years to treat various intestinal disorders. Hancock et al. [ 2010] reported that this bacterium can outcompete pathogenic E. coli strains such as enteropathogenic and enterotoxigenic E. coli during biofilm formation and it has been reported that E. coli Nissle can inhibit growth, gas production and toxin production (α-toxin and NetB) by C. perfringens in a cell-dependent manner (Jiang et al., 2014). The anti-biofilm effects of various candidate probiotics have also been studied against upper respiratory tract pathogens. A mixture of Streptococcus salivarius and Streptococcus oralis is reported to significantly reduce biofilm formation by S. aureus, S. epidermidis, S. pneumoniae, P. acnes and M. catarrhalis (Bidossi et al., 2018). Similar effects of Bifidobacterium longum have been reported against EHEC O157:H7, with the autoinducer (AI-2) activity being identified as the target (Kim et al., 2012). The potential inhibitory effect of lactobacilli on the formation of biofilms by the food-borne pathogens Salmonella enterica serovar Typhimurium and Listeria monocytogenes has been investigated, where it was reported that L. rhamnosus and L. paracasei both significantly reduced biofilm formation by L. monocytogenes by competing with, excluding, and displacing pathogenic bacteria (Woo and Ahn, 2013). The study also reported that only L. paracasei significantly displaced the cells in the S. Typhimurium biofilms, but that S. Typhimurium was significantly diminished by L. acidophilus due to competition. In a study on the voice prostheses biofilms, it was reported that treating prostheses in an artificial throat model with biosurfactants derived from Lactococcus lactis and Streptococcus thermophiles led to a significant reduction in the number of bacteria and subsequently reduced the airflow resistance. This may be promising for extending the lifespan of voice prostheses (Rodrigues et al., 2004). We hypothesized that a wide range of bacteria may produce factors that inhibit various aspects of microbial virulence. The aim of the current study was therefore to investigate the antimicrobial and antibiofilm activities of a range of bacteria including organisms isolated from human skin, and the candidate probiotic E. coli Nissle 1917. E. coli Nissle 1917 is a commonly studied candidate probiotic bacterium. P. aeruginosa was used as a representative versatile pathogen. ## Bacterial strains and growth conditions The Pseudomonas aeruginosa strains used in this study were PAO1 along with SNP0614, BR1-B, LYT-4 which were wound isolates. Escherichia coli Nissle 1917 was isolated from Mutaflor tablets, manufactured by Ardeypharm (Herdecke, Germany). Comparator Escherichia coli strains were E. coli WIBG 2.4 (Oates et al., 2014) as well as E. coli K-12 MG165 and E. coli ATCC 25922. The human commensal bacteria that were tested are listed in Supplementary Table S1. All organisms were grown in Luria agar or broth and incubated aerobically at 37°C. ## Chemicals and media All chemicals and dehydrated bacteriological media were provided by (Sigma-Aldrich, United Kingdom). Petri dishes, 96-well microtiter plates and different reagents were purchased from (Sigma-Aldrich, United Kingdom). Tips, microcentrifuge tubes, solutions and growth media were sterilized in an autoclave at 121°C for 30 min. ## Preparation of bacterial supernatants Stationary phase broths (10 ml) of the test bacteria were harvested by centrifugation at 4,000 rpm for 10 min. The supernatant of the bacterium was then passed through 0.22 μm Millex-GV syringe filters to remove any resident cells. ## Antibacterial assay The methodology was adapted from Wilson et al. [ 2021]. An overnight broth culture of P. aeruginosa was adjusted spectrophotometrically to OD600 of 0.8 and then diluted 1:100 and a total of 200 μl (100 μl of the prepared broth and 100 μl of tested bacterial cell-free supernatant, $50\%$ v/v) was inoculated into 96 well plates. Control wells were inoculated with 100 μl of each pathogen together with 100 μl of its supernatant. The plate was then incubated in a Powerwave XS plate reader (Biotek, Bedfordshire, United Kingdom) at 37°C, where the absorbance of each well was measured at 600 nm every 1 h over 24 h to determine the effect of the cell-free supernatant on the growth rate of P. aeruginosa. The growth curves were analyzed using the Gen5 Software program (Biotek, Bedfordshire, United Kingdom). The experiment was run in triplicate and repeated on at least three separate occasions for each organism. ## Biofilm inhibition assay The crystal violet biofilm assay procedure was adapted from O’Toole [2011]. This was used to assess the effects of cell-free supernatant of bacteria on biofilm formation by P. aeruginosa. An overnight broth of P. aeruginosa was adjusted to OD600 of 0.8 and then diluted 1:100, and 100 μl of the prepared broth was inoculated into the wells of 96 well plates with peg lids (Calgary devices) with 100 μl of the tested bacterial cell-free supernatant. In this way, the cell-free supernatant was diluted 1:2 with a final concentration of $50\%$ v/v, and 100 μl of P. aeruginosa was inoculated with 100 μl of its supernatant used as a negative control. The compartment was then covered with the sealed peg lid and the plate was incubated for 24 h at 37°C. After 24 h, the sealed peg lid containing the biofilm was transferred first to a new 96-well plate containing PBS to wash any unattached bacteria (planktonic cells) and then the attached biofilms were stained with 200 μl of filtered $1\%$ (w/v) crystal violet and washed with PBS to wash away any excess stain. Finally, the pegs were immersed in a new well plate containing 200 μl of absolute ethanol and the absorbance of the de-stained crystal violet was measured using a plate reader at 590 nm. The percentage of biofilm inhibition was calculated using the following formula: (%) = [(Control OD 590 nm − Treated OD 590 nm)/Control OD 590 nm] × 100. ## Minimal biofilm inhibitory concentration The MBIC was determined for Nissle cell-free supernatants using a method modified from a previously described procedure (Yang et al., 2021). Nissle cell-free supernatants were serially diluted two-fold using the supernatant of the pathogen as diluent. The cell-free supernatants were diluted in the range of 50–$3\%$ (v/v). Subsequently, each concentration was co-incubated with the pathogen and tested for biofilm inhibition. The anti-biofilm activities of the different concentrations of the supernatant were compared using crystal violet biofilm assay against P. aeruginosa. The experiment was performed in triplicate. ## Biofilm disruption assay The crystal violet biofilm assay (O’Toole, 2011) was performed to assess the effect of Nissle cell-free supernatant on mature P. aeruginosa biofilms. An overnight broth of each bacterium was adjusted to an OD600 of 0.8 and then diluted 1:100. Aliquots of 200 μl of the prepared broth were inoculated into the wells of 96 well plates with Calgary device peg lids, and incubated for 24 h at 37°C to allow biofilm formation. Subsequently, the peg lid containing the biofilm was aseptically washed with PBS to remove any unattached bacteria (planktonic cells) then the attached biofilms were moved to another 96 well plates containing 200 μl of Nissle cell-free supernatant. P. aeruginosa supernatant was used as a control. The plates were incubated for a further 24 h at 37°C. After incubation periods, the biofilm was transferred to a new 96-well plate containing PBS. The attached biofilms were then stained with 200 μl of filtered $1\%$ (w/v) crystal violet and washed with PBS. Finally, the pegs were immersed in a new well plate containing 200 μl of absolute ethanol and the absorbance of the de-stained crystal violet was measured using a plate reader at 590 nm. The percentage of biofilm dispersion was calculated using the following formula: (%) = [(Control OD 590 nm − Treated OD 590 nm)/Control OD 590 nm] × 100. ## Confocal microscopy of Pseudomonas aeruginosa biofilms Pseudomonas. aeruginosa cultures incubated for 24 h were adjusted to an OD600 of 0.8 and then diluted 1:100, and 1 ml of the prepared broth was inoculated into ibiTreat petri dish (ibidi, Munich, Germany) with 1 ml of the Nissle cell-free supernatant. The dish was incubated for 24 h at 37°C. 1 ml of P. aeruginosa inoculated with 1 ml of its supernatant was used as a negative control. For assessing effects on mature biofilms, an overnight broth of P. aeruginosa was adjusted to OD600 of 0.8 and then diluted 1:100 and 1 ml of the prepared broth was inoculated into u-Dish (ibidi) and incubated for 24 h at 37°C. After incubation, the planktonic cells were removed and 1 ml of Nissle cell-free supernatant was added and then incubated for another 24 h. 1 ml of P. aeruginosa supernatant was used as a negative control. ## Confocal microscopy analysis of biofilm inhibition Biofilms were stained with the cell-impermeant nucleic acid stain TOTO-1 (2 μM for 5 min., at 37°C; Thermo Fisher Scientific, MA, United States) and counterstained with the cell-permeant nucleic acid stain SYTO-60 (10 μm for 15 min, at 37°C; Thermo Fisher Scientific, MA, United States). Samples were visualized using excitation lasers of 488 nm and 640 nm for TOTO-1 and SYTO-60, respectively. An emission band of 450–630 nm was acquired for TOTO-1 and an emission band of 656–700 nm was acquired for SYTO-60. Biofilms were observed on a Leica SP8 Inverted Tandem Head confocal microscope using a 63 × NA 1.4 oil immersion objective and a 1 × confocal zoom. The images were collected sequentially to eliminate crosstalk between channels. When acquiring Z-stack, confocal software was used to determine the optimal number of Z sections. 3D optical stack reconstruction of biofilms and quantification were performed using Imaris 9.0 software (Bitplane, Zurich, Switzerland). ## Physicochemical characterization of Escherichia coli Nissle cell-free supernatant The methodology was adapted from (Yang et al., 2021). Aliquots of the supernatant were treated with different enzymes (proteolytic and non-proteolytic): lipase, 𝛼-amylase, and proteinase K. Enzyme-treated supernatants were activated by incubating at 37°C for 3 h, after which the enzymes were immediately inactivated at 95°C for 3 min. Untreated supernatant was used as control. The heat stability of the supernatant was also evaluated by incubating aliquots of the supernatant at different temperatures of 50, 70, and 100°C for 1 h. For each of the above tests, the anti-biofilm activities of treated and untreated culture supernatants were compared using crystal violet biofilm assay against P. aeruginosa. Each data point is composed of three independent experiments performed in at least three replicates. ## Size assessment of active fractions The supernatant was separated with different molecular weight cut-offs (MWCO) using a protein concentrator (Amicon Ultra centrifugal filter units, Merck, Germany) via centrifugation at 14000 × g. The supernatants (MW < 30 MW 30 ~ 50 MW 50 ~ 100 MW > 100 kDa) were collected as a flow-through using 30, 50, and 100 K MWCO concentrators. The concentrated and un-filtrated supernatants were diluted with PBS to restore their volume to be the same as the initial volume. Each fraction was tested on P. aeruginosa biofilms using a crystal violet biofilm assay. ## Galleria mellonella pathogenicity assay This was performed as described previously (Moman et al., 2020). Larvae of G. mellonella were incubated for 30 min at room temperature before injection. Overnight cultures of P. aeruginosa and E. coli Nissle were centrifuged (4,000 rpm, 10 min) and suspended in PBS. This was repeated twice. Cultures were adjusted to an OD600 nm of 0.1 for intrahemocoelic injection, bacterial suspensions were prepared with final concentrations in the range of 104 CFU/ml to 108 CFU/ml. E. coli Nissle viable cells and the supernatant were administrated to the larvae either simultaneously with P. aeruginosa or 24 h before the injection with the pathogen. Volumes of 5 μl of each bacterium or cell-free supernatant were delivered directly to the hemocoel through an injection in the rear left pro-leg using a 26-gage needle Hamilton microsyringe (Sigma, United Kingdom). Sterile PBS (5 μl) was injected into the trauma control group and additionally, a no-treatment control group was added. The right pro-leg was used as the injection site. Different sites were used for the pathogen and the probiotic or its supernatant to reduce the risk of injection site infection. Infected larvae were incubated in a petri dish in groups of 10 at 37°C in the dark for 7 days. Larval mortality was determined daily over a week. Larvae that had turned black and that were not moving in response to a gentle shaking of the dish or touching with a pipette tip were considered dead. Dead larvae were removed from the petri dish and the death was recorded. The experimental endpoint was designated by either the death of all the larvae in the tested groups or the conversion of larvae into pupae. Pupae were identified via a color change to white (Jorjão et al., 2018). Three Petri dishes containing ten larvae each were assigned to each experiment and control group (30 larvae total for each sample). The experiments were terminated once two of the control individuals had died or pupated. ## Extraction of proteins Pseudomonas aeruginosa was grown in Luria broth overnight at 37°C. The overnight culture OD was adjusted to 0.8 then diluted 1:100 and 2 ml of the prepared broth was inoculated into a 24-well plate with or without the presence of Nissle cell-free supernatant. The plate was then incubated for 24 h at 37°C to allow biofilm formation. The bacterial biofilms were aseptically transferred into tubes and the biofilm cells were collected by centrifugation at 1500 × g for10 min at 4°C. the supernatant was discarded, and the pellet was washed twice with PBS and centrifuged at 1500 × g for 10 min at 4°C. The PBS was removed, and the pellets were weighed on a fine balance to add 3 μl of lysis buffer ($0.05\%$ SDS in 200 mM Tris–HCL pH 8.0) for every 1 mg of the pellet and sonicated for 6 min on ice. The protein concentration was measured in each sample using bicinchoninic acid (BCA) assay and 30 μg of the protein lysate was used for peptide digestion. To reduce the protein lysate, a solution of DTT (prepared in 20 mM AMBIC) was added to 30 ug protein lysate at a final concentration of 10 mM and incubated at 60°C in an incubator shaker for 30 min. Reduced cysteine residues were alkylated by adding Iodoacetamide solution (prepared in 20 mM AMBIC) at a final concentration of 50 mM and incubation proceeded for 30 min at room temperature, in the dark. Another reduction was carried out by adding a solution of DTT (prepared in 20 mM AMBIC) at a final concentration of 10 mM and incubated for 10 min at room temperature, in the dark. Mass spectrometry grade trypsin was added in a 1:50 (enzyme: protein). The digestions took place overnight at 37°C while shaking. A second digestion in $80\%$ Acetonitrile was performed by adding trypsin in a 1:100 (regardless of the protein content of the sample) and added $80\%$ (v/v) Acetonitrile in the final sample volume and incubated at 37°C for 3 h. Trypsin activity was stopped by adding $5\%$ (v/v) formic acid. The digested samples were dried to completion using a vacuum centrifuge and then stored at –20°C until analysis. ## LC–MS/MS protein analysis The extracted samples were resuspended in $0.1\%$ formic acid and 1 μg portions were analyzed by LC–MS/MS using a U3000 NanoRSLC liquid chromatography system (ThermoScientific), coupled to an Orbitrap Fusion Lumos Tribrid (ThermoScientific, San Jose, CA, United States). Peptides were previously concentrated on a trap column (C18 5 μm 0.1 × 20mm at 5 μl/min) and separated on an EASYSPRAY PEPMAP RSLC (C18 2 μm particle size, 50 cm × 75 μm i.d.; Thermo Fisher Scientific) using a 135-min method at a flow rate of 300n/min. For 5 min at $3\%$ B, 5-125 min from 3 to $35\%$B, 125–125.5 min from 35 to $95\%$B, 125.5-128 min at $95\%$B, 128–128.5 min from 95 to $3\%$B, from 128.5 to 135 min at $3\%$B. Solvents were composed of: A: $0.1\%$ formic acid, B: $80\%$ acetonitrile/$0.08\%$ formic acid). The peptides were ionized using an electrospray nanoEasySpray ion source. The mass spectrometer was operated in Date Independent Acquisition (DIA) mode. The method was divided into two sections. In the first section, the mass of the peptides was measured using an orbitrap at a resolution of 120,000, with an m/z range of 375–1,500, the maximum injection time was set to 50 ms and the normalized AGC target to $100\%$. The data were acquired in positive profile mode. In the second section of the DIA method, peptides in a defined m/z interval were fragmented using HCD at $35\%$ and the mass of the fragments was measured using an orbitrapat 30,000 resolution with a maximum injection time set to 54 ms and normalized AGC target set to $100\%$. The data were acquired in positive centroid mode. ## Statistical analyses All data are presented as the mean ± SEM of at least three independent experiments with triplicate samples within each independent experiment. All statistical tests were carried out using GraphPad Prism v9·0 software (GraphPad Software Inc., La Jolla, CA, United States). Data were analyzed by One-way ANOVA and differences were considered statistically significant at a value of $p \leq 0.05.$ G. mellonella data were plotted as survival curves using the Kaplan–Meier estimator. ## Cell-free supernatants of tested bacteria did not inhibit the growth of Pseudomonas aeruginosa in planktonic culture We determined whether cell-free supernatants of the bacterial isolates contained antibacterial substances. The supernatants showed no inhibitory effects on the planktonic growth of P. aeruginosa in a microtiter plate when incubated for 24 h. Cell-free supernatants of E. coli Nissle 1917 were also tested for antibacterial activity against different P. aeruginosa strains. E. coli Nissle cell-free supernatants did not have any inhibitory effects on the growth rate of any of the tested P. aeruginosa strains (Figure 1). Figure 1: E. coli Nissle cell-free supernatant did not significantly inhibit the planktonic growth of four strains of P. aeruginosa. Circular symbols, co-incubation of Pseudomonas aeruginosa strains with Nissle supernatant. Square symbols, control cultures. Data are mean ± SEM, n = 3. Significance was set at p < 0.05. ## Escherichia coli Nissle cell-free supernatants inhibit biofilm formation by Pseudomonas aeruginosa We subsequently determined whether cell-free supernatants of the tested bacteria had any biofilm inhibitory activities against P. aeruginosa. Cell-free supernatants of most of the bacterial isolates did not show antibiofilm effects on P. aeruginosa when incubated in a microtiter plate for 24 h. However, E. coli Nissle cell-free supernatant significantly inhibited biofilm formation by all P. aeruginosa strains (Figure 2A). To assess whether the antibiofilm activity is unique to E. coli Nissle, E. coli reference strains ATCC 25922, K-12 MG165 and a wound isolate (WIBG 2.4) were tested. The supernatants of these bacteria did not show any biofilm-inhibitory effects against P. aeruginosa (Supplementary Figure S2). Figure 2: Relative biofilm formation of P. aeruginosa (PA) strains grown in the presence of Nissle cell-free supernatant (CFS) measured by crystal violet staining. Nissle cell-free supernatant significantly inhibited biofilm formation and disrupted mature biofilms for all tested P. aeruginosa strains. Data are mean ± SEM., n = 3. ## Escherichia coli Nissle cell-free supernatant disperses mature biofilms of Pseudomonas aeruginosa We subsequently, investigated whether Nissle cell-free supernatant can disperse formed biofilm of P. aeruginosa strains. For that, the strains were allowed to adhere to the surface of a microtiter plate and form biofilms for 24 h. The formed biofilms were co-incubated with Nissle cell-free supernatant to check for disruption activity. The supernatant was significantly able to disrupt the mature biofilm produced by all strains after 24 h incubation (Figure 2B). Viable cell counts of mature P. aeruginosa biofilms treated with or without Nissle cell-free supernatants were also investigated by plate counting. The results showed no significant difference in biofilm cell counts between the control and the treated samples (Supplementary Figure S3). ## Dose–response in the inhibition of Pseudomonas aeruginosa biofilm formation by Escherichia coli Nissle cell-free supernatants To determine the lowest concentration of cell-free supernatant that could inhibit biofilm formation of P. aeruginosa, two-fold dilutions of cell-free supernatant ranging between 50 and $3\%$ (v/v) were tested. Data indicate that $25\%$ (v/v) was the lowest concentration that retained significant biofilm inhibitory activity (Figure 3). Figure 3: Dose response for Nissle cell-free supernatant (CFS) on P. aeruginosa (PA) biofilm. Different dilutions of Nissle cell-free supernatant were tested on P. aeruginosa biofilm to determine the minimal biofilm inhibitory concentration (MBIC). 25% (v/v) cell-free supernatant was the lowest concentration that caused significant biofilm inhibition against P. aeruginosa. Data are mean ± SEM, n = 3. ## Escherichia coli Nissle cell-free supernatant inhibits the accumulation of eDNA and disperses formed eDNA in Pseudomonas aeruginosa biofilm Confocal laser scanning microscopy (CLSM) was used to visualize the changes in eDNA by staining with TOTO-1 to detect eDNA and dead cells, and Syto-60 to detect live cells of P. aeruginosa biofilm matrix before and after treatment with E. coli Nissle cell-free supernatant. We found that 24 h incubation of E. coli Nissle cell-free supernatant with P. aeruginosa significantly inhibits the accumulation of eDNA of the matrix in P. aeruginosa biofilm. Moreover, we also observed that the cell-free supernatant of E. coli Nissle significantly reduced the eDNA signal in 24 h formed P. aeruginosa biofilm after treatment with E. coli Nissle cell-free supernatant. The results obtained from the confocal images suggest that the cell-free supernatant of E. coli Nissle reduced the accumulation of eDNA (Figure 4) and degraded formed eDNA on the P. aeruginosa biofilms (Figure 5). eDNA fluoresced green dead cells yellow signal and live cells red (Afshar et al., 2022). Figure 4: E. coli Nissle cell-free supernatant (CFS) reduces the accumulation of eDNA in developing biofilms. 2D and 3D confocal images of P. aeruginosa (PA) biofilms grown in the absence (A) and presence (B) of cell-free supernatants of E. coli Nissle. Biofilms were stained with the eDNA-specific cell-impermeant nucleic acid stain TOTO-1 (Green) and counterstained with a cell-permeant nucleic acid stain, SYTO-60 (Red). Viable cells appear red while eDNA appears green. Dead cells appear are yellow (co-localization of red and green color). (C) Quantification of fluorescence signals derived from CLSM 3D imaging indicates that the accumulation of eDNA of the matrix of P. aeruginosa biofilms was significantly reduced by incubation with Nissle cell-free supernatants. Data are means ± SEM (n = 3). Scale bars = 30 μm. Figure 5: E. coli Nissle cell-free supernatant (CFS) reduces eDNA in extant biofilms. 2D and 3D confocal images of P. aeruginosa (PA) biofilms grown in the absence (A) and presence (B) of cell-free supernatants of E. coli Nissle. (C) Quantification of fluorescence signals derived from CLSM 3D imaging. See legend to Figure 4. ## Proteinaceous compounds are involved in anti-biofilm activities To characterize components responsible for the activity of E. coli Nissle cell-free supernatant against P. aeruginosa, the antibiofilm effects of cell-free supernatants were evaluated against P. aeruginosa following incubation with Proteinase K, Lipase, and α-amylase. Treatment of cell-free supernatants with Proteinase K eliminated the inhibitory effects of the cell-free supernatant on P. aeruginosa biofilm, while Lipase or α-amylase did not (Figure 6A). The antibiofilm component was also inactivated by heating to 100°C (Figure 6B). Figure 6: The active factor is proteinaceous and heat-labile. Effects of Nissle cell-free supernatants (CFS) on P. aeruginosa (PA) biofilm after enzymatic and heat treatment. Proteinase K eliminated the inhibitory effects. Lipase or α-amylase did not. The anti-biofilm activity was inhibited by heating to 100°C. Data are mean ± SEM, n = 3. ## The active fraction of the antibiofilm in Escherichia coli Nissle cell-free supernatant is greater than 30 kDa in size Cell-free supernatant was separated by size fractionation using columns with different size cut-offs (30, 50, and 100 kDa) and each fraction was tested against P. aeruginosa biofilm in a microtiter plate assay. P. aeruginosa biofilm formation was significantly inhibited by 30 and 100 kDa fractions, while there was no inhibition observed in fractions lower than 30 kDa and higher than 100 kDa which indicates that the size of the antibiofilm component is between 30 and 100 kDa (Figure 7). Figure 7: P. aeruginosa (PA) biofilm formation was significantly inhibited by 30 and 100 kDa fractions of Nissle cell-free supernatant (CFS), while there was no inhibition observed in fractions lower than 30 kDa and higher than 100 kDa. Aliquots of cell-free supernatant were separated with different molecular sizes and each fraction was tested against biofilm formation by P. aeruginosa. Whole cell-free supernatant was used as a control. Data are mean ± SEM, n = 3. ## Escherichia coli Nissle cell-free supernatant protects Galleria mellonella infected with Pseudomonas aeruginosa The protective effects of viable cells of E. coli Nissle or cell-free supernatants on larvae injected with P. aeruginosa were evaluated. Initially, P. aeruginosa, E. coli Nissle viable cells and its cell-free supernatant were injected separately into the larvae to determine toxicity. P. aeruginosa was significantly lethal to the larvae. E. coli Nissle viable cells were less lethal to the larvae and no significant toxic effects of its cell-free supernatant were observed on the larvae compared to the untreated group (Figure 8). Escherichia coli Nissle viable cells or cell-free supernatants were then administrated to the larvae either simultaneously or 24 h before the pathogen challenge. Simultaneous administration of both Nissle live cells and its cell-free supernatants with the pathogen did not increase larvae survivability (Figure 8). However, Nissle cell-free supernatants were administrated 24 h before pathogen injection. This conferred significant protection to the larvae against the pathogen. However, pre-treatment with E. coli Nissle viable cells increased the mortality of the larvae (Figure 8). Figure 8: Pretreatment with E. coli Nissle cell-free supernatant (CFS) confers significant protection against P. aeruginosa toxicity in a Galleria mellonella virulence assay. Kaplan–Merier survival curves of larvae injected with Nissle cells or supernatants 24 h before, or simultaneously with P. aeruginosa. Pre-treatment with Nissle cell-free supernatant conferred significant protection to the larvae. Administration of Nissle viable cells increased larval mortality. P. aeruginosa was significantly lethal to the larvae. No significant toxic eﬀects of the Nissle cell-free supernatant were observed. Asterisks indicate statistically significant diﬀerences (p < 0.05). ## Escherichia coli Nissle cell-free supernatant downregulates essential proteins related to biofilm formation, and other virulence factors in Pseudomonas aeruginosa Biofilm-related proteins and other virulence proteins that were up or downregulated (> 1.5-fold change) in the presence of cell-free supernatant were investigated (Table 1; Figure 9). Proteomic analyses revealed that some biofilm-related proteins and other virulence factors were differentially expressed between control and cell-free supernatant-treated samples. Motility-related proteins (i.e., flagellum, flagella, and type IV pili) proteins were generally downregulated by at least a 1.5-fold change with the presence of CFS. Also, the quorum-sensing molecule acyl-homoserine lactone synthase lasI as well as HTH-type quorum-sensing regulator rhlR were downregulated (> 2.6- > 8.5-fold) respectively. In addition, rhamnosyltransferase subunits, i.e., RhlA and RhlB were significantly downregulated (> 3.6- > 15-fold) respectively. Moreover, upon treatment with cell-free supernatant, the expression level of RNA polymerase sigma factor RpoS and positive alginate biosynthesis regulatory protein algR, alginate biosynthesis sensor protein KinB were all significantly reduced (> 5- > 3.7 and > 2.2-fold) respectively compared to the control samples. However, Elastase LasB was not significantly expressed between treatments and controls. Furthermore, Pyoverdine and Pyochelin were shown to be differently expressed between control and treatment groups, i.e., L-ornithine N [5]-monooxygenase pvdA and Pyochelin synthase PchD, PchF were significantly downregulated (> 37- > 3.4- > 6.7-fold) respectively. In contrast, Fe (3+)-pyochelin receptor fptA was upregulated (> 6.2-fold) after being treated with cell-free supernatant. Moreover, type II secretion system-related proteins xcpS and xcpT were significantly downregulated (> 30 and > 4-fold) respectively. In addition, Type VI secretion system sheath protein TssC1 was downregulated by > 13 times compared to the control samples. Transport and efflux pumps-related proteins, on the other hand, were differentially expressed upon treatment with E. coli Nissle cell-free supernatant, i.e., outer membrane porin D oprD, outer membrane protein assembly factor BamD, and the outer membrane lipoprotein oprI were upregulated by (> 10.8– > 3.8– > 2.6) respectively. However, the outer membrane porin F oprF, and TonB were downregulated (> 5.1–> 15.3) respectively. ## Discussion We have demonstrated that cell-free supernatants of the probiotic bacterium E. coli Nissle 1917 (Nissle) inhibit biofilm formation and disperse mature biofilm in P. aeruginosa. Nissle cell-free supernatants significantly reduced biofilm biovolume and dispersed biovolume that had developed for 24 h (Figure 2). Dispersion is an important phenomenon in biofilm and has been studied in detail previously in P. aeruginosa (Davies and Marques, 2009). In contrast, when testing the possible inhibitory effects of Nissle cell-free supernatants against the planktonic growth of P. aeruginosa, antibacterial effects were not observed (Figure 1). The lowest concentration of Nissle supernatant that significantly inhibited P. aeruginosa biofilm formation was $25\%$ (v/v; Figure 3). Further investigation of the effect of Nissle cell-free supernatant on P. aeruginosa biofilms using confocal laser scanning microscopy showed a significantly lower eDNA signal P. aeruginosa biofilms following exposure to Nissle supernatant during biofilm development (Figure 4), and a similar reduction in the eDNA associated fluorescence following the exposure of developed biofilms (Figure 5). While the mechanism(s) responsible for this are currently unclear, the involvement of nucleases is a possible explanation. The upregulation of DNAases was however not apparent in the proteomic data, and the lack of similar anti-biofilm activity in the other E. coli test strains suggests either that another mechanism is involved or that the nuclease profiles associated with Nissle are in some manner distinct. It has recently been reported, using a similar methodology, that dead cells stained using TOTO-1 appear yellow due to the colocalization of red and green fluorophores, while eDNA appears green (Afshar et al., 2022). Lysed dead cells are a major source of eDNA within biofilms and hence may give a fluorescence signal at various steps in the intact but dead cell to the extracellular DNA pathway. In line with the current findings, a study investigating the effects of E. coli Nissle on Clostridium perfringens growth and virulence reported that Nissle inhibited biofilm formation, gas production and toxin production (α-toxin and NetB) in C. perfringens. However, the growth inhibition effect was not observed when C. perfringens was incubated with Nissle cell-free supernatants (Jiang et al., 2014). A study investigating the inhibitory effects of Nissle cell-free supernatant on Salmonella Typhimurium adhesion to porcine intestinal epithelial cells reported that Nissle cell-free supernatant inhibited Salmonella adhesion by down-regulating SiiE-mediated adhesion gene and, in agreement with the current study, Nissle cell-free supernatant did not inhibit the planktonic growth rate of the Salmonella (Schierack et al., 2011). These findings are also consistent with a recent investigation that reported anti-biofilm activity in spent cultures of several lactobacilli and bifidobacterial strains against two strains of E. coli (Abdelhamid et al., 2018). Inhibitory activities against Vibrio biofilms by Lactobacillus have also been reported (Kaur et al., 2018). In another report, supernatants derived from Pseudomonas sp. IV2006 SNIV2006 decreased biofilm formation and adhesion in Flavobacterium sp. II2003 without killing or suppressing growth (Doghri et al., 2020). Christofi et al. [ 2019] reported that E. coli can colonize the gut of healthy mice and protect them against intestinal colonization by P. aeruginosa. To determine the type of component(s) responsible for the activity of Nissle cell-free supernatant against P. aeruginosa biofilm, enzymatic and heat treatments on the cell-free supernatant were performed. Proteinase K and heat treatment at 100°C removed the capacity of cell-free supernatant to inhibit biofilm formation (Figure 6), suggesting that the active substance includes heat labile proteinaceous factors. The pH of the cell-free supernatant was initially measured to exclude the potential of acidity-mediated anti-biofilm effects, and since the cell-free supernatant was pH neutral. Moreover, the cell-free supernatants were separated by size fractionation to determine the protein/s size involved in the anti-biofilm activity. The anti-biofilm activity was present in the 30 and 100 kDa fractions, which indicates the possible involvement of multiple proteins. No inhibition was observed in other fractions (Figure 7). James et al. [ 1996] reported antibacterial and antibiofilm activity in proteins released by Pseudoalteromonas tunicate D2, and it has been additionally reported that proteinaceous exoproducts of the marine bacterium Pseudoalteromonas sp. 3J6 can inhibit the development of biofilms without affecting their planktonic growth (Klein et al., 2011; Rodrigues et al., 2015). Kim et al. [ 2009] demonstrated the capacity of L. acidophilus-released exopolysaccharide (r-EPS) to suppress EHEC biofilm formation. The r-EPS had no antibacterial effects on the planktonic form of EHEC but significantly inhibited biofilm production. The study reported that r-EPS influenced the initial adhesion and early autoaggregation steps in biofilm development. Petrova et al. [ 2016] described lectin-like compounds from in L. rhamnosus GG with anti-biofilm activity against Salmonella Typhimurium and uropathogenic E. coli biofilms. Ahn et al. [ 2018] reported that lipoteichoic acid is responsible for the anti-biofilm activity caused by L. plantarum supernatant against Streptococcus mutans biofilm. We also assessed the potential protective effects of E. coli Nissle viable cells and the cell-free supernatants on a model of bacterial virulence based on waxworm moth larvae. P. aeruginosa viable cells were significantly lethal to the larvae but E. coli Nissle viable cells were markedly less so, and no significant toxic effects of cell-free supernatant of E. coli Nissle on the larvae were observed. This protection of pathogen-infected larvae may occur via direct pathogen suppression or effects involving the immune system of larvae. Direct pathogen suppression is probably most likely if protection is conferred when the probiotic is delivered to the larvae at the same time as the pathogen. In the current study, however, protective effects were not observed when Nissle viable cells and the cell-free supernatant and P. aeruginosa were injected concomitantly (Figure 8). Regarding host-dependent mechanisms (for example by activating the immune response of larvae by the probiotic), measurable protection of the larvae was not observed when Nissle viable cells were injected before the pathogen inoculation, but the cell-free supernatant was significantly protective against the lethal effects of P. aeruginosa only when administrated 24 h before the inoculation of the pathogen. This might be due to effects on the larval immune response that subsequently inhibited infection with P. aeruginosa (Figure 8). However, elucidating such mechanisms is beyond the scope of the current study. In a previous report, the ability of Lactobacillus rhamnosus GG, Lactobacillus reuteri, and Streptococcus salivarius K-12 to confer protection against the periodontal pathogens Fusobacterium nucleatum, Porphyromonas gingivalis, and Aggregatibacter actinomycetemcomintans, G. mellonella was assessed. Prophylactic exposure to candidate probiotics conferred protection (Moman et al., 2020). A separate study was conducted by a diffrent research group to explore the effects of the probiotic Lactobacillus acidophilus ATCC 4356 on biofilm development and C. albicans infection. Here, both L. acidophilus cells and filtrates inhibited biofilm formation by C. albicans and protected G. mellonella from the lethal effects of C. albicans, with an associated decreased microbial burden recorded in the larvae (Vilela et al., 2015). In terms of other studies of protection by E. coli Nissle, it is reported that pretreatment with E. coli Nissle significantly inhibited the cellular invasion of S. pullorum in a chicken fibroblast model (Sun et al., 2022) and that supplementation with Nissle reduced Campylobacter jejuni colonization and enhanced the immune responses in infected chickens through the activation of the Th1, Th2, and Th17 pathways (Helmy et al., 2022). The protective effects of Nissle on human colonic cells infected with Campylobacter jejuni have also been investigated. Helmy et al. [ 2017] reported that pretreatment of HT-29 with Nissle conferred significant protection against invasion and intracellular survival of C. jejuni through increasing tight junction integrity and enhancing intestinal barrier function. In a similar study on the effects of Nissle on human colonic cells against Campylobacter jejuni infection, it was reported that the pretreatment of HT-29 cells with Nissle conferred significant protection against C. jejuni invasion by inducing anti-inflammatory cytokines and activating anti-apoptotic Akt signaling with associated protection against pro-inflammatory, and apoptotic responses induced by the campylobacter (Helmy et al., 2020). In the current investigation, whole proteome analysis on P. aeruginosa biofilm cells after 24 h treatment with cell-free supernatant was done to gain an understanding of the potential mechanisms (Table 1; Figure 9). The expression of motility proteins related to flagella and pili was generally downregulated by at least 1.5-fold after treatment. Flagella and pili are recognized to be important in bacterial motility, adhesion to a surface, and movement within a biofilm. Including swimming, swarming, and twitching (Overhage et al., 2007; Giraud et al., 2011; De la Fuente-Nunez et al., 2012). The adhesion of bacterial cells to a surface is recognized to be one of the initial events in the creation of a biofilm. This process is aided by bacterial mobility in some situations (De la Fuente-Nunez et al., 2012). Some bacteria then begin to multiply and create extracellular polymeric compounds. Type IV pili are engaged in twitching motility, caused by pili extension and retraction (De la Fuente-Nunez et al., 2012). Twitching activity is involved in the cell-to-cell contacts required for the creation of microcolonies and the production of cell agglomerates characteristic of mature biofilms (Giraud et al., 2011). P. aeruginosa can swarm through viscous conditions in addition to swimming and twitching motility involving coordinated cell advancement across a semi-solid surface and is dependent on both flagella and type IV pili. Swarming aids in surface colonization and is critical in the establishment of early biofilms (Overhage et al., 2007). It has been previously reported that antimicrobial peptides could target one of the major quorum sensing systems, rhl, by downregulating the expression of rhlA and rhlB proteins. The rhamnosyltransferase subunits rhlA and rhlB, are both important enzymes in the biosynthesis of rhamnolipids, which are bacterial surfactants that influence P. aeruginosa swarming motility and biofilm formation (Lin et al., 2018). In keeping with these findings are observed decreases in the synthesis of key molecules involved in P. aeruginosa pathogenicity and biofilm architecture, such as pyoverdine and rhamnolipids (Skariyachan et al., 2018). In the current investigation proteomic data suggest that treatment with cell-free supernatant of Nissle reduced the expression of RNA polymerase sigma factor RpoS and alginate-related proteins, e.g., positive alginate biosynthesis regulatory protein algR, alginate biosynthesis sensor protein KinB. It has been previously shown that these proteins influence the production of extracellular alginate, exotoxin A, and biofilm development in P. aeruginosa (Suh et al., 1999). Furthermore, secretion systems in P. aeruginosa are recognized to play important roles in P. aeruginosa virulence (Sultan et al., 2021). Treatment of P. aeruginosa with Nissle cell-free supernatant in the current study resulted in a significant reduction in type II secretion system-related proteins, e.g., xcpS and xcpT according to proteomic analysis. The type II secretion system of P. aeruginosa is involved in the extracellular release of numerous toxins and hydrolytic enzymes such as exotoxin A, lipases, phospholipases C, alkaline phosphatase, and elastase (Durand et al., 2003). A reduction level in type VI secretion system sheath protein TssC1 was also observed. P. aeruginosa within biofilm uses this enzyme to resist antibiotics (Zhang et al., 2011). Exposure to Nissle cell-free supernatant also resulted in up-regulation of the outer membrane porin D porD, outer membrane protein assembly factor BamD, and the outer membrane lipoprotein oprI. It has been reported that the basic amino acid-specific OprD porin in P. aeruginosa mediates the uptake of the β-lactam antibiotic imipenem (Trias and Nikaido, 1990; Huang and Hancock, 1993). Moreover, in P. aeruginosa, BamD interacts with T5SS to release proteins such as LepB and LepA (Meuskens et al., 2019), and oprI reportedly interacts with the peptidoglycan layer in P. aeruginosa (Duchene et al., 1989). However, in contrast, we observed a significant reduction level in other efflux pumps related proteins, the outer membrane porin F oprF, and TonB protein. The oprF is mainly engaged in several processes of P. aeruginosa infection, such as adhesion to eukaryotic cells (Azghani et al., 2002), and is known to be involved in biofilm development in cystic fibrosis (Yoon et al., 2002). Loss of OprF in P. aeruginosa PA14 induces susceptibility to a broad spectrum of antimicrobials, such as carbapenem (ertapenem), cephalosporins (cefotaxime), aminoglycosides (levofloxacin), tetracyclines (tigecycline; Dötsch et al., 2009), and the fluoroquinolone ciprofloxacin (Breidenstein et al., 2008). TonB is an energy-transducing protein that links the cytoplasmic membrane (CM) energy to several outer membrane receptors needed for the import of ferrisiderophores and other compounds that facilitate bacterial infection (Braun, 1995). Taken together, findings improve our knowledge of the mechanisms involved in the inhibitory effects of factors elaborated by E. coli Nissle against P. aeruginosa. ## Conclusion Escherichia coli Nissle 1917 cell-free supernatants inhibited biofilm formation and dispersed mature Pseudomonas aeruginosa biofilms without inhibiting bacterial growth, and reduced eDNA in developing and extant biofilms. Physicochemical characterization of the putative anti-biofilm compound indicates the involvement of proteinaceous factors. Proteomic analysis showed a significant reduction in the expression of biofilm-associated proteins in P. aeruginosa treated with the cell-free supernatant. The cell-free supernatant had a significant protective effect in a G. mellonella-based larval virulence assay when administrated 24 h before challenge with the P. aeruginosa. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Author contributions AM, CO’N, and RL: conceptualization. AA and MA: performed the antibacterial and antibiofilm assays. AA and CE-C: performed the confocal microscopy and the proteomic experiments analysis. AA: performed the Galleria mellonella pathogenicity assay. AM, CO’N, and RL: supervision. AM and AA: writing the original draft. AM, AA, CO’N, and RL: writing, reviewing, and editing. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by a PhD studentship from the Ministry of Education (Saudi Arabia). The funder had no role in study design, data collection and interpretation or the decision to submit the work for publication. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1108273/full#supplementary-material ## References 1. Abdelhamid A. G., Esaam A., Hazaa M. M.. 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--- title: 'Factors affecting the support for physical activity in children and adolescents with type 1 diabetes mellitus: a national survey of health care professionals’ perceptions' authors: - Emma J. Cockcroft - Eva L. Wooding - Parth Narendran - Renuka P. Dias - Alan R. Barker - Christopher Moudiotis - Ross Clarke - Robert C. Andrews journal: BMC Pediatrics year: 2023 pmcid: PMC10031957 doi: 10.1186/s12887-023-03940-3 license: CC BY 4.0 --- # Factors affecting the support for physical activity in children and adolescents with type 1 diabetes mellitus: a national survey of health care professionals’ perceptions ## Abstract ### Background Many children and adolescents with Type 1 Diabetes Mellitus (T1DM) don’t meet the recommended levels of physical activity. Healthcare professionals (HCPs) have a key role in supporting and encouraging children and adolescents with T1DM to be physically active. This study aims to understand the perspectives of HCPs in relation to supporting physical activity and implementing guidelines relating to physical activity. ### Methods An online mixed methods survey was circulated to HCPs in pediatric diabetes units in England and Wales. Participants were asked about how they support physical activity in their clinic and their perceptions of barriers/enablers of providing physical activity support to children and adolescents with T1DM. Quantitative data were analysed descriptively. An deductive thematic approach was applied to the free text responses using the Capability Opportunity Motivation model of Behaviour (COM-B) as a framework. ### Results Responses were received from 114 individuals at 77 different pediatric diabetes units ($45\%$ of pediatric diabetes units in England and Wales). HCPs surveyed felt that the promotion of physical activity is important ($90\%$) and advised patients to increase levels of physical activity ($88\%$). $19\%$ of the respondents felt they did not have sufficient knowledge to provide support. HCPs reported limited knowledge and confidence, time and resources as barriers to providing support. They also felt the current guidance was too complicated with few practical solutions. ### Conclusion Pediatric HCPs need training and support to be able to encourage and support children and adolescents with T1D to be a physical activity. In addition, resources that provide simple and practical advice to manage glucose around exercise are needed. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12887-023-03940-3. ## Introduction Type 1 Diabetes Mellitus (T1DM) is one of the most common chronic conditions in children and adolescents [1]. The management of T1DM requires the administration of exogenous insulin, the dose of which is adjusted according to blood glucose values and dietary intake [2]. Regular physical activity is also important in the management of T1DM and is associated with improvements in metabolic control, body composition, quality of life, and mental wellbeing, as well as protecting against future development of cardiovascular disease and premature mortality [3–5]. As a consequence of these benefits, physical activity is explicitly mentioned in National Institute for Health and Care Excellence (NICE) guidance for the management of children and young people with T1DM. Despite the benefits and clinical recommendations, studies suggest that up to $70\%$ of children and adolescents with T1DM are not meeting the recommended 60 min of moderate to vigorous physical activity per day [6] and are less physically active compared to peers without T1DM [7]. Children and adolescents with T1DM are likely to find physical activity recommendations harder to achieve than those without T1DM due to the unique difficulties associated with the management of their condition, particularly fear of hypoglycemia around exercise, lack of knowledge on how to manage blood glucose during exercise, and the need to check blood glucose levels during exercise [8]. Behaviour is not just influenced by the individual and changing behaviour requires change in multiple domains. Healthcare professionals (HCPs), such as senior doctors and specialist nurses working within paediatric diabetes clinics have a central role in ensuring recommendations around physical activity are supported and encouraged and help with behavioural change. The need for this support is highlighted by the International Society for Paediatric and Adolescent Diabetes (ISPAD), who have issued consensus guidelines for exercise in children and adolescents with T1DM [9]. These recommendations include clinical guidelines around supporting physical activity in clinics. It is currently not known if paediatric clinics in England and Wales are aware of these recommendations. Nor do we know what factors affect HCPs support for physical activity in their patients. Theoretical models can provide a framework for understanding influences on behavior. The Capability, Opportunity, and Motivation model of Behavior (COM-B) presents human behavior as resulting from interactions between ‘capability’ (physical and psychological), ‘opportunity’ (physical and social), and ‘motivation’ (autonomic and reflexive) [10]. Detailing factors influencing behaviours falling under the three COM-B components can be used to understand potentially modifiable factors to target in an intervention. Previous research has explored factors affecting children and adolescents with T1DM from the perceptions of HCPs [8], with a focus on barriers for children and adolescents and not necessarily factors which may influence the support HCPs can provide. The purpose of this study is to build on this previous work to understand the factors which influence HCPs’ ability to support physical activity and implement ISPAD guidance. ## Methods A 14-question cross-sectional survey hosted by Jisc Online survey software (https://www.onlinesurveys.ac.uk) was developed using a mixture of closed and open questions. The survey aimed to ascertain perceived barriers to HCP, support of meeting, recommended physical activity targets, and patients’ engagement in this. Demographic data were also collected on the size and location of the pediatric diabetes clinic and the professional role of the people completing the survey. Participants were asked to rate statements on a five-point Likert scale from ‘strongly agree’ to ‘strongly disagree’ on a range of themes designed to ascertain their views about their role as professionals in supporting young people with diabetes to be more active, the barriers to supporting them, their awareness of guidance informing best practice, and their views on what would facilitate increased physical activity in their patient population. A copy of the survey is included in Supplementary materials. The content of the survey was based on the recommendations from ISPAD on physical activity for young people with T1DM and building on previous unpublished work in adults with T1DM by members of the study team. The survey was piloted by five independent HCPs prior to circulation, who suggested changes in structure and wording. ## Sample Survey data were collected from September 2019 to June 2020. All 173 pediatric diabetes units in England and Wales were invited to participate via email from regional representatives of the National Children and Young People Diabetes Network. This was supplemented by social media posts and snowball sampling amongst colleagues. The participants were a convenience sample of consented participants from those units. All HCPs working in paediatric diabetes units were eligible to participate. Because of the nature of participant recruitment, it was not possible to determine the total number of HCPs approached to take part in this study. ## Data analysis Survey responses were downloaded from survey software and anonymised. Quantitative responses were exported into SPSS (version 22.0, Chicago, USA) and analysed descriptively. Where relevant, categorical data were presented as frequencies. Survey responses were checked by the first author to ensure they met the inclusion criteria of being adequately filled out (< 10 missing items) by HCPs treating children and adolescents with T1DM. No responses were excluded. Survey responses for the open-ended qualitative questions were analysed using a deductive thematic analysis using the COM-B behavior change framework [10]. The thematic approach suits questions relating to experiences, views or perceptions and is a commonily used methods for identifying and reporting within qualitative data [11]. Free text responses were imported into NVivo (version 12) for analysis. Two researchers (EC, EW) initially familiarized themselves with the free text responses before independently coding responses to the COM-B domain that they were judged to represent. Similar responses in the same COM-B domain were grouped and given a label which summarised the factors influencing behaviour. Each stage of analysis was conducted independently by two researchers (EC & EW) with uncertainties resolved through discussion.. Key factorss were described as either barriers; problems, issues, challenges, and/or difficulties with physical activity participation or support; or enablers; programmes, interventions, or factors that may improve physical activity participation or support. Findings from quantitative and qualitative open ended responses were synthesized to evaluate targets for change to improve how HCPs can support physical activity. The sysnthesis was guided by the COM-B and Theoretical Domains Framework (TDF) to facilitate organization of influencing factors and potential intervention functions. ## Sample characteristics One hundred fourteen responses were received from HCPs working at 77 different paediatric diabetes units ($45\%$ of paediatric diabetes units in England and Wales [$34\%$ ($$n = 39$$) specialist nurses, $27\%$ ($$n = 31$$) dieticians, $31\%$ ($$n = 35$$) senior doctors, $7\%$ ($$n = 8$$) psychologists, $1\%$ ($$n = 1$$) associate specialists]. $44\%$ ($$n = 50$$) of participants worked in clinics with over 200 patients on their caseload. Another $22\%$ ($$n = 25$$) of the respondents worked in clinics with 150–199 patients and $24\%$ ($$n = 27$$) in clinics with 100–149 patients. Only $11\%$ of professionals worked in smaller clinics with 51–99 patients. Clinics were spread across different regions with the highest number of responses were from HCPs working in the Southeast of England ($26\%$, $$n = 30$$) and the Northeast and Yorkshire ($21\%$, $$n = 25$$).The distributions of respondents split by county are shown in Fig. 1.Fig. 1Distribution of respondents by county $79\%$ ($$n = 90$$) of participants were aware of ISPAD guidance around supporting physical activity, of which $67\%$ ($$n = 76$$) found the guidance useful. $61\%$ ($$n = 70$$) of respondents had received a type of training around supporting physical activity. This varied from formal academic qualifications (e.g., MSc degree), conferences (such as EXTOD and PEAK), and informal advice and support from colleagues. ## HCP’s attitude towards supporting physical activity HCPs attitudes towards supporting physical activity are shown in Table 1, responses split by role are shown in Supplementary file 1. Most respondents felt that supporting ($94\%$) and promoting ($89\%$) physical activity in their patients was part of their clinical role. 12 respondents ($11\%$) were neutral or disagreed that promoting physical activity was important. $96\%$ of said that blood glucose management plans during exercise were part of an ongoing education programme for patients ($96\%$) with advice generally given even when unprompted by patients. Most respondents ($80\%$) felt they had sufficient knowledge to provide advice around physical to their patients. However, 22 respondents ($19\%$) did not feel they have sufficient knowledge to advise children and adolescents with T1DM about physical activity. Table 1HCPs views and attitudes towards supporting physical activity in clinics. $$n = 114$$itemStrongly agree, n (%)Agree, n (%)Neither agree nor disagree, n (%)Disagree, n (%)Strongly disagree, n (%)Helping children and adolescents with type 1 diabetes to be physically active is part of my clinical role74 ($65\%$)33 ($29\%$)5 ($4\%$)2 ($2\%$)0 ($0\%$)Promoting physical activity in children and adolescents with type 1 diabetes is seen as important in the clinic that you work in65 ($57\%$)37 ($32\%$)10 ($9\%$)2 ($2\%$)0 ($0\%$)I don't advise children and adolescents with type 1 diabetes about physical activity unless specifically asked by the patient1 ($1\%$)6 ($5\%$)5 ($4\%$)53 ($46\%$)49 ($43\%$)I don't advise children and adolescents with type 1 diabetes about physical activity unless the patient reports difficulties with physical activity0 ($0\%$)6 ($5\%$)4 ($4\%$)57 ($50\%$)47 ($41\%$)I have sufficient knowledge to advise children and adolescents with type 1 diabetes about physical activity30 ($26\%$)62 ($54\%$)15 ($13\%$)5 ($4\%$)2 ($2\%$)I try to encourage children and adolescents with type 1 diabetes to increase their physical activity levels42 ($37\%$)58 ($51\%$)11 ($10\%$)3 ($3\%$)0 ($0\%$)When physical activity is discussed, advice is given to children and adolescents with type 1 diabetes on managing blood glucose81 ($71\%$)30 ($26\%$)3 ($3\%$)0 ($0\%$)0 ($0\%$)Blood glucose management during exercise is included as part of the ongoing education programme for children and adolescents with type 1 diabetes76 ($67\%$)33 ($29\%$)4 ($4\%$)1 ($1\%$)0 ($0\%$) ## Factors affecting physical activity support in clinic Responses to questions relating to factors affecting physical activity support in clinic are shown in Table 2. Most respondents ($82\%$) felt confident in giving advice to their patients, but a large proportion of respondents ($44\%$) felt there was a lack of educational opportunities for HCPs relating to supporting physical activity in their patients. Only $52\%$ thinking that the current provision of educational materials for patients was appropriate. Table 2HCPs views on factors affecting physical activity support in clinicitemStrongly agree, n (%)Agree, n (%)Neither agree nor disagree, n (%)Disagree, n (%)Strongly disagree, n (%)I do not have enough time to discuss physical activity with children and adolescents with type 1 diabetes0 ($0\%$)16 ($14\%$)25 ($22\%$)54 ($47\%$)19 ($17\%$)Educational materials about physical activity are inappropriate for children and adolescents with type 1 diabetes4 ($4\%$)18 ($16\%$)33 ($29\%$)44 ($39\%$)15 ($13\%$)There is a lack of educational opportunities for health professionals regarding physical activity in children and adolescents with type 1 diabetes8 ($7\%$)42 ($37\%$)24 ($21\%$)37 ($32\%$)3 ($3\%$)Children and adolescents with type 1 diabetes are unlikely to be motivated to follow advice to be more active1 ($1\%$)17 ($15\%$)34 ($30\%$)47 ($41\%$)15 ($13\%$)I feel confident giving children and adolescents with type 1 diabetes advice on managing blood glucose with physical activity36 ($32\%$)57 ($50\%$)12 ($11\%$)15 ($13\%$)3 ($3\%$) ## Barriers and enablers of supporting physical activity From the free text survey responses, we identified a a number of factors within each COM-B domain.thefrequency, whether they are barriers or enablers, along with supporting quotes are presented in Table 3 and summarised in Fig. 2.Table 3Summary of COM-B themes classified and Barriers and Enablers for health care professionalsCOM-B componentFactorBarrier/enablerFrequencySample QuotePsychological CapabilityKnowledge and skillBarrier15“no support/programmes/resources to support children with physical activity”“no formal qualification. unsure of ability to suggest insulin dose changes”“A lack of training around how to manage blood glucose levels with activity—this is not standard training given to psychologists”Complicated guidanceBarrier4“No explicit guidance is given around the practicalities of what exercise to promote and when and how to review this in a professional capacity”EducationEnabler50“Better training and local diabetic networks to work on agreed consensus”“National guidance—e.g. flow chart or info sheet to provide to families Making it auditable as to whether it is discussed. ”“opportunity to network attend education specific to diabetes, type 1 and CYP”Physical opportunityResourcesBarrier10“Making it easier to understand no support/ programmes/ resources to support children with physical activity”“I feel able to offer appropriate advice if they desire it, but if I am seeking to persuade them to increase exercise (i.e. change their behaviour) this requires intensive coaching-style support which we do not have resource to offer. ”ResourcesEnabler38“Places to signpost children and adolescents to—lots of sites available but not very child focused”“Simplified instructions Electronic resources to direct patients to” TimeBarrier37“Sometimes there isn't adequate time in clinic. ”“Having specific time to spend a long discussion in the clinic setting.” TimeEnabler15“Time to discuss these issues,”“To acknowledge that this topic is required to be discussed as one of the important factors and one of the basics of the diabetes care. Have available time to support 'fine tuning' of advice.” Social opportunityColleague supportBarrier6“lack of support from colleagues and no formal qualification. ”“consistent team approach. support from colleagues and no formal qualification. unsure of ability to suggest insulin dose changes” Reflective motivationAuditing and Tarif paymentsEnabler5“Make this a mandatory part of diabetes care e.g. inclusion of PA measurement and support in the National Paediatric Diabetes Audit. ”Fig. 2Summary of HCP level barriers and enablers to supporting physical activity ## Psychological capability Two barriers to HCP support for physical activity were identified: [1] knowledge and skills and [2] complicated guidance. Knowledge and skills related to the HCPs ability and confidence in providing support for young people. Several respondents suggested that the lack of formal qualifications and training could inhibit their ability to provide support: “no formal qualification. unsure of the ability to suggest insulin dose changes” (Dietitian). This barrier was coupled with current complicated guidelines: “No explicit guidance is given around the practicalities of what exercise to promote and when and how to review this in a professional capacity”. ( Psychologist). The provision of knowledge and education was frequently suggested as an enabler to the provision of support for physical activity. One HCP stated “Better training and local diabetic networks to work with an agreed consensus” (senior doctor) as an approach to improve support. ## Physical opportunity Time and resources were identified as both barriers and enablers to support physical activity. In terms of resources, HCPs would value somewhere to direct patients to simplified instructions and child-focused information. The lack of appropriate resources at present was noted as a barrier, for example, one participant noted: “Making it easier to understand no support/ programmes/ resources to support children with physical activity” (specialist nurse). The second theme relating to physical opportunity was time. It was noted that “there isn’t adequate time in clinic” (Dietitian) and that it would be beneficial to have “time to discuss these issues” (senior doctor) and to “have available time to support the fine tuning of the advice” (dietitian). ## Social opportunity Colleague support, was noted as a barrier to supporting physical activity. There was a “lack of support from colleagues” (dietitian) and wasn’t a “consistent team approach” (dietitian). ## Reflective motivation Auditing and tariff payments were suggested as an enabler for supporting physical activity. “ Make this a mandatory part of diabetes care, e.g. inclusion of physical activity measurement and support in the National Paediatric Diabetes Audit” (dietitian). ## Synthese of findings – interventions suggestions From the synthesis of findings we identified 5 intervention functions which were relevant for enabling HCPs to support physical activity in adolescents with T1DM. The synthesis and links between the COM-B model, TDF and intervention functions are shown in Table 4.Table 4Synthesis of findings. Links between COM-B model, TDF domains and suggestions of intervention functions to enable HCPs to support physical activity in adolescents with T1DMbarriers and enablers for supporting adolescents with T1DM to be physically activeTDF domains linking to COM-B componentsPossibly intervention functionsCAPABILITYPsychological Capability:Limited knowledge on what to advise in terms of adjustments to insulin and carbohydrate intake, as well as how different forms of exercise may impact blood glucose levelsKnowledge and skillsEducation, Training – increasing HCP knowledge and understanding of physical activity as well as skills training to help them motivate and encourage behaviour change in adolescents they care forOPPORTUNITYPhysical opportunity:TimeEnvironmental context and resourcesEnvironmental restructuring – Ensure HCPs have time to have discussions around physical activity or prioritise these conversationsResources – have the necessary materials to be able to signpost toEnvironmental context and resourcesEnablement – Provide resources to allow HCPs to signpost to advice. Ensure guidance is easy to follow and accessibleCurrent guidelines that HCPs are meant to follow are Complex and so hard to implement in practiceSocial opportunity:Colleague support – having support from others to do it and people around them doing itSocial influencesModelling, Enablement – Making conversations about physical activity a normal part of consultations. Having physical activity champions within clinicsMOTIVATIONReflective motivation:Important part of clinical role – Have a sense that they should be doing itMemory, attention and decision ProcessesTraining, environmental restructuring – Enuring that supporting physical activity remains an important part of clinical role through training and providing prompts to have conversations with patientsAuditing and tariff paymentsReinforcementIncentivisation, Environmental restructuring – Making support for physical activity part of clinical care and auditable, or incentivising promotion of physical activityAutomatic motivation:ConfidenceBeliefs about capabilitiesEducation, Modelling – education to boost confidence in providing evidence based advice to patients ## Discussion The results from this study provide valuable insight into current practice and how children and adolescents with T1DM can be better supported to achieve physical activity guidelines, as well as how HCPs can be assisted to enable this change in behavior. This study used the COM-B theoretical framework of behavior to understand the potential barriers and enablers to physical activity support from the perspective of HCPs working in paediatric diabetes units. In the present study, we found that the majority of HCPs valued the importance of promoting and supporting their patients to be physically active, despite highlighting a number of factors that influence their ability to support patients with this. To our knowledge, this is the first study which has used the COM-B theoretical framework to understand both barriers and enablers to supporting physical activity from the perspective of HCPs, helping to situate the findings within a frameowork of behvioural theory to develop an intervention to address the identified barriers. Previous studies have investigated the perspective of patients [12–15] their families [16–18], and HCPs perspectives of children and adolescents’ barriers [19], but not specifically their own barriers to support. This topic has recently been reviewed by Dash and colleagues [2020] [8]. Given the role of HCPs in supporting children and adolescents with T1DM [20], a theoretical understanding of factors which may inhibit and enable better support is vital to try and address the current low levels of physical activity within this population. Our results highlight a number of factors across the COM-B domains which can be targeted in future interventions which aim to improve physical activity in children and adolescents with T1DM. The majority of respondents reported that supporting and promoting physical activity was part of their clinical role ($95\%$ and $80\%$ respectively), however approximately 1 in 5 respondents felt they did’t have sufficient knowledge. These findings are in line with previous work from Ilkowitz and colleagues [21] in the USA. In their sample, $85.5\%$ of providers believed that counselling regarding exercise for pediatric patients was a priority. Interestingly, this study also highlighted the limited knowledge of providers around physical activity guidelines with $79.3\%$ of respondents not able to correctly identify American Diabetes Association guidelines correctly. This goes beyond our data which is limited to self-reporting of own knowledge and confidence levels instead of more formal ‘testing’. Data from the present study also suggest that $80\%$ of HCPs are aware of ISPAD guidelines, with $67\%$ of those aware finding them helpful. Given the importance of physical activity, it is important to raise awareness of the use of this available guidance and work with staff to ensure it is in an accessible and helpful format to be implemented in clinics. Despite respondents suggesting they are able to provide support for children and adolescents with T1DM as part of their clinical role, the findings highlight a number of barriers relating to this support. Acknowledging these barriers is important as support received from professionals has been shown to facilitate participation in physical activity [22]. The main aspect of psychological capability was the acknowledgement that there were limited support and training opportunities for HCPs, with no formal qualifications and no explicit guidelines around the practicalities of promoting physical activity. Only $26\%$ of respondents reported to strongly agree that they had sufficient knowledge relating to this and HCP education was frequently suggested as an enabler to supporting children and adolescents with T1DM. These findings suggest the importance of developing and providing formal training to staff and have clear national guidelines to help enable support for children and adolescents with T1DM. The suggestion of the importance of education has been previously discussed by others [19], but with focus on patient and parent education rather than specifically on HCPs. Given the important role of HCPs in supporting patient care, we would argue that both are essential components of supporting physical activity. Quantitative responses to this survey suggest that most HCPs had adequate time, however responses to free text questions indicate that HCPs perceive time to be a barrier to providing specific support for children and adolescents with physical activity. The idea of time was often related to complex cases and fine tuning advice, rather than giving simple generic information. The idea of lack of time has previously been described elsewhere in a qualitative synthesis of factors that affect participation in physical activity among children and adolescents with T1DM [8], where it was stated that current deliverers of education may have a lack of time to cover all areas. It was suggested by a number of participants in our study that including an assessment of physical activity in tariff payments or in the National Pediatric Diabetes Audit could help to ensure physical activity is supported and monitored in clinics. We suggest this could be in an easy-to–to-implement format of the physical activity questionnaire or more formal objective measurements of physical activity with accelerometers at annual reviews. It is important to acknowledge that HCPs promotion physical activity and providing advice to children and adolescents is part of a complex system of behaviours. In isolation improving the support for HCPs is only part of the solution and may not alone increase physical activity levels of children and adolescents with T1DM. As previously reported by others [8] factors such as motivation of children and adolescents to be physically active, fear of hypoglycemia and social support are also important factors and need to be considered alongside any HCP focused intervention. An additional consideration is the complexity of support, with different recommendations and lack of evidence based consensus on adjustments to insulin (CSII and MDI), carbohydrate intake and the effects of different exercise types and intensities of blood glucose specifically for children and adolescents [23]. The findings of the current study should be considered in light of several limitations. Firstly, the convenience sample of this survey means that HCPs were self-selected and may not be representative. HCPs with more experience of supporting physical activity or who value the importance of physical activity may have been more likely to complete this survey, reducing the generalizability of the results. However, a range of opinions seem to be captured and our sample has highlighted a number of barriers which add to the existing body of research in this area. Secondly, the use of survey methodology meant we could not explore the suggested barriers in depth with the researcher unable to follow up ideas and clarify issues. However, this methodology has allowed us to gain insight into individuals' perspectives and experiences [24] in a relatively large sample of HCPs across England and Wales, reaching $44\%$ of paediatric diabetes units. ## Conclusions and recommendations Our findings suggest that HCPs feel that they need more specific training and support to enable them to give advice to children and adolescents. Findings from this study suggest that developing a HCP focused educational rources to help increase knowledge and understanding around physical activity and T1DM would be a useful approach to help HCPs to provide improved support for children and adolescents with T1DM. Future research should develop a more indeph understanding of the educational needs of HCPs as well as how best to provide this support in terms of content, format and implementation considerations such as time to undertake any training. Additionally findings suggest the potential impact of developing patient friendly resources to help in their support as well as incentivisation, for example, by including physical activity support as part of the paeadiatric best practive tarif. ## Supplementary Information Additional file 1: Table S1. HCPs views and attitudes towards supporting physical activity in clinics. $$n = 114$.$ Split my role. Table S2. HCPs views on Factors affecting physical activity support in clinic, split by role. ## References 1. Jin MW, An Q, Wang L. Chronic conditions in adolescents (Review). Exp Ther Med (2017.0) 14 478-482. DOI: 10.3892/etm.2017.4526 2. DiMeglio LA, Acerini CL, Codner E, Craig ME, Hofer SE, Pillay K. 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--- title: The correlation between the serum LDL-C/Apo B ratio and lumbar bone mineral density in young adults authors: - Anjun Tan - Juntao Shu - Hong Huang - Heng Shao - Jingjing Yang journal: BMC Musculoskeletal Disorders year: 2023 pmcid: PMC10031959 doi: 10.1186/s12891-023-06325-w license: CC BY 4.0 --- # The correlation between the serum LDL-C/Apo B ratio and lumbar bone mineral density in young adults ## Abstract ### Background Numerous studies have confirmed that atherosclerosis is related to osteoporosis (OP), and patients with atherosclerosis are more prone to OP. The ratio of low-density lipoprotein cholesterol (LDL-C) to apolipoprotein B (Apo B) is the valid indicator of atherosclerosis. Nevertheless, conclusions regarding relation between LDL-C/Apo B ratio and bone mineral density (BMD) are still lacking. As a result, this study concentrated on investigating the relationship between LDL-C/Apo B ratio and lumbar BMD in the young adult population according to the National Health and Nutrition Examination Survey (NHANES). ### Methods Information of 2027 young adults (age 20–40 years) from NHANES database was obtained for this cross-sectional study. The correlation between serum LDL-C/Apo B ratio and lumbar BMD was explored through weighted multiple stratified linear regression, while the smooth curve fitting model was utilized for analyzing nonlinear relation. In the nonlinear relation, the inflection point was calculated by saturation threshold analysis. The weighted two-piecewise linear regression model was constructed. ### Results After covariates were adjusted, the relation between serum LDL-C/Apo B ratio and lumbar BMD varied by sex (males: β = -0.0126, $95\%$ CI -0.0892, 0.0640; females: β = 0.0322, $95\%$ CI -0.0367, 0.1011). By performing age-stratified subgroup analysis, the association also varied by age and sex. Males aged 20–30 years presented a negative trend (β = -0.0570, $95\%$ CI -0.1656, 0.0517), and males with the age of 31–40 years showed a positive trend (β = 0.0810, $95\%$ CI -0.0312, 0.1931). Women showed a positive trend by age (females of 20–30 years: β = 0.0051, $95\%$ CI -0.0935, 0.1036; females of 31–40 years: β = 0.0265, $95\%$ CI -0.0767, 0.1296). In race-stratified subgroup analysis, the relations varied by sex and race. To be specific, non-Hispanic black males showed a negative trend (β = -0.0754, $95\%$ CI -0.2695, 0.1188), and males of other races exhibited a positive trend. The trend was positive for women of all races. ### Conclusion Differences were detected in the association between serum LDL-C/Apo B ratio and lumbar BMD among cases aged 20–40 years across sex, age, and race/ethnicity. In addition, the inflection points in U-shaped relationships were also calculated. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12891-023-06325-w. ## Introduction Osteoporosis (OP) displays the features of bone mass loss, bone quality impairment, and a higher risk of fracture among males and females. It shows association with high risk of complications and mortality [1, 2], and exerts a serious impact on its socioeconomic burden. Therefore, special attention should be paid to modifiable risk factors such as smoking, alcohol consumption, decreased body mass index, physical inactivity and poor nutrition [3]. In the past, the management for cases showing the above characteristics has emphasized the identification of secondary factors that lead to decreased bone mass and the treatment of fractures by pain control and orthopedic procedures [4]. Due to the measurements of bone mineral density (BMD), improved treatment skills and increased awareness among the population, OP has emerged as the primary disorder with several manifestations, which can be managed through aggressive prevention and treatment. Great progress has been made in understanding the complicated pathogenic mechanisms of the disease [5, 6]. Consensus has been reached on the relationship strength of reduced BMD with the risk of fracture and on the significance of bone quality as another risk factor for fracture [7]. Recently, some scholars have proposed that bone is an endocrine organ that regulates metabolic homeostasis by releasing bone-specific peptides [8]. A number of bone diseases, including OP, are associated with metabolic changes, and increasing attention has been focused on the close relationship between lipid metabolism and bone metabolism. It is known that cholesterol is related to bone metabolism, and vitamin D, a critical metabolite, exerts an important effect on maintaining bone calcification. Hypercholesterolemia has also been shown to induce bone loss [9]. Cholesterol delivery induced by low-density lipoprotein cholesterol (LDL-C) obviously enhances osteoclast viability, while LDL-C depletion inhibits osteoclast formation [10]. Apolipoprotein B (Apo B) represents a vital part of lipid metabolism. A high plasma Apo B level is an unfavorable factor of atherosclerosis [11], it can be used as a routine lipid test to assess atherosclerosis risk [12]. In addition, cases of cardiovascular disease have also been shown to be associated with an increased risk of OP [13]. Atherosclerotic lesions affect not only peripheral blood vessels but also intraosseous arterioles [14]. Besides, patients with atherosclerosis are also more vulnerable to OP [15]. This may be ascribed to the decreased bone turnover rate caused by bone microvascular disease [16]. All of the aforementioned studies suggest a correlation between cholesterol metabolism, atherosclerosis and OP. Based on the ability of Apo B to assess atherosclerosis risk, Apo B may be an indicator to assess OP. The LDL-C/Apo B ratio has been identified to be the valid proxy of LDL particle size [17], which has been contained as an influencing factor in several studies, including the Framingham Heart Disease Study [17–19]. Currently, conclusions regarding relation between LDL-C and BMD are inconsistent, and studies on relation between LDL-C/Apo B ratio and BMD are also lacking. Therefore, this study focused on investigating relation between LDL-C/Apo B ratio and lumbar BMD among young adults through National Health and Nutrition Examination Survey (NHANES). ## Study population The NHANES refers to a research program for assessing adult and child health and nutrient statuses in the USA beginning in the early 1960s. It is a survey of different populations or health topics. The survey is conducted annually in a representative random sample of approximately 5,000 people in the United States. These people are located in counties throughout the United States, and 15 counties are included each year. The database includes dietary data, demographic data, laboratory data (the data was obtained through laboratory testing and included complete blood count with a 5-part differential haematology instrument, cholesterol, triglycerides, oral glucose tolerance test, alkaline phosphatase levels, blood urea nitrogen levels, total calcium levels, etc.), examination data (the data was obtained by instrumental measurements and included blood pressure, overall bone density, lung capacity, muscle strength and grip strength tests, etc.), limited access data and questionnaire data. The results can be applied to determine the incidence of major disease and the associated risk factors. Our study was a cross-sectional study and the inclusion and exclusion criteria were as follows: Inclusion criteria: 1. Subjects in three NHANES cycles from 2011 to 2016 (because both Apo B and lumbar BMD data were collected during the above cycle) and 2. Aged 20–40 years. Exclusion criteria: 1. Patients with lumbar BMD score deficiency; 2. Patients with LDL-C deficiency; 3. Patients with APO B deficiency; 4. Patients with cancer; and 5. Patients taking lipid-lowering drugs. We finally obtained data on 2,027 young adults between the ages of 20 and 40. Our study used open data from NHANES database and included human participants. In compliance with the Declaration of Helsinki, the approval of all NHANES protocols was obtained from the Ethics Review Board of the National Center for Health Statistics. Moreover, all the subjects provided informed consent (NCHS IRB/ERB Protocol Number: 2011–2012: Protocol #2011–17; 2013–2014: Continuation of Protocol #2011–17; 2015–2016: Continuation of Protocol #2011–17). Detailed information is available at https://www.cdc.gov/nchs/nhanes/irba98.htm. ## Variables In the current work, LDL-C/Apo B ratio was selected to be the exposure variable. Lumbar BMD was selected to be the outcome variable. The rest are covariates. ## LDL-C/Apo B ratio measure Serum Apo B levels were detected by a Seimens ProSpec Analyzer. Meanwhile, serum LDL-C levels were determined based on high-density lipoprotein cholesterol (HDL-C), triglyceride (TG) and total cholesterol (TC) levels measured by Friedewald calculation [20]. Both Serum Apo B and LDL-C levels were expressed in mg/dL. Serum TC, TG and HDL-C levels were detected by the Roche/Hitachi Modular P Chemistry Analyzer. ## Lumbar BMD measure Lumbar BMD was evaluated by a qualified radiologist. The Hologic Discovery Model A densitometer (Hologic, Inc., Bedford, Massachusetts) was applied in scanning the outcome variable lumbar BMD, whereas the Apex 3.2 software was adopted for analysis. ## Covariates and confounders Covariates and confounders included age, sex, education, race/ethnicity, waist circumference (WC), income-to-poverty ratio, drinking, smoking, calcium supplementation, physical activity, serum uric acid, serum alkaline phosphatase (ALP), total protein, TC, TG, HDL-C, serum calcium, serum phosphorus, fasting blood glucose (FBG), diastolic blood pressure (DBP) as well as systolic blood pressure (SBP). Detailed data on variables can be found in the NHANES guidelines and manual at http://www.cdc.gov/nchs/nhanes/. Measurements and classifications for all variables are shown in Table S1. ## Statistical analysis The weights of all estimates were determined in the NHANES samples to further reflect sample population features [21, 22]. Weighted chi-square test (χ2) was employed to investigate categorical data, whereas continuous variables were investigated with the use of a weighted linear regression model. Relations of serum LDL-C/Apo B ratio with lumbar BMD in different sex groups were explored using weighted multiple stratified linear regression, while the smooth curve fitting model was utilized for analyzing the nonlinear relation between serum LDL-C/Apo B ratio and lumbar bone mineral density in different sex groups with different ages and races/ethnicities. Concerning the nonlinear relationship, the inflection point was calculated by saturation threshold analysis. Later, the weighted two-piecewise linear regression model was constructed. All data were analyzed using EpowerStats (http://www.empowerstats.com) and R software package (http://www.R-project.org). In addition, the significance level was determined at $P \leq 0.05.$ ## Results Table 1 presents the baseline features of 2027 patients. In relative to serum LDL-C/Apo B ratio quintile 1 group, TG levels gradually decreased, while TC and serum calcium levels gradually elevated with the increase of LDL-C/Apo B ratio. Table 1Weighted characteristics of the participants based on serum LDL-C/Apo B ratio quintilesserum LDL-C/Apo B ratioTotalQ1Q2Q3Q4Q5P valueAge (years)29.82 ± 6.1329.50 ± 6.1329.25 ± 6.1629.48 ± 6.2029.31 ± 6.0830.16 ± 6.100.2048Sex (%)0.8217 Male53.4351.5655.4854.0653.5455.21 Female46.5748.4444.5245.9446.4644.79Race/ethnicity (%)0.0145 Non-Hispanic white35.9651.6258.3053.7260.2459.07 Non-Hispanic black18.7511.8611.8413.5312.3215.40 Mexican American15.1916.1313.3314.4110.187.37 Other race/ethnicity30.0920.3816.5318.3417.2718.16Level of education (%)0.0016 Less than high school17.3721.3915.4915.1512.5010.96 High school21.1622.8920.1220.3620.2319.46 More than high school61.4755.7264.3964.5067.2769.58Income to poverty ratio2.24 ± 1.542.35 ± 1.502.46 ± 1.582.50 ± 1.582.42 ± 1.582.78 ± 1.620.0010Smoking behavior (%)0.0039 Every day16.7718.0618.5419.7714.3912.60 Some days5.625.936.536.654.054.54 Not at all14.0116.4919.5315.2316.6912.57 Not recorded63.5959.5255.4158.3564.8770.29Alcohol consumption (%)0.3376 High alcohol use11.6913.3911.2511.1810.6912.55 None/moderate alcohol use70.3573.3775.3871.5374.1768.81 Not recorded17.9613.2413.3717.2915.1418.64Physical activity (%)0.0216 Sedentary5.774.426.675.606.044.88 Low activity14.8514.7815.7818.1814.1314.62 Moderate activity22.1022.5221.4725.3220.9727.02 High activity3.952.726.435.152.054.30 Not recorded53.3355.5749.6545.7556.8149.18Waist circumference (cm)94.15 ± 16.7298.65 ± 18.6094.65 ± 16.6595.73 ± 17.1293.46 ± 16.3292.47 ± 13.98 < 0.0001Total protein (g/dL)7.22 ± 0.457.14 ± 0.447.14 ± 0.447.25 ± 0.447.19 ± 0.417.17 ± 0.430.0009Total cholesterol (mg/dL)182.47 ± 36.42166.73 ± 32.99176.26 ± 35.27180.58 ± 34.18186.30 ± 33.97195.86 ± 36.99 < 0.0001Triglycerides (mg/dL)107.96 ± 67.89159.98 ± 89.14118.14 ± 63.7798.03 ± 50.7289.39 ± 43.7976.61 ± 35.04 < 0.0001HDL-C (mg/dL)52.47 ± 14.2347.71 ± 15.6950.29 ± 14.0653.04 ± 14.8153.51 ± 11.9758.01 ± 12.80 < 0.0001Fasting blood glucose (mg/dL)93.07 ± 24.3797.20 ± 31.3992.63 ± 23.1592.44 ± 21.5289.79 ± 9.1389.61 ± 14.46 < 0.0001systolic blood pressure115.25 ± 12.72118.77 ± 14.07115.00 ± 12.09115.05 ± 11.52114.30 ± 12.04114.05 ± 10.69 < 0.0001diastolic blood pressure68.31.25 ± 11.3969.75 ± 11.8067.54 ± 11.5468.44 ± 9.9167.55 ± 10.6467.97 ± 10.640.0282Serum uric acid (mg/dL)5.37 ± 1.355.53 ± 1.465.32 ± 1.265.44 ± 1.355.33 ± 1.275.37 ± 1.330.1566Serum total alkaline phosphatase (IU/L)62.55 ± 19.4566.65 ± 31.5562.24 ± 18.5362.14 ± 18.7061.16 ± 17.2160.32 ± 17.600.0004Serum phosphorus (mg/dL)3.74 ± 0.563.73 ± 0.583.76 ± 0.593.74 ± 0.533.72 ± 0.543.78 ± 0.590.5329Serum calcium (mg/dL)9.36 ± 0.319.31 ± 0.319.33 ± 0.289.37 ± 0.329.39 ± 0.319.41 ± 0.31 < 0.0001Calcium supplementation (%)0.0090 Not use86.3389.6483.0184.9185.5281.18 < 0.4 g/d7.946.598.808.5110.369.63 ≥ 0.4 g/d5.723.778.196.584.129.19Lumbar BMD (g/cm2)1.04 ± 0.141.05 ± 0.161.02 ± 0.141.05 ± 0.141.04 ± 0.131.05 ± 0.130.0128Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression modelPercent (%) for categorical variables: the P value was calculated by the weighted chi-square testAbbreviations: Apo B Apolipoprotein B, BMD Bone mineral density, HDL-C High density lipoprotein cholesterol, LDL-C Low density lipoprotein cholesterol Tables 2 and 3 display diverse multiple linear regression model analyses of male and female young adults, respectively. Covariates from Table 1 were adjusted, and the relation between serum LDL-C/Apo B ratio and lumbar BMD varied by sex (males: β = -0.0126, $95\%$ CI -0.0892, 0.0640; females: β = 0.0322, $95\%$ CI -0.0367, 0.1011). Based on age-stratified subgroup analysis, the association also varied by age and sex. Males aged 20–30 years presented a negative trend (β = -0.0570, $95\%$ CI -0.1656, 0.0517), while males with the age of 31–40 years showed a positive trend (β = 0.0810, $95\%$ CI -0.0312, 0.1931). Women showed a positive trend by age (females aged 20–30 years: β = 0.0051, $95\%$ CI -0.0935, 0.1036; females aged 31–40 years: β = 0.0265, $95\%$ CI -0.0767, 0.1296). As shown in race-stratified subgroup analysis, the relations varied by sex and race. Specifically, non-Hispanic black males showed a negative trend (β = -0.0754, $95\%$ CI -0.2695, 0.1188), and males of other ethnicities presented a positive trend. The trend was positive for women of all races (Tables 2 and 3).Table 2The correlation between serum LDL-C/Apo B ratio and lumbar BMD (g/cm2) in males ($$n = 1083$$)Model 1β ($95\%$ CI)Model 2β ($95\%$ CI)Model 3β ($95\%$ CI)LDL-C/Apo B ratio0.0363 (-0.0203, 0.0930)0.0130 (-0.0421, 0.0681)-0.0126 (-0.0892, 0.0640)Stratified by year 20–30 years-0.0208 (-0.1004, 0.0588)-0.0371 (-0.1139, 0.0397)-0.0570 (-0.1656, 0.0517) 31–40 years0.1040 (0.0233, 0.1846)0.0739 (-0.0059, 0.1536)0.0810 (-0.0312, 0.1931)Stratified by race Non-Hispanic White0.0159 (-0.0772, 0.1090)0.0153 (-0.0778, 0.1085)0.0240 (-0.1137, 0.1616) Non-Hispanic Black-0.1232 (-0.2724, 0.0260)-0.1138 (-0.2629, 0.0353)-0.0754 (-0.2695, 0.1188) Mexican American0.0693 (-0.0413, 0.1798)0.0678 (-0.0442, 0.1798)0.0462 (-0.1350, 0.2274) Other race/ethnicity0.0666 (-0.0278, 0.1610)0.0665 (-0.0281, 0.1611)0.0051 (-0.1205, 0.1306)Model 1, no covariates were adjustedModel 2, age, race/ethnicity were adjustedModel 3, age, race/ethnicity, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted. In the subgroup analysis stratified by age and race/ethnicity, the model was not adjusted for age or race/ethnicityTable 3The correlation between serum LDL-C/Apo B ratio and lumbar BMD (g/cm2) in females ($$n = 944$$)Model 1β ($95\%$ CI)Model 2β ($95\%$ CI)Model 3β ($95\%$ CI)LDL-C/Apo B ratio0.0202 (-0.0319, 0.0723)0.0095 (-0.0417, 0.0608)0.0322 (-0.0367, 0.1011)Stratified by year 20–30 years0.0144 (-0.0589, 0.0878)0.0055 (-0.0670, 0.0780)0.0051 (-0.0935, 0.1036) 31–40 years0.0240 (-0.0505, 0.0985)0.0089 (-0.0643, 0.0821)0.0265 (-0.0767, 0.1296)Stratified by race Non-Hispanic White0.0144 (-0.0712, 0.1000)0.0067 (-0.0795, 0.0929)0.0300 (-0.0919, 0.1518) Non-Hispanic Black-0.0775 (-0.2062, 0.0511)-0.0759 (-0.2046, 0.0528)0.0346 (-0.1323, 0.2015) Mexican American-0.0524 (-0.1844, 0.0796)-0.0478 (-0.1782, 0.0826)0.0122 (-0.1933, 0.2177) Other race/ethnicity0.0945 (0.0075, 0.1814)0.0967 (0.0093, 0.1842)0.1065 (-0.0078, 0.2208)Model 1, no covariates were adjustedModel 2, age, race/ethnicity were adjustedModel 3, age, race/ethnicity, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted. In the subgroup analysis stratified by age and race/ethnicity, the model was not adjusted for age or race/ethnicity In addition, smooth curve fitting was conducted with the aim of investigating the potential nonlinear correlation between serum LDL-C/Apo B ratio and lumbar BMD (Figs. 1, 2, 3 and 4). Therefore, there existed a nonlinear connection between serum LDL-C/Apo B ratio and lumbar BMD among males and females aged 20–30 years, non-Hispanic black women, Mexican Americans (regardless of the sex), and women of other races/ethnicities. Among them, men aged 20–30 years, Mexican American men, and non-Hispanic black women showed an inverted U-shaped relationship. Mexican American women displayed a U-shaped relationship. The inflection point was further calculated to be 1.08 males aged 20–30 years, 1.39 for Mexican American males, 1.44 for Mexican American women, and 1.20 for non-Hispanic black women (Table 4).Fig. 1The correlation between serum LDL-C/Apo B ratio and lumbar BMD in males stratified by age. ( Race/ethnicity, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted.)Fig. 2The correlation between serum LDL-C/Apo B ratio and lumbar BMD in females stratified by age. ( Race/ethnicity, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted.)Fig. 3The correlation between serum LDL-C/Apo B ratio and lumbar BMD in males stratified by race/ethnicity. ( Age, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted.)Fig. 4The correlation between serum LDL-C/Apo B ratio and lumbar BMD in females stratified by race/ethnicity. ( Age, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted.)Table 4Threshold effect analysis of serum LDL-C/Apo B ratio on lumbar BMDLumbar bone mineral densityAdjusted β ($95\%$ CI)Male 20-30 years Fitting by standard linear model-0.0570 (-0.1656, 0.0517) Fitting by two-piecewise linear model Inflection point1.08 serum LDL-C/Apo B ratio < 1.080.0096 (-0.2967, 0.3159) serum LDL-C/Apo B ratio > 1.08-0.0697 (-0.1915, 0.0520) Log-likelihood ratio0.638 Mexican American Fitting by standard linear model0.0462 (-0.1350, 0.2274) Fitting by two-piecewise linear model Inflection point1.39 serum LDL-C/Apo B ratio < 1.390.2113 (-0.0101, 0.4327) serum LDL-C/Apo B ratio > 1.39-0.4082 (-0.8121, -0.0044) Log-likelihood ratio0.007Female Non-Hispanic black Fitting by standard linear model0.0346 (-0.1323, 0.2015) Fitting by two-piecewise linear model Inflection point1.20 serum LDL-C/Apo B ratio < 1.200.4646 (0.0779, 0.8514) serum LDL-C/Apo B ratio > 1.20-0.1111 (-0.3140, 0.0917) Log-likelihood ratio0.009 Mexican American Fitting by standard linear model0.0122 (-0.1933, 0.2177) Fitting by two-piecewise linear model Inflection point1.44 serum LDL-C/Apo B ratio < 1.44-0.1290 (-0.3472, 0.0892) serum LDL-C/Apo B ratio > 1.441.7282 (0.6076, 2.8488) Log-likelihood ratio < 0.001Age, race/ethnicity, education, income to poverty ratio, waist circumference, smoking behavior, alcohol consumption, physical activity, calcium supplementation, serum total alkaline phosphatase, serum uric acid, total protein, total cholesterol, triglycerides, HDL cholesterol, serum phosphorus, serum calcium, fasting blood glucose, systolic blood pressure and diastolic blood pressure were adjusted. In the subgroup analysis stratified by age and race/ethnicity, the model was not adjusted for age or race/ethnicity ## Discussion In this study on young adults aged 20–40 years in the United States, it was found that [1] there was a sex difference in the correlation between serum LDL-C/Apo B ratio and lumbar BMD, and [2] the association also varied with age and race/ethnicity. Approximately $80\%$ of cholesterol synthesis occurs in the liver and intestines, with the remaining $20\%$ occurring in bone cells [23]. Lipid rafts play a crucial role in signal transduction during osteoclastogenesis, and cholesterol is an important component of lipid rafts [10]. On the other hand, studies have also confirmed that excessive cholesterol accumulation may increase bone renewal. The outcome can be the promotion of osteoclast formation and the subsequent loss of bone mass [24]. Lipoproteins may also affect osteoclast activity by regulating cholesterol levels. LDL-C has been reported to substantially increase osteoclast activity by inducing cholesterol delivery, whereas LDL-C consumption inhibits osteoclast formation [25]. All the above studies indicate that cholesterol is closely related to bone metabolism, but the underlying mechanism is not completely clear. Hyperlipidemia is usually involved in the occurrence and progression of diseases (or chronic complications) [16]. The LDL-C/Apo B ratio represents the proxy of LDL particle size, with a decreased LDL-C/Apo B ratio suggesting a low LDL-C density within LDL particles and higher risks of infiltration into the artery wall and atherosclerosis [26]. According to numerous studies, LDL-C/Apo B ratio is superior to Apo B alone in the predication of atherosclerosis. A previous study shows that bone microvascular disease may represent an important pathogenic mechanism underlying the declined bone turnover [27]. A histological study reports that in addition to the peripheral vessels, atherosclerotic lesions also develop in the intraosseous arterioles during atherosclerosis [14]. Another study indicates that the bone mineral density of the crus without atherosclerotic plaque is higher than that of the crus with atherosclerotic plaque [15]. All of these studies demonstrate that the LDL-C/Apo B ratio reflects atherosclerosis severity and OP degree. However, conclusions on the connection of serum LDL-C and APO B with BMD are inconsistent, and research regarding the association of serum LDL-C/APO B ratio with BMD is also lacking. According to a cross-sectional study including 481 participants, serum LDL-C may not show association with BMD [28]. In a study of the Old Order Amish population, LDL-C is inversely related to BMD, and it is also observed that the Apo B R3500Q variant predicts lower BMD levels in the whole body, lumbar spine and femoral neck [29]. According to a study conducted more than a decade ago, serum LDL-C shows a positive relation with BMD [30]. Another study indicates a nonlinear relation of serum LDL-C with lumbar BMD among postmenopausal women [31]. Apart from sex, age, and race/ethnicity, differences in genetic risk factors, obesity status, metabolic status, and lifestyle habits (e.g., smoking, alcohol consumption, exercise) may also be a possible explanation for the variability. A study evaluating LDL-C/Apo B ratio within arteriosclerosis demonstrates that cases showing a low LDL-C/Apo B ratio had a higher number of low-density LDL particles, which exhibits an increased TG level [32]. Consistently, our research found that TG content increased within the lower quintile of serum LDL-C/Apo B ratio in comparison with the higher quintiles. A recent study suggests that the reduced LDL-C/Apo B ratio can be related to lower bone turnover among type 2 diabetes mellitus cases independently. However, no relation of LDL-C/Apo B ratio with BMD was observed in this study [33]. Subgroup analyses were conducted based on STROBE guidelines [34] in this study. Therefore, there were differences in relation between serum LDL-C/Apo B ratio and lumbar BMD by age, sex and race/ethnicity. In addition, we found that this relationship in males aged 20–30 years, Mexican American men and non-Hispanic black women showed an inverted U-shaped relationship, and that Mexican American women had a U-shaped relationship. The inflection point was also calculated to be 1.08 for males aged 20–30 years, 1.39 for Mexican American men, 1.44 for Mexican American women, and 1.20 for non-Hispanic black women. These differences might be attributed to age, gender, and racial/ethnic heterogeneity. ## Strengths and limitations First, we perform a subgroup analysis based on the STROBE statement [34]. Furthermore, a weighted, multiracial, typical sample was applied in this study to ensure the high representativeness of our results. A nonlinear relationship was observed by using smooth curve fitting. Nevertheless, certain limitations should be noted. At first, the cross-sectional design was used. Therefore, causality between serum LDL-C/Apo B ratio and lumbar BMD in the young adult population could not be explored. Second, tumor patients and people taking lipid-lowering drugs were excluded from the current work, aiming to eliminate the potentially remarkable impact on serum LDL-C/Apo B ratio and lumbar BMD. Therefore, our study was not representative of these populations. Third, to generalize the findings, our study did not rule out other diseases that might affect bone health. Fourth, some of the covariate information collected through the questionnaire may have a recall bias. Finally, due to some limitations of the NHANES database, we did not include hip/neck femur BMD, T scores or Z scores in our study, so this paper has some limitations. ## Conclusions In summary, there were differences in the connection between serum LDL-C/Apo B ratio and lumbar BMD among cases aged 20–40 years across sex, age, and race/ethnicity. The inflection points in U-shaped relationships were calculated. Moreover, these studies may provide some reference for further revealing the underlying mechanism between serum LDL-C/Apo B ratio and OP. ## Supplementary Information Additional file 1: Supplementary Table S1. Detailed information on variables. ## References 1. Chen W, Simpson JM, March LM, Blyth FM, Bliuc D, Tran T, Nguyen TV, Eisman JA, Center JR. Comorbidities only account for a small proportion of excess mortality after fracture: a record linkage study of individual fracture types. J Bone Miner Res (2018.0) 33 795-802. DOI: 10.1002/jbmr.3374 2. 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--- title: PNPLA3 rs738409 risk genotype decouples TyG index from HOMA2-IR and intrahepatic lipid content authors: - Ákos Nádasdi - Viktor Gál - Tamás Masszi - Anikó Somogyi - Gábor Firneisz journal: Cardiovascular Diabetology year: 2023 pmcid: PMC10031960 doi: 10.1186/s12933-023-01792-w license: CC BY 4.0 --- # PNPLA3 rs738409 risk genotype decouples TyG index from HOMA2-IR and intrahepatic lipid content ## Abstract ### Background Recent reports suggested a different predictive value for TyG index compared to HOMA-IR in coronary artery calcification (CAC) and other atherosclerotic outcomes, despite that both indices are proposed as surrogate markers of insulin resistance. We hypothesized a key role for liver pathology as an explanation and therefore assessed the relationship among the two indices and the intrahepatic lipid content stratified by PNPLA3 rs738409 genotypes as a known non-alcoholic fatty liver disease (NAFLD) genetic risk. ### Methods Thirty-nine women from a prior GDM-genetic study were recalled with PNPLA3 rs738409 CC and GG genotypes for metabolic phenotyping and to assess hepatic triglyceride content (HTGC). 75 g OGTT was performed, fasting lipid, glucose, insulin levels and calculated insulin resistance indices (TyG and HOMA2-IR) were used. HTGC was measured by MR based methods. Mann–Whitney-U, χ2 and for the correlation analysis Spearman rank order tests were applied. ### Results The PNPLA3 rs738409 genotype had a significant effect on the direct correlation between the HOMA2-IR and TyG index: the correlation ($R = 0.52$, $$p \leq 0.0054$$) found in the CC group was completely abolished in those with the GG (NAFLD) risk genotype. In addition, the HOMA2-IR correlated with HTGC in the entire study population ($R = 0.69$, $p \leq 0.0001$) and also separately in both genotypes (CC $R = 0.62$, $$p \leq 0.0006$$, GG: $R = 0.74$, $$p \leq 0.0058$$). In contrast, the correlation between TyG index and HTGC was only significant in rs738409 CC genotype group ($R = 0.42$, $$p \leq 0.0284$$) but not in GG group. A similar pattern was observed in the correlation between TG and HTGC (CC: $R = 0.41$, $$p \leq 0.0335$$), when the components of the TyG index were separately assessed. ### Conclusions PNPLA3 rs738409 risk genotype completely decoupled the direct correlation between two surrogate markers of insulin resistance: TyG and HOMA2-IR confirming our hypothesis. The liver lipid content increased in parallel with the HOMA2-IR independent of genotype, in contrast to the TyG index where the risk genotype abolished the correlation. This phenomenon seems to be related to the nature of hepatic fat accumulation and to the different concepts establishing the two insulin resistance markers. ## Introduction Insulin resistance (IR) refers to a decreased sensitivity of peripheral tissues to insulin and is a hallmark pathophysiologic feature of type 2 diabetes mellitus (T2DM) that clinically develops when the β-cell compensatory mechanisms might no longer overcome the increased insulin need due to the peripheral IR [1–3]. IR is closely related to the sedentary lifestyle, obesity and non-alcoholic fatty liver disease (NAFLD) that are highly prevalent [4, 5]. The most established method to assess the IR is the euglycemic–hyperinsulinemic clamp [6] developed as early as 1979, however its cost and complexity limited its everyday clinical use. There are many IR indices that are easier to assess in the clinical practice out of which HOMA-IR index appears to be the most widely used since its first description in 1985 till date [7, 8]. HOMA-IR was developed as a breakthrough model of the glucose-insulin feedback system in the homeostatic (overnight fasting) state and hepatic insulin resistance under these conditions is the major determinant of HOMA-IR [9, 10]. Nevertheless there are other surrogate markers of IR such as the triglyceride-glucose (TyG) index [11, 12] that are increasingly used due to the clinicians need to meaningfully assess IR without measuring the insulin levels. TyG index was produced in a logarithmized manner from the fasting triglycerides and glucose values and was reported to be a predictor of adverse cardiovascular outcomes in acute coronary syndrome patients with, but also without T2DM [13, 14]. Unsurprisingly both the HOMA-IR [15, 16] and the TyG index [17] are closely related to NAFLD. NAFLD is not only the most common chronic liver disease currently, but also its prevalence is doubled in patients with T2DM [4] and due to the increased IR, decreased insulin clearance [18–20] and the key metabolic functions of the liver the presence of NAFLD is a crucial factor in the speed-up of the metabolic deterioration in T2DM development and worsening of atherogenic dyslipidaemia [5, 21, 22]. Although both the HOMA-IR and TyG indices are proposed as surrogate markers of IR, recently the TyG index was found to be superior compared to the HOMA-IR in predicting coronary artery calcification (CAC) [23]. To the best of our current knowledge, the PNPLA3 rs738409 variant shows the strongest association with NAFLD development [24]. The rs738409 gene variant and particularly the GG genotype is also associated with the progression of NAFLD throughout its entire spectrum [15, 25, 26], including complications occurring in patients with more advanced liver disease [27, 28]. The risk G allele occurs with high enough allele frequency in many of the populations worldwide, including the European (RAF: $23\%$ [29]) and Hungarian subjects (RAF: $22\%$—[30]) to have a potential influence at population level. Therefore, we have assessed the relationship between hepatic fat content, the rs738409 gene variant and the potential genotype effect on the correlation between the HOMA-IR and TyG indeces in a T2DM prone middle aged Hungarian female population. ## Participants and study design We have access to data of over 600 Hungarian female subjects genotyped for 77 gene variants including PNPLA3 rs738409 in a prior study [30]. A genetic based recall study (GBR) design was applied due to that GBR studies allow the assessment of genotype–phenotype associations in studies with substantially smaller samples on the basis of pre-existing genetic data resulting in a substantially higher power compared to same-sized conventional studies [31–33]. Thirty-nine women were recruited with known PNPLA3 rs738409 genotype data: only individuals with CC or GG genotype were eligible for this study-part. A patients flow-chart is indicated on Fig. 1.Fig. 1Flow chart of the genotype-based recall (GBR) study. * reference number [30] The study was conducted according to the Declaration of Helsinki after receiving approval from the relevant institutional bodies (Semmelweis University, Regional and Institutional Committee of Science, the Medical Research Council, Scientific and Research Committee of Hungary, 14486-$\frac{6}{2017}$/EKU). Participants had previously given informed consent for the whole project [30]. ## Exclusion criteria Exclusion criteria from the original case-control study [30] were extended to returning volunteers to the GBR study and included: prediabetes/T2DM diagnosed since delivery, claustrophobia and/or MR-incompatible metal implants, weight over 200 kg, waist/hip circumference ≥ 135 cm/148 cm, taking medication with a known impact on glycemic and lipid traits or HTGC, significant alcohol consumption (> 20 g/day), malignancy, or any other causes of fatty liver as detailed in the new MAFLD definition consensus statement [34]. In addition, patients with other major chronic/acute diseases or ongoing pregnancy/breastfeeding were also excluded. ## Phenotyping Anthropometric data (age, weight, height, BMI) was recorded. 75 g OGTT was performed. Insulin and glucose levels were measured in addition to fasting „routine” laboratory parameters, including HbA1c, lipid profile and liver enzymes. From fasting trigliceride (TG), glucose and insulin levels insulin resistance indices: Trigliceride-Glucose index: TyG = ln[38,67Tg(mg/dl)18glucose(mg/dl)/2], and homeostasis model assessement 2 insulin resistance index (HOMA2-IR) values were calculated [35, 36]. Diabetes mellitus (DM) and prediabetes (impaired fasting glucose (IFG)/impaired glucose tolerance (IGT)) were diagnosed according to the WHO, 2016 American Diabetes Association (ADA), and Hungarian guidelines [37–39] based on the 75 g OGTT and/or HbA1c results. ## HTGC measurement All MR imaging (MRI) sessions were acquired on a clinical 3 T MRI system (Prisma, Siemens Healthineers, Erlangen, Germany) with the subject in a supine position. For MRI protocol, a standard body-array of 18 channel flexible coils was positioned on the liver region and combined with a spine array coil located below the subject. Imaging proton density fat-fraction (PDFF), unenhanced axial images were obtained by using a low–flip-angle, six-echo two-dimensional spoiled gradient-recalled-echo sequence with all array coil elements (TE = 2, 4.1, 6.2, 8.2, 10.2, and 12.3 ms). The repetition time and flip angle were chosen to avoid T1 weighting: TR: 15 ms, Flip-angle: 11°, FOV = 240 × 400 mm in plane, matrix = 240 × 130, slice thickness = 3.5 mm, space between slices 4.3 mm. Phase and magnitude images were systematically saved. For MR imaging using custom-made MATLAB routines, multi-section liver PDFF maps were generated offline from the source images via joint estimation of water and fat images and field maps [40]. This method uses a complex signal model similar to advanced multipoint DIXON/IDEAL algorithms (incorporating a multipeak/multifrequency fat model and T2 decay) analyzing six-echo FLASH complex images. Voxels representing liver tissue were delineated with a freehand ROI defining tool by an expert radiologist on multi-section PDFF maps excluding vessels, liver and edges, and artifacts. In each subject the mean value of the selected voxels was calculated, representing the average liver fat fraction. Fatty liver was diagnosed if the liver fat fraction exceeded $5.5\%$ [15]. ## Statistics Due to the limited sample size and the non-normal distribution of the majority of variables we used non-parametric tests. Data are expressed in median and 25th-75th percentiles. For evaluation of the association between two parameters Spearman’s correlation test and for comparing two (genotype) groups Mann–Whitney U/χ2 tests were applied. TIBCO Statistica (version 13.4.0.14, TIBCO Software Inc.) software was used. ## Study population clinical characteristics Age and BMI values of study participants were 37.0 (34–40) years and 26.2 (22.8–32.5) kg/m2, respectively. We diagnosed thirteen individuals with prediabetes and one with overt T2DM. No patient had established cardiovascular disease at enrolment. No significant difference of anthropometric data, glycaemic or lipid values was detected between genotype groups, except of the HTGC values (and NAFLD prevalence). The characteristic of the study population is indicated in Table 1.Table 1Population characteristicPNPLA3 rs738409CC ($$n = 27$$)GG ($$n = 12$$)Age (years)38.0 (36.0–40.0)35.0 (34.0–38.0)BMI (kg/m2)25.6 (21.9–32.0)30.5 (24.8–32.8)Overweight + obesity15 ($55\%$)9 ($75\%$)Waist to hip ratio0.89 (0.80–0.92)0.92 (0.87–0.96)GDM history16 ($59\%$)6 ($50\%$)HbA1c %5.5 (5.3–5.6)5.4 (5.2–5.6)Prediabetesa + T2DM10 ($37\%$)4 ($33\%$)HTGC (%)b5.5 (2.4–5.9)11.4 (3.8–19.1)NAFLD7 ($26\%$)+7 ($58\%$)+Fasting glucose (mmol/l)5.2 (4.9–5.3)5.1 (5.0–5.5)Fasting insulin (μU/ml)10.2 (7.2–15.9)14.0 (9.7–19.6)Fasting TG (mmol/l)1.0 (0.8–1.3)1.1 (0.8–1.5)HOMA2-IR1.3 (1.0–2.0)1.8 (1.3–2.5)TyG index7.4 (7.3–7.8)7.6 (7.3–7.8)Medians (25th-75th percentile) or Number of patients (%) are indicatedaDiagnosed by using HbA1c and/or glucose values from OGTT (IFG, IGT)bSix women had an indeterminate (− 1.455–0.676) NFS value, of which 4 patients had prediabetes/T2DM and none had pathologic FIB-4 score (> 1.45) or routine abdominal MRI scans indicating advanced liver fibrosis/cirrhosisGDM: gestational diabetes mellitus, T2DM: type 2 diabetes mellitus, HTGC: hepatic triacylglycerol content, NAFLD: non-alcoholic fatty liver disease, NFS: NAFLD fibrosis score, FIB-4 score: fibrosis-4 score$$p \leq 0.01$$,+$$p \leq 0.05$$ ## Correlation between TyG and HOMA2-IR indices stratified by PNPLA3 rs738409 genotype Between the two insulin resistance indices we found an association ($R = 0.42$, $$p \leq 0.0078$$), however a significant correlation was only detectable in individuals with the rs738409 CC genotype ($R = 0.52$, $$p \leq 0.0054$$) but not with GG (r = − 0.16, $$p \leq 0.62$$). The scatterplot is indicated in Fig. 2.Fig. 2Correlation between HOMA2-IR and TyG indices stratified by PNPLA3 rs738409 genotypes. Spearman rank order test results indicated in the figure ## Genotype effect on correlations among insulin resistance indices, their components and HTGC HTGC values significantly correlated with both markers of insulin resistance: (TyG $R = 0.36$, $$p \leq 0.0252$$, HOMA2-IR: $R = 0.69$, $p \leq 0.0001$), and their components (fasting glucose: $R = 0.51$, $$p \leq 0.0008$$, fasting insulin: $R = 0.66$, $p \leq 0.0001$, fasting TG: $R = 0.32$, $$p \leq 0.0445$$). After genotype stratification In contrast to HOMA2-IR and its components, where the correlations were nearly parallel in the two genotype groups after genotype stratification, the correlations of TyG index and fasting TG were only significant in the rs738409 CC genotype group indicating different associations between the parameters according to the genotypes to HOMA2-IR and its components, where the correlations were nearly parallel in the two genotype groups, the correlations of TyG index and fasting TG were only significant in the rs738409 CC genotype group indicating different associations between the parameters according to the genotypes. Similarly, the correlation between HOMA2-IR and fasting TG was disrupted in individuals with GG genotype. Correlations between HTGC and TyG, HTGC and HOMA2-IR, HTGC and TG, HOMA2-IR and TG are indicated in Fig. 3A, B, C, D, respectively. Fig. 3Correlations among HTGC, insulin resistance indices and TG values stratified by PNPLA3 rs738409 genotypes (CC vs GG): A: between HTGC and TyG index, B: between HTGC and HOMA2-IR, C: between HTGC and TG, D: between HOMA2-IR and TG. Spearman rank order test results indicated in the figure. HTGC: hepatic triacylglycerol content, TG: fasting serum triglyceride ## Discussion We assessed intrahepatic lipid content measured by MR method and two indices proposed as surrogate markers of insulin resistance indices (TyG and HOMA2-IR) in 39 middle-aged women prone to T2DM development. Genotype based recall study design was applied on the basis of PNPLA3 NAFLD risk genotype to decrease the number of patients needed to be enrolled [31, 33]. Therefore, only patients with rs738409 homozygous genotypes (GG vs CC) were included. Despite the two IR indices were suggested to have a linear relationship [17, 41], we found compelling difference in the relationship between TyG and HOMA2-IR when the data were stratified by the PNPLA3 genotypes. The profound genetic effect on the correlations between TyG, TG and HTGC and also between TG and HOMA2-IR was observed. Insulin resistance and hyperinsulinemia are classified as major factors contributing to pathology of T2DM development and its cardiovascular complications. Many concepts and methods were developed to measure the “insulin resistance” (IR), however no single parameter could meet the need of a precise pathophysiological assessment combined with an easy use for the everyday clinics. Out of the many IR indices HOMA-IR index appears to be the most widely used in the clinical practice, including the recent recommendation to establish the diagnosis of metabolic (dysfunction) associated fatty liver disease (MAFLD) [34]. The HOMA-IR and the further developed HOMA2-IR are proposed to be mainly associated with hepatic insulin resistance under fasting steady-state conditions [8, 36, 42]. Despite HOMA-IR is easily calculated from only two parameters there is a need from clinicians to find alternative surrogate markers of IR which do not require the measurement of fasting insulin levels such as the triglyceride-glucose (TyG) index [12]. Two markers of insulin resistance, the HOMA-IR and TyG were compared in a recent study on the prognostic value of T2DM development and atherosclerosis and the authors concluded that TyG index performed better in both endpoints [43]. Nevertheless there is still no clear-cut opinion in the literature regarding the predictive powers of the TyG and HOMA-IR index in CVD, and studies suggested only a moderate coronary artery disease (CAD) predictive power for TyG, although its inclusion into predictive models of MACEs could improve the predictive accuracy in patients with ACS [44]. Due to that hepatic insulin resistance is significantly increased in NAFLD that is the most prevalent liver disease and strongly related to obesity, T2DM, dyslipidaemia and metabolic syndrome, the pathophysiology of the liver lipid accumulation could be a key factor in (hepatic) IR associated to the common metabolic diseases. Therefore we assessed the effect of PNPLA3 rs738409 gene variant on the correlation between TyG and HOMA2-IR. This* gene variant was identified in fatty liver genome-wide association studies (GWAS) [24] with pathogenic role throughout the entire NAFLD spectrum [25, 27, 28]. We astonishingly found that TyG and HOMA2-IR indices were completely dissociated in individuals with the rs738409 GG risk genotype (M148M) in contrast to those with the CC (I148I) genotype where the strong positive correlation was conserved. This finding should mean that despite both the TyG and the HOMA2-IR are considered as surrogate markers of insulin resistance they display fundamentally different characteristics according to the genetic background of the liver lipid accumulation confirming the hypothesis of the PNPLA3 rs738409 genotype effect. Although prior studies with human liver tissue samples reported that the hepatic content of diacylglycerol (DAG) species (FA18:1, implicated in diminished insulin signaling) remained unaltered in rs738409 homozygous and heterozygous G allele carriers (M148M and I148M) and it was hypothetically suggested that the G risk allele carriers might be protected from insulin resistance [45] we report here that the HOMA2-IR values remained correlated with HTGC. In addition, our observation is consistent is consistent with prior results where HOMA-IR/HOMA2-IR and HTGC values were directly measured and reported to be correlated in subjects with PNPLA3 risk genotypes [16]. In contrast to the HOMA2-IR only the TyG index displayed the theoretically expected characteristic in correlation with HTGC in individuals homozygous for the PNPLA3 NAFLD risk genotype based on the reported hepatic DAG species content critical in disturbing the insulin signal [45]. We observed that the correlation between TyG and HTGC was diminished in those with the rs738409 GG risk genotype. The explanation could be attributed to that the input parameters and their PNPLA3 risk genotype associated changes are largely different. The insulin concentrations were increasing in parallel with HTGC, in contrast to the glucose levels that were not different. Consistently, the fasting insulin levels and the HOMA-IR indices were significantly higher and the glucose levels were unchanged in a severely obese large European cohort in the rs738409 GG genotype group [46] that was presumably presented with higher HTGC values as well. Opposingly, the serum TG levels in the morbid obese patients with the GG genotype was found to be lower compared to CC genotype group [46] and confirmed subsequently [47, 48]. The lower circulating TG levels in the PNPLA3 risk genotype group could be due to the lower hepatic TG efflux in the fasting state associated to the genetic effect on intrahepatocellular lipolysis [46, 48, 49]. Based on that the hepatic VLDL output occurs in parallel with the TG efflux in the fasting state [50] these observations might well explain that the TyG index could be an useful prognostic marker for CAD, especially in high CVD risk patients with T2DM [43, 44, 51–53]. Furthermore, in a recent study with over 4000 participants undergoing cardiac CT the TyG index was found to be superior compared to the HOMA-IR in predicting CAC [23]. It could be outlined that the rs738409 G allele frequencies in the East Asian populations are substantially higher compared to populations with European or African origin [29] which is consistent with the results of the latter East Asian study [23] and could contribute to the difference found between TyG index and HOMA-IR in predicting CAC. The current state of the art is that HOMA-IR primarily reflects hepatic insulin resistance [10]. The increase of HOMA2-IR occurred in parallel with the increase of HTGC in those with the PNPLA3 rs738409 GG risk genotype and partially behaves like a biomarker of intrahepatic steatosis that is dissociated from the other surrogate marker of IR, the TyG index composed of only metabolic parameters but does not directly include serum insulin level. As a consequence, it may also be raised, that HOMA-IR could be significantly dissociated from the TyG index in those populations where the rs738409 G NAFLD risk allele is the major PNPLA3 allele [29, 54]. 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--- title: Multi-omics analysis identifies drivers of protein phosphorylation authors: - Tian Zhang - Gregory R. Keele - Isabela Gerdes Gyuricza - Matthew Vincent - Catherine Brunton - Timothy A. Bell - Pablo Hock - Ginger D. Shaw - Steven C. Munger - Fernando Pardo-Manuel de Villena - Martin T. Ferris - Joao A. Paulo - Steven P. Gygi - Gary A. Churchill journal: Genome Biology year: 2023 pmcid: PMC10031968 doi: 10.1186/s13059-023-02892-2 license: CC BY 4.0 --- # Multi-omics analysis identifies drivers of protein phosphorylation ## Abstract ### Background Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated. ### Results We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes. ### Conclusions Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13059-023-02892-2. ## Background Protein phosphorylation is a reversible post-translational modification (PTM) and one of the most common mechanisms for regulating protein activity and transmitting signals in cell biology [1–3]. Phosphorylation occurs at specific sites within a protein where kinases and phosphatases add and remove phosphate moieties [4]. The level of activity of kinases and phosphatases is determined by their abundance [5, 6], intracellular and extracellular stimuli [7–10], interaction with co-factors [11], and PTMs including phosphorylation [12–14]. Therefore, the phosphorylation level of a given site within a protein depends on multiple factors, any of which could be influenced by genetic variation [15, 16]. Genetic variants that affect quantitative phenotypes can be identified through quantitative trait locus (QTL) mapping in humans and in model organisms. In addition to clinical phenotypes, QTL mapping can be applied to molecular traits such as gene expression [17–22], chromatin accessibility [23], and protein abundance [24, 25]. QTL mapping of transcripts (eQTL) and proteins (pQTL) has revealed how genetic variants can alter the regulatory flow from encoded gene through transcription and translation [20, 26, 27]. However, only limited research has been conducted on how genetic variation influences protein phosphorylation or other PTMs [18, 28, 29]. Genetically diverse model organism populations increase the scope and power of QTL mapping. The Collaborative Cross (CC) [30, 31] is a panel of recombinant inbred mouse strains descended from eight founder inbred strains: A/J (AJ), C57BL/6J (B6), 129S1/SvImJ [129], NOD/ShiLtJ (NOD), NZO/HlLtJ (NZO), CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB). The founder strains represent traditional laboratory as well as wild-derived strains, encompassing three subspecies of the house mouse [32, 33] and harbor ~50 million known genetic variants [34]. The current CC panel consists of more than 60 strains that are homozygous at most loci (> $99\%$). The ability to use replicate animals of CC strains is an important feature of CC studies that improves QTL mapping power [23, 35] and enables studies of response to interventions [36–38] and other applications [39]. We previously reported on the genetic regulation of protein abundance in liver of CC strains [25]. Here we expand on our earlier investigation to examine how genetic variation regulates protein phosphorylation. We used mass spectrometry analysis to quantify the proteome and phosphoproteome across three tissues (heart, kidney, and liver) from 116 mice representing female/male pairs from 58 CC strains. We performed QTL mapping to obtain pQTL and phosphorylation QTL (phQTL). In addition, we mapped the residuals of phosphopeptide abundance after regression on the abundance of the protein they derived from, i.e., the parent protein abundance, to obtain adjusted phosphopeptide QTL (adj-phQTL). This approach allowed us to differentiate between the contributions of two distinct mechanisms that determine the abundance of phosphopeptides, the abundance of its parent protein, and the proportion of target sites that are phosphorylated, with the latter likely reflecting the activity of a catalyst intermediate. We then applied mediation analysis and identified candidate genes that influence phosphorylation levels through the second mechanism. ## Quantitative phosphoproteome profiling of heart, kidney, and liver in CC mice Heart, kidney, and liver tissue samples were collected from 116 mice representing one male and one female from each of 58 CC strains (Additional file 2: Table S1). We utilized a tandem mass tag (TMT)-based proteomics workflow (Fig. 1A) to quantify total protein abundance and the abundance of phosphorylated peptides (phosphopeptides). We quantified 6172, 7286, and 6558 proteins, and 4975, 4236, and 4246 non-polymorphic phosphopeptides in heart, kidney, and liver tissue, respectively. The number of proteins reported for liver differs slightly from our previous study, where we report 6798 proteins, due to differences in the preprocessing and filtering steps. Nearly 5000 proteins were quantified in all three tissues and ~6500 proteins were quantified in at least two tissues (Fig. 1B) Fewer phosphopeptides were quantified across multiple tissues; ~1500 were observed in all three tissues, but the majority of phosphopeptides were observed in only one tissue (Fig. 1C). The number of phosphorylation sites identified for a given protein ranged from 1 to 148 (TTN in heart), with fewer than 10 sites detected for most proteins (median = 1) (Additional file 1: Fig. S1A). The abundance of most phosphopeptides was correlated with the abundance of their parent proteins (median correlation: heart = 0.32, kidney = 0.36, liver = 0.40) (Additional file 1: Fig. S1B). To obtain an estimate of phosphorylation that is independent of the parent protein abundance, we computed the residual of phosphopeptide abundance after regression on the abundance of the parent protein (adjusted phosphopeptides abundance) (Fig. 1D). For this purpose, we modified the protein abundance estimation by excluding all peptides corresponding to detected phosphopeptides (Methods). In some cases, we were not able to quantify the parent protein after removing phosphorylated peptides, and we obtained 3875, 3471, and 3492 adjusted phosphopeptides in heart, kidney, and liver tissue, respectively. Fig. 1Overview of the proteome and phosphoproteome profiling of three tissues from Collaborative Cross strains using Tandem mass tags (TMT). A Liver, kidney, and heart samples were collected from one male and one female mouse from 58 Collaborative Cross (CC) inbred strains. Samples [116] were multiplexed utilizing TMT sample multiplexing reagents. Proteome and phosphoproteome analyses were collected by mass spectrometry. B Venn diagrams of the quantified proteins, C phosphopeptides, and D adjusted phosphopeptides in liver, kidney, and heart tissues ## Sex differences among phosphopeptides We estimated the effect of sex on abundance of proteins, phosphopeptides, and adjusted phosphopeptides in all three tissues (Methods). For heart, we detected significant sex effects (FDR < 0.01) for 323 proteins, 12 phosphopeptides, and 0 adjusted phosphopetides; for kidney, 4499 proteins, 2031 phosphopeptides, and 538 adjusted phosphopetides; and for liver, 2367 proteins, 547 phosphopeptides, and 97 adjusted phosphopetides (Additional file 3: Table S2). Sex effects are most prevalent in kidney, followed by liver, and there are relatively few in heart (Fig. 2A). Standardized sex effects on phosphopeptides and their parent proteins are highly correlated (Additional file 1: Fig. S2A). After adjustment for parent protein abundance, the magnitude of the sex effects is reduced (Fig. 2A), but many remain significant. In addition, we see strong positive correlation of sex effects on phosphopeptides before and after adjustment (Additional file 1: Fig. S2B). Thus, sex effects on phosphopeptide abundance are determined by sex effects on parent protein abundance and by sex-specific factors that act directly on phosphorylation levels. Fig. 2Sex effect and heritability on protein and phosphopepitdes across three tissues. A Histograms of standardized sex effect (difference/SE) on protein abundance (upper), phosphopeptides (middle), and adjusted phosphopeptides (lower) in heart, kidney, and liver tissues. B Sex difference in the relative abundance (batch corrected log2 intensity) of phosphopeptide harboring LDHD pS23 is due to sex effect on its parent protein. C Sex difference in the relative abundance of phosphopeptide harboring CGREF pS272 is not due to sex effect on its parent protein. D Histograms of heritability on protein abundance (upper), phosphopeptides (middle), and adjusted phosphopeptides (lower) in heart, kidney, and liver tissues. Dashed vertical lines represent the median We illustrate how sex can influence phosphopeptide abundance with two examples (Fig. 2B). There is a significant effect of sex on the protein LDHD, which has a higher abundance in males. The phosphopeptide LDHD pS23 also has higher abundance in males, but the adjusted phosphopeptide abundance shows no significant difference between the sexes. We conclude that the sex effect on LDHD pS23 is mediated through the sex effect on the abundance of its parent protein (Fig. 2B). The protein CGREF1 has higher abundance in males but CGREF1 pS272 has substantially lower abundance in males. This sex difference in the phosphopeptide persists after adjusting for the parent protein abundance. We conclude that the sex effects on CGREF1 pS272 are mediated by sex-specific processes that act independently of the parent protein abundance (Fig. 2C). ## Heritability of phosphopeptides Heritability is the proportion of phenotypic variation explained by genetic relatedness. It reflects the additive genetic effects on a trait relative to the precision of measurement. We estimated heritability (h2) for the abundance of individual proteins and phosphopeptides in all three tissues. The median heritability across tissues ranged from 0.308 to 0.332 for proteins, from 0.138 to 0.165 for phosphopeptides, and from 0.076 to 0.100 for adjusted phosphopeptides (Fig. 2D; Additional file 4: Table S3). Protein heritability was substantially higher than phosphopeptide heritability (Additional file 1: Fig. S2C), which at least in part, reflects the higher precision of protein quantification that combines measurements across multiple peptides. The adjusted phosphopeptides are generally less heritable than the phosphopeptides (Additional file 1: Fig. S2D), indicating that a dominant component of phosphopeptide heritability is mediated through genetic effects on the parent protein. Nonetheless, there are many adjusted phosphopeptides with non-zero heritability, indicating that genetic factors can directly influence phosphorylation levels. ## Genetic mapping of proteins and phosphopeptides We mapped pQTL, phQTL, and adj_phQTL in all three tissues. We computed a genome-wide adjusted p-value for each trait and then applied a false discovery rate adjustment (FDR < 0.1) to account for the number of proteins or peptides (Methods). We identified 1608, 1801, and 1609 pQTL (Fig. 3A); 211, 251, and 275 phQTL (Fig. 3B); and 40, 58, and 41 adj-phQTL (Fig. 3B) in heart, kidney, and liver tissue, respectively (Additional file 5: Table S4). We defined local QTL as being located within 10 Mbp of the midpoint of the protein-coding gene, all others are distant QTL. Mapping resolution of the CC panel is not uniform across the genome and we noted several instances where QTL classified as distant were clearly local, based on the local LD structure. We see greater sharing across tissues for local pQTL ($41\%$ are present in at least two tissues) compared to distant pQTL ($11\%$ are present in at least two tissues) (Additional file 1: Fig. S3A). This is consistent with previous studies on multi-tissue gene expression QTL (eQTL) [19, 40]. The proportion of phQTL shared across tissues is lower, with only five local and one distant phQTL found in all three tissues and $10.5\%$ of all phQTL present across two or more tissues (Additional file 1: Fig. S3B). The sharing of adj-phQTL is lower still, with only one local (EIF3B pS90; Figs. 3C and S3C) and one distant (ATP5A1 pS53) site found across all three tissues and only $10.5\%$ of all adj-phQTL present in two or more tissues. The majority of adj-phQTL have a corresponding phQTL ($81.8\%$ of all adj-phQTL) (Figs. 3B and S3D). The lower proportion of sharing across tissues for phQTL and adj-phQTL could be due to tissue specificity of phosphorylation, but we cannot rule out reduced mapping power for phosphopeptides relative to proteins. Fig. 3pQTL and phQTL mapping from CC strains in heart, kidney, and liver tissues. Stringently detected (FDR < 0.1) A pQTL, B phQTL, and adjusted phQTL in heart (left), liver (middle), and kidney (right) tissues. QTL are plotted by the genomic positions of proteins against QTL coordinates. Adjusted phQTL were highlighted in black. C Adjusted phQTL identified on EIF3B pS90 co-mapped in all three tissues. Relative abundances (batch corrected log2 intensity) of EIF3B pS90 in each tissue were grouped based on founder local haplotypes. D LOD scores of local and distant phQTL (FDR < 0.1 or 0.5) changed after adjusting for their parent protein abundances in heart, kidney, and liver tissues. MCAT pS41, GAS2 pS283, and COMT pS261 were labelled To determine how much of the genetic contribution to phQTL is mediated through abundance of their parent proteins, we first looked at the correlation of allele effects at concordant pQTL-phQTL pairs. We observed high positive correlations for most pairs, consistent with shared genetic effects (Additional file 1: Fig. S3E). We then calculated the difference of the LOD score for each phQTL before and after adjusting for parent protein abundance. If the LOD score drops after adjustment, this indicates that the phQTL is at least partially mediated through variation in the abundance of the parent protein. The phQTL with the greatest reduction in LOD score (Delta LOD percentage < − $50\%$, FDR < 0.1) were primarily local phQTL (89.5–$95.7\%$ across tissues), although a few distant phQTL ($$n = 21$$) showed a similar reduction in LOD score (Fig. 3D, Additional file 6: Table S5). We looked at a larger set of phQTL using a less stringent multiple testing correction (FDR < 0.5) and saw the same pattern. We conclude that local genetic effects on phosphopeptide abundance are often mediated through parent protein abundance. However, a substantial number of phQTL, especially those that are distant from the coding gene, show little or no drop in LOD score after adjustment, indicating that these phQTL are responding to genetics effects independent of their parent protein abundance. The drop in LOD scores for many phQTL falls somewhere between these extremes, indicating that they are influenced by parent protein abundance and by independent mechanisms. We note that the genetic effects on phosphopeptides can be modified by sex. Our experimental design, with one male and one female mouse from each CC strain, is well suited for mapping QTL with genetic effects that differ between the sexes, which we refer to as sex-interactive QTL. We mapped 2, 43, and 5 sex-interactive pQTL (FDR < 0.1) in heart, kidney, and liver, respectively (Additional file 1: Fig. S3F). We identified 4 sex-interactive phQTL in kidney (3 local and one distant). We found no sex-interactive phQTL in heart or liver and no sex-interactive adj-phQTL in any tissue. The local sex-interactive phQTL for HAO2 pS171 illustrates how sex and genetic variation can simultaneously affect protein and phosphoprotein abundance (Additional file 1: Fig. S3G). Female mice generally have higher phosphorylation of HAO2 pS171 relative to their male counterparts, but the magnitude of the sex effect is amplified for mice with the CAST allele at this QTL. ## Distant phQTL effects are mediated through kinases, phosphatases, and cytokines Phosphopeptide abundance can be driven by abundance of the parent protein (Mechanism 1), and by factors that affect phosphorylation levels independently of protein abundance (Mechanism 2; Fig. 4A). We set out to quantify the relative contributions of these two mechanisms and to identify candidate mediators of Mechanism 2, which we expected to be enriched for kinases, phosphatases, and upstream regulators of protein phosphorylation. Fig. 4Phosphopeptide abundance can be regulated by substrate abundance dependent or non-substrate abundance dependent mechanisms. A Diagram showing how the genetic effect resulting in phQTL detection may be regulated by either parent protein abundance (batch corrected log2 intensity) changes (Mechanism 1) or by phosphorylation stoichiometry (Mechanism 2) or both. B Genome scans for GAS2 and GAS2 pS283 in kidney tissue. C Path diagram of GAS2 pS283 abundance regulation in kidney tissue. D The PWK allele of the GAS2 pS283 phQTL drove low phosphopeptide abundance in kidney tissue. Data were categorized based on the founder haplotye at the identified pQTL. E Abundances of overall GAS2 and GAS2 pS283 were highly correlated ($r = 0.99$). Points are colored based on founder haplotype at Gas2. F Overall abundance of GAS2 and adjusted abundance (residual from regression of batch corrected log2 intensity) of GAS2 pS283 were not correlated ($r = 2.4$e−17). Points are colored based on founder haplotype at Gas2. G Abundance of GAS2 pS283 and adjusted abundance of GAS2 pS283 were not correlated ($r = 0.02$). Points are colored based on founder haplotype at Gas2. H Genome scans for MCAT and MCAT pS41 in heart tissue. I NZO alleles at Pkd1 drove the low abundances of MCAT pS41 in heart tissue. Colors denote the founder haplotype of additive allele effects at the identified pQTL of MCAT pS41. J Mediation analysis identified PDK1 expression as the mediator of MCAT pS41 abundances. Each gray dot is a mediation score representing the MCAT pQTL LOD score conditioned on a protein as candidate mediator. K Path diagram of MCAT pS41 abundance regulation in heart tissue. L NZO alleles at Pkd1 drove the low abundances of PDK1 in heart tissue. Colors denote the founder haplotype of additive allele effects at the identified pQTL of MCAT pS41. M The adjusted abundances of MCAT pS41 and PDK1 were highly correlated ($r = 0.86$) in heart tissue. N Mediation analysis identified PDK1 as the mediator of several phQTL in heart, kidney, and liver tissue, respectively We observed that many phQTL have a corresponding pQTL but no corresponding adj-phQTL, i.e., the LOD score drops when the phosphopeptide is adjusted for the parent protein abundance (Fig. 3D). The genetic effects at these phQTL are mediated by Mechanism 1. For example, a pQTL on chromosome 7 at 64Mb explains $81\%$ of variation in the abundance of the protein GAS2 in kidney ($$p \leq 8.2$$e−16) (Fig. 4B,C). The abundance of GAS2 is low in animals with the PWK allele at this locus (Fig. 4D). The abundance of GAS2 pS283 is highly correlated with its parent protein’s abundance ($r = 0.99$) (Fig. 4E). After adjusting for GAS2 abundance, GAS2 pS283 is no longer associated with the genotype at the phQTL locus (Fig. 4G). Additional examples of phQTL that are mediated through parent proteins include TPMT pS34 (Additional file 1: Fig. S4A-D) and MTX3 pS284 (Additional file 1: Fig. S4E-H). Common features of phQTL consistent with Mechanism 1 are a strong local pQTL for the parent protein and strong correlation between the parent protein and the phosphopeptide. We observed 74 distant phQTL that had no corresponding pQTL, and after adjusting for parent protein abundance, the adj-phQTL remained significant (Fig. 3D). The genetic effects at these QTL are mediated primarily by Mechanism 2. For example, MCAT pS41 in heart (Additional file 1: Fig. S5) has distant phQTL and adj-phQTL on chromosome 2 at 71.8Mb (Fig. 4H). The abundance of MCAT pS41 is low when this Chr 2 locus carries an NZO allele (Fig. 4I). To identify the gene candidates responsible for this effect, we applied mediation analysis to evaluate the transcripts and proteins in the phQTL region on Chr 2 (Methods). The strongest mediation signature was found for PDK1, pyruvate dehydrogenase kinase 1 (Fig. 4J,K). The transcript abundance of Pdk1 was also identified as a mediator. PDK1 has a local pQTL with low expression in mice with an NZO allele (Fig. 4L), and PDK1 abundance is tightly correlated with MCAT pS41 (Fig. 4M). The pQTL of PDK1 explains $97\%$ ($p \leq 2.2$e−16) of the variation in PDK1 abundance and $77\%$ ($p \leq 2.2$e−16) of variation in MCAT pS41 abundance. The effect of the pQTL on MCAT was not significant, and the effect of MCAT abundance on MCAT pS41 was significant but weak (Fig. 4K), confirming that the phQTL on MCAT pS41 is primarily driven by PDK1 abundance. Across all three tissues, we found a total of 9 distant phQTL (on 6 different proteins) that map to the Pdk1 locus on Chr 2 and are mediated by PDK1, including the confirmed substrate of PDK1, pyruvate dehydrogenase E1 component subunit alpha, PDHA1 [41] (Fig. 4N). The phQTL at Chr 2 for ATP5A1 pS53 is found in all three tissues, and the phQTL for PCCA pS248 is found in heart and liver tissues. These results indicate that PDK1 is the upstream kinase of these phosphorylation sites. We found 45 examples of phosphopeptides whose abundance is influenced by both mechanisms 1 and 2 to different degrees and with genetic associations that are local, distant, or both. For example, the protein COMT in liver has a local pQTL on Chr 16 at 18Mb, and COMT pS261 has a distant adj-phQTL on Chr 13 at 54.8Mb (Fig. 5A). The local pQTL drives higher expression of COMT in the presence of a CAST allele (Fig. 5B). After adjusting for COMT abundance, the adjusted phosphopeptide shows high abundance in the presence of a WSB allele at the distant phQTL (Fig. 5C). Mediation analysis of the Chr 13 QTL identified the transcript of Cdc14b as a candidate mediator of phosphorylation (Fig. 5D). We note that CDC14B was not quantified in the proteomics analysis. The distant adj-phQTL for COMT pS261 co-maps with a local eQTL for Cdc14b on Chr 16 and exhibit mirrored allele effects, i.e., the WSB allele confers low expression of Cdc14b but high abundance of COMT pS261, resulting in negative correlation between COMT pS261 and Cdc14b mRNA abundance. Regressing out the effect of COMT protein abundance on the abundance of the COMT pS261 phosphopeptide improves this correlation between Cdc14b mRNA and COMT pS261, which confirms that abundance of COMT pS261 phosphopeptide is regulated by both its parent protein abundance and the transcript abundance of Cdc14b (Fig. 5E-G). The Chr 13 QTL explains $63\%$ ($$p \leq 5.2$$e−9) of variation in COMT, and in turn, COMT explains $48\%$ ($$p \leq 2.6$$e−9) of variation in COMT pS261. The QTL on Chr 13 explains $51\%$ ($$p \leq 4.2$$e−6) of variation in Cdc14b, which in turn explains $38\%$ ($$p \leq 2.8$$e−7) of variation in COMT pS261.Fig. 5Phosphopeptide abundance can be regulated by both substrate abundance dependent and non-substrate abundance dependent mechanisms. A COMT pQTL and phQTL for COMT were mapped to different loci in liver tissue. B A local CAST allele at Comt drove high abundance of COMT in liver tissue. C Adjusted abundance of COMT pS261 categorized according to founder haplotype at Cdc14b. D Mediation analysis using transcriptomics data identified Cdc14b as the mediator of a phQTL for COMT pS261. Each gray dot is a mediation score representing the COMT pS261 phQTL LOD score conditioned on a transcript as candidate mediator. E Abundance of Cdc14b transcripts pS261 categorized according to founder haplotype at Cdc14b. The abundance of COMT pS261 is less correlated with Cdc14b transcripts before adjustment (r = −0.49) (F) compared to after adjustment (G) (r = −0.62). H Path diagram of COMT pS261 abundance regulation in liver tissue A second example of complex regulation, LMNA pS394, was also found to be mediated by Cdc14b in heart (Additional file 1: Fig. S6 I-L). Cdc14b is a dual specificity protein phosphatase known to be involved in DNA damage response [42] and cell cycle regulation [43], and based on this genetic data is the likely upstream phosphatase acting on COMT pS261 and LMNA pS394 in liver and heart, respectively. Additional examples with complex genetic regulation include PDLIM4 pS119 and NGEF pS606, both found in heart (Additional file 1: Fig. S6). Genetic effects on PDLIM4 pS119 were mediated through PDLIM4 abundance and Il15 transcript expression (Additional file 1: Fig. S6A-F). Il15 is a cytokine, and signaling through Il15 results in kinase SYK activation to stimulate cell proliferation [44]. Allele effects of the phQTL (PDLIM4 pS119), the eQTL (Il15), and mediation analysis are all consistent with higher levels of Il15 leading to higher levels of PDLIM4 pS119. For NGEF pS606, we found that its distant phQTL was mediated through the transcript abundance of Prkca, protein kinase C, alpha (Additional file 1: Fig. S6G-L). In summary, we found that most local phQTL have a corresponding pQTL and are primarily driven by their parent protein abundance (mechanism 1), while distant phQTL with adj-phQTL are primarily driven by factors that are independent of the parent protein abundance (mechanism 2). We identified many examples of regulation of phosphopeptides by both mechanisms 1 and 2 (Additional file 6: Table S5). These include 6 kinases (Pdk1, Mapkapk3, Nme6, Plk2, Prkca, Sbk3), 3 phosphatases (Cdc14a, Cdc14b, Pxylp1), and additional genes that are known to be involved in cell signaling transduction and affect protein phosphorylation, including Il15, Negr1, and Stat6. ## Regulation of phosphorylation sites within a protein We next asked whether phosphopeptides that co-occur on the same protein were coordinately regulated. We identified 1151, 1148, and 1093 proteins with two or more phosphopeptides quantified in heart, kidney, and liver tissues, respectively (Additional file 1: Fig. S1A). To determine whether phosphorylation sites on the same protein were potentially co-regulated, we looked at the correlation of the abundances of phosphopeptides from the same protein. ( Additional file 1: Fig. S7A-B). In each tissue, the median correlation of phosphopeptides decreased but remained significant after adjustment based on their parent proteins, indicating that phosphopeptides from the same protein can be co-regulated independently of parent protein abundance. For example, we quantified 7 phosphopeptides from EGFR in liver tissue with correlations among the adjusted phosphopeptides ranging from −0.049 to 0.598 (Fig. 6A). While only one of these sites had a significant adj-phQTL (pS1044, Chr 9 at 107Mb), two sites had sub-threshold adj-phQTL with allele effects that are consistent with a shared adj-phQTL, suggesting that phosphorylation sites on EGFR can be co-regulated. Fig. 6Phosphorylation sites on one protein can be regulated coordinated and not coordinated. A Heatmap of Pearson correlations of abundances of phosphopeptides from parent protein ABLIM1. B Genome scans of pS539 and pS56 on UCKL1 in kidney tissue. C A local CAST allele at Uckl1 drove low abundance of UCKL1 pS539 in kidney tissue. D Distant NOD and PWK allele on chromosome 18 drove low abundance of UCKL1 pS56 in kidney tissue. E Heatmap of Pearson correlations among all proteins quantified in ATP synthase complex in heart tissue. F The AJ allele at Atp5h drove low abundance of the entire ATP synthase complex in heart tissue. All quantified ATP synthase complex subunits have low protein abundance in CC032, CC033, and CC044 strains, which possess the AJ allele, in heart data. G Mediation analysis using proteomics data identified ATP5H as the mediator of a phQTL for ATP5E. Each gray dot is a mediation score representing the ATP5E pQTL LOD score conditioned on a protein as candidate mediator. ATP5H was detected as the strongest mediator of the ATP5E distal pQTL in heart tissue. All ATP synthase complex subunits have mediation z-scores < −8 and were highlighted in black. Other quantified ATP synthase complex subunits, ATP5S, ATP5G2, and ATP5J, were highlighted in blue. Horizontal dashed line at LOD of 6 was included for reference. H Heatmap of Pearson correlations among all phosphorylation events quantified from the ATP synthase complex in heart tissue. The correlations among the five sites from ATP5A1 are highlighted by a dashed square. Correlation with FDR < 0.01 were highlighted using stars. I Genome scans of ATP5A1 pS53 in the three tissues, revealing co-mapping phQTL in all the three tissues. J Allele effects of ATP5A1 pS53 phQTL were highly correlated in the three tissues Phosphorylation sites on one protein can be regulated differently. For example, abundances of UCKL1 pS56 and UCKL1 pS539 are not correlated ($r = 0.020$), and genetic mapping identifies a local phQTL on chr 2 for UCKL1 pS539 and a distant phQTL on chr 18 for UCKL1 pS56 (Fig. 6B). The CAST allele drives the low abundance of UCKL1 pS539 (Fig. 6C), and NOD and PWK alleles drive the low abundance of UCKL1 pS56 (Fig. 6D), presenting an example of phosphorylation sites on one protein that are regulated by distinct mechanisms. An adj-phQTL was identified for UCKL1 pS56 but not for UCKL1 pS539. We conclude that the local phQTL of UCKL1 pS539 was mediated through protein abundance (Mechanism 1), whereas the phQTL of UCKL1 pS56 was independent of protein abundance regulation (Mechanism 2). ## Genetic regulation of the ATP synthase complex We conclude with two examples that illustrate how these data can be used as a resource to dissect the genetic regulation of protein and phosphopeptide abundance. The first example is the ATP synthase complex, which is localized to the inner mitochondrial membrane where it converts ADP to ATP as the final step of oxidative phosphorylation [45]. In heart tissue, we quantified 15 subunits of the complex and detected 15 phosphopeptides. The complex is present in kidney and liver as well, but fewer proteins and phosphopeptides were detected in these tissues. Previously, we demonstrated that proteins that form complexes are often co-regulated [24, 25]. The abundance of subunits from the ATP synthase complex are tightly correlated (median correlation $r = 0.83$) (Fig. 6E). In heart tissue, several subunits share a significant co-mapping distant pQTL on Chr 11 at 96Mb, which is the location of the Atp5h gene. Mediation analysis of the distant pQTL identified ATP5H as the mediator of complex-wide protein abundance (Additional file 6: Table S5). The A/J allele at Atp5h is associated with low complex-wide abundance, consistent with stoichiometric regulation of the complex by the lowest expressed subunit (Fig. 6F,G) [24, 25]. We looked at phosphorylation sites across the complex in heart tissue. The abundance of phosphopeptides from the ATP synthase complex are less tightly correlated (median correlation $r = 0.049$) compared to the proteins (Fig. 6E, H). Similar results were seen in liver and kidney (Additional file 1: Fig. S7C-F). Among the 15 phosphorylation sites detected, a cluster of sites in ATP5A1 including pS53 are highly correlated, and share a suggestive (FDR < 0.5) genetic association with the Pdk1 locus. ATP5A1 pS53, which is quantified in all three tissues, has a distant adj-phQTL on Chr 2 at 73 Mb that is mediated by PDK1 (Fig. 6I, Additional file 1: Fig. S7C), and has low levels of phosphorylation associated with the NZO allele at this locus (Fig. 6J). We also identified two significant (FDR < 0.01) correlations between sites in different subunits: ATP5F1 pS226 and ATP5A1 pT236, and ATPF1 pS226 and ATP5C1 pS265, suggesting possible coordination of phosphorylation activity across subunits within the Atp5 synthase complex. ## Genetic regulation of propionyl-CoA carboxylase PCCA and PCCB together make up the biotin-dependent propionyl-CoA carboxylase (PCC), a mitochondrial enzyme involved in the catabolism of odd chain fatty acids and branched-chain amino acids [46, 47]. A single phosphorylation site pS248 on PCCA was detected in all three tissues, whereas no phosphopeptides were found from PCCB. The site PCCA pS248 had a significant distant adj-phQTL on Chr 2 at 72Mb in both heart and liver. There is a suggestive distant adj-phQTL at the same locus in kidney (LOD = 6.7). In all three tissues, the Chr 2 QTL had a low NZO allele (Fig. 7A, B) and was mediated through PDK1 (Fig. 7C).Fig. 7Genetic regulation of PCCA and PCCB across three tissues. A Co-mapping distant phQTL of PCCA pS248 was identified in liver and heart but not kidney tissue. NZO allele drove the low level of this phosphorylation event. Data were categorized based on the founder haplotye at the identified phQTL. B Abundances of PDK1 and PCCA pS248 were highly correlated in heart and liver but not in kidney tissue. Abundance of PDK1 and PCCA pS248 in each individual sample [116] were categorized based on the haplotye of the phQTL on PCCA pS248 in heart and liver tissues on Chromosome 2. C Genome scans for PCCA pS248 are overlayed with mediation scores in heart and liver tissues. Each gray dot is a mediation score representing the PCCA pS248 phQTL LOD score conditioned on a protein as candidate mediator. D Genome scans of PCCA and PCCB in all the three tissues. Local pQTL for PCCB and distant pQTL for PCCA co-mapped to the same locus in heart tissue. PCCB was identified as the mediator of the PCCA distant pQTL. Local pQTL for PCCA and distant pQTL for PCCB co-mapped to the same locus in liver tissue and kidney tissues. PCCA was identified as the mediator of the PCCB distant pQTL. E Allele effects of identified pQTL for PCCA and PCCB in the three tissues. F Protein abundance of PCCA and PCCB were highly correlated in each tissue. Protein abundance in each individual sample [116] were categorized based on the haplotye of the pQTL on PCCA in kidney and liver tissues on Chromosome 14. G The transcript level of Pccb is distinctly higher than the mRNA level of Pcca in kidney and liver tissues but not in heart tissue. mRNA abundance of Pcca and Pccb in each individual sample [116] were categorized based on the haplotye of the pQTL on PCCA in heart tissue on Chromosome 9. H Sex-interactive local pQTL on PCCA and sex-interactive distant pQTL on PCCB co-mapped to the same locus in the kidney tissue, characterized by a distinct NZO effect (NZO males with greater abundances than NZO females). PCCA was identified as the mediator of PCCB sex-interactive distant pQTL. Points are colored by founder haplotype at sex-interactive phQTL. Males and females from the same CC strain were connected by a line We also identified a local pQTL on PCCB and PCCA has a co-mapping distant pQTL (Chr 9 at 100Mb) that is mediated by PCCB in heart tissue. Low expression of PCCA and PCCB is associated with the NZO and PWK alleles at the Chr 9 QTL. In kidney and liver, PCCA instead maps with the a local pQTL and PCCB has a distant pQTL (Chr 14 at 123Mb), mediated by PCCA (Fig. 7D). Low expression at the Chr 14 QTL is associated with the NZO allele. The protein abundances of PCCA and PCCB are tightly correlated in both tissues ($r = 0.935$ in kidney to $r = 0.975$ in heart, $p \leq 2.2$e−16; Fig. 7E, F). We hypothesized that the switching across tissues of the local and distant QTL for protein abundances was due to tissue-specific changes in stoichiometric regulation. This is confirmed by looking at the mRNA level at these genes (Fig. 7G). In kidney and liver, Pcca mRNA has lower abundance and the Chr 14 QTL (local to Pcca) is the common driver of PCCA and PCCB protein abundance. In heart, when NZO or PWK alleles are present at the Chr 9 locus (local to Pccb), the mRNA level of Pccb is lower than Pcca, and PCCB becomes the driver of protein abundances. This is consistent with stoichiometric regulation in which the gene with lowest mRNA expression becomes the genetic driver of protein complex abundance [24]. We also note that the Chr 14 pQTL for PCCA (local) and PCCB (distant) in the kidney is sex-specific, with low expression in the presence of the NZO allele being most pronounced in females (Fig. 7H). ## Discussion We quantified transcripts, proteins, and phosphorylated peptides across three tissues in a genetically diverse mouse population. Examining the adjusted phosphopeptides, we demonstrated that phosphorylation levels are heritable and can differ between sexes. We mapped pQTL, phQTL, and adj-phQTL and describe two distinct mechanisms for genetic regulation of phQTL. A large proportion of phQTL are mediated through protein abundance. Other phQTL remain significant after accounting for the effects of the parent protein abundance on phosphopeptide abundance (adj-phQTL) suggesting that genetic factors are likely affecting the levels of site-specific phosphorylation. We applied mediation analysis to identify proteins or transcripts that are candidate causal intermediates underlying distant adj-phQTL. These mediators included kinases, phosphatases, and upstream regulators involved in the phosphorylation process. We highlighted the most significant mediation effects above. However, there are many more examples of plausible mediation that can be mined from these data (Additional file 6: Table S5), providing experimentally testable hypotheses about the molecular interactions that mediate site-specific phosphorylation. We identified PDK1 abundance as the mediator of nine adj-phQTL across three tissues. All nine phosphorylation sites were found in parent proteins involved in the respiratory chain, including sites on APT5A1 and PCCA with adj-phQTL that are shared across all three tissues. Low expression of PDK, specifically in mice carrying the NZO haplotype, leads to lower kinase activity and ultimately lower levels of phosphorylation on key proteins involved in respiratory chain metabolism. These findings are particularly interesting because the NZO mouse is a well-studied polygenic model for human metabolic syndrome [48, 49]. QTL mapping in the NZO mouse has identified Tbc1d1 [50], Zfp69 [51], and Lepr [52] as genes contributing to type 2 diabetes. Here, we identify a potential role for aberrant protein phosphorylation due to low expression of PDK1 that may further contribute to metabolic disease phenotypes characteristic of the NZO mouse. While investigating the protein complex formed by PCCA and PCCB, besides the adj-phQTL identified on pS248 on PCCA, we identified two additional pQTL, one local to PCCA and the other local to PCCB, both with low expression of the NZO allele. In heart tissue, the C to T (100,982,310bp) and G to A (100,987,863bp) mutations specific to the NZO and PWK alleles potentially affect transcription and lead to the lower transcript level and protein level of PCCB (100,864,085–100,916,951bp). In kidney and liver tissues, NZO-specific mutations in Pcca may cause the low abundance of PCCA and PCCB. This example illustrates the complexity and delicacy of the mechanisms of genetic regulation of protein abundance and phosphorylation. We recognize some limitations of the current study, including limitations on the power to map genetic associations for phQTL. We find suggestive evidence for many genetic effects on phosphopetides that may be real but did not reach stringent genome-wide and multiple testing adjusted significance criteria. The CC panel is finite and it is not possible to improve mapping power substantially by adding more strains. However, by adding more animals per strain, the precision of protein and peptide quantification can be improved to increase mapping power [23]. There were also instances where we did not detect phosphorylated peptides that must be present, for example, the ATP synthase complex in liver and kidney. Advanced mass spectrometry technology, especially targeted mass spectrometry technology could be developed and used to obtain better coverage and provide a more complete picture of the phosphorylated proteome [53]. ## Conclusions This integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. Similar approaches could be used in combination with interventions, including mapping modifiers of transgenic models of disease [54]. The multi-omics data generated in this study provides a resource for further exploration. The upstream kinases, phosphatases, or other regulating factors identified here can seed hypotheses and motivate further mechanistic studies in disease models. Moreover, it sets a precedent for future studies of regulatory mechanisms for other post translation modifications (PTMs) of proteins, such as methylation and ubiquitination. Coupled with advanced mass spectrometry technology for deeper coverage, we foresee this strategy being used to provide a comprehensive regulatory map of PTMs. ## Mice We received pairs of young mice from 58 CC strains from the UNC Systems Genetics Core Facility between the summer of 2018 and early 2019. Mice were singly housed upon receipt until 8 weeks of age. More information regarding the CC strains can be found at https://csbio.unc.edu/CCstatus/index.py. ## Genotyping, founder haplotype reconstruction, and gene annotation The genotyping and haplotype reconstruction for the CC mice were previously described [25]. Briefly, the 116 CC mice were genotyped on the Mini Mouse Universal Genotyping Array [55] (MiniMUGA), which includes 11,125 markers. Founder haplotypes were reconstructed using a Hidden Markov Model (HMM), implemented in the qtl2 R package [56], using the “risib8” option for an eight-founder recombinant inbred panel and Genome Reference Consortium Mouse Build 38 (mm10). Heterozygous genotypes were omitted, and haplotype reconstructions are limited to homozygous states, smoothing over a small number of residual heterozygous sites that remain in the CC mice. Ensembl v91 gene and protein annotations were used in the CC and founder strains. ## Sample preparation for proteomics and phosphoproteomics analysis Proteome sample preparation and data analysis for the CC liver tissue was described previously [25]. We also collected kidney and heart tissues along with liver tissue. Singly housed CC mice had their food removed 6 h prior to euthanasia and tissue harvest. Tissues were dissected, weighed, and snap frozen in liquid nitrogen. Pulverized heart and kidney tissue were syringe-lysed in 8 M urea and 200 mM EPPS pH 8.5 with protease inhibitor and phosphatase inhibitor. BCA assay was performed to determine protein concentration of each sample. Samples were reduced in 5 mM TCEP, alkylated with 10 mM iodoacetamide, and quenched with 15 mM DTT. One hundred micrograms protein was chloroform-methanol precipitated and re-suspended in 100 μL 200 mM EPPS pH 8.5. The proteins were digested by Lys-C at a 1:100 protease-to-peptide ratio overnight at room temperature with gentle shaking. Trypsin was used for further digestion for 6 h at 37°C at the same ratio with Lys-C. After digestion, 50 μL of each sample was combined in a separate tube and used as the 16th sample in all 8 tandem mass tag (TMT) 16plex, rather than the 11plex used previously for liver tissue. Fifty microliters of each sample was aliquoted, and 12 μL acetonitrile (ACN) was added into each sample to $30\%$ final volume. One hundred micrograms TMT reagent (126, 127N, 127C, 128N, 128C, 129N, 129C, 130N, 130C, 131N, 131C, 132N, 132C, 133N, 133C, 134N) in 10 μL ACN was added to each sample. After 1 h of labeling, 1 μL of each sample was combined, desalted, and analyzed using mass-spec. Total intensities were determined in each channel to calculate normalization factors. After quenching using $0.3\%$ hydroxylamine, 16 samples were combined in 1:1 ratio of peptides based on normalization factors. High-Select Fe-NTA Phosphopeptide Enrichment Kit (Thermo Fisher) was used to enrich the phosphorylated peptides (phosphopeptides) according to the manufacturer’s protocol. Flow through and washes from phosphopeptide enrichment were combined, dried, and fractionated with basic pH reversed phase (BPRP) high-performance liquid chromatography (HPLC) as described before. We used an Agilent 1260 pump equipped with a degasser and a single wavelength detector (set at 220 nm). Peptides were subjected to a 50-min linear gradient from 8 to $40\%$ acetonitrile in 10 mM ammonium bicarbonate pH 8 at a flow rate of 0.6 mL/min over an Agilent 300Extend C18 column (3.5 μm particles, 4.6 mm ID and 250 mm in length). The peptide mixture was fractionated into a total of 96 fractions which were consolidated into 24. Twelve fractions were desalted and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Meanwhile, the eluent from the phosphopeptide enrichment was desalted and analyzed by LC-MS/MS. ## Liquid chromatography and tandem mass spectrometry The method for proteome data collection in liver tissue was described previously [57]. Proteome data in heart and kidney tissues were collected on an Orbitrap Eclipse mass spectrometer coupled to a Proxeon NanoLC-1200 UHPLC. The peptides were separated using a 100-μm capillary column packed with ~35 cm of Accucore 150 resin (2.6 μm, 150 Å; Thermo Fisher Scientific). The mobile phase was $5\%$ acetonitrile, $0.125\%$ formic acid (A) and $95\%$ acetonitrile, $0.125\%$ formic acid (B). For BPRP fractions, the data were collected using a DDA-SPS-MS3 method with online real-time database searching (RTS) [58] [59]. The data were collected using a DDA-SPS-MS3 method. A database that included all entries from an indexed Ensembl mouse database version 90 (downloaded:$\frac{10}{09}$/2017) was used in RTS. Each fraction was eluted using a 90-min method over a gradient from 6 to $30\%$ B. Peptides were ionized with a spray voltage of 2500 kV. The instrument method included Orbitrap MS1 scans (resolution of 1.2×105; mass range 400−1600 m/z; automatic gain control (AGC) target 4×105, max injection time of 50 ms and ion trap MS2 scans (CID collision energy of $35\%$; AGC target 7.5× 103; rapid scan mode; max injection time of 50 ms)). RTS was enabled and quantitative SPS-MS3 scans (resolution of 50,000; AGC target 2×105; max injection time of 200 ms) were processed through Orbiter real-time database searching. This data acquisition includes high-field asymmetric-waveform ion-mobility spectrometry (FAIMS). The dispersion voltage (DV) for FAIMS was set at 5000V, the compensation voltages (CVs) were set at −40V, −60V, and −80V, and TopSpeed parameter was set at 1 s per CV [60]. Mass spectrometric data for phosphopeptides fractions in liver tissue were collected on an Orbitrap Lumos mass spectrometer. Mass spectrometric data were collected in HCD and CID modes. Each fraction was eluted using a 180-min method over a gradient from 6 to $30\%$ B. Peptides were ionized with a spray voltage of 2600 kV. The instrument method included Orbitrap MS1 scans (resolution of 1.2×105; mass range 400−1400 m/z; automatic gain control (AGC) target 1×106, max injection time of 50 ms). The 10 most intense MS1 ions were selected for MS2 analysis. Following acquisition of each MS2 spectrum, a synchronous precursor selection (SPS) MS3 scan was collected on the Top 10 most intense ions in the MS2 spectrum. The isolation width was set at 0.7 Da, and isolated precursors were fragmented using two methods. In the first method, we used collision-induced dissociation (CID) at a normalized collision energy (NCE) of $35\%$ with MultiStage Activation (MSA), and in the second method, we used higher-energy collision-induced dissociation (HCD) at a normalized collision energy (NCE) of $33\%$. Following acquisition of each MS2 spectrum, a synchronous precursor selection (SPS) MS3 scan was collected on the Top 10 most intense fragment ions in the MS2 spectrum. SPS-MS3 precursors were fragmented by higher-energy collision-induced dissociation (HCD) at an NCE of $65\%$ and analyzed using the Orbitrap. Phosphoproteome analysis in heart tissues were processed with FAIMS/hrMS2 using our optimized workflow for multiplexed phosphorylation analysis on an Orbitrap Eclipse mass spectrometer. Briefly, the Thermo FAIMS Pro device was operated with default parameters (inner and outer electrode were set at 100°C, yielding a FWHM between 10 and 15 V and dispersion voltage (DV) was set at −5000 V). Each fraction was analyzed twice by the mass spectrometer, once with a method incorporating two CVs (CV= −45 and −70V) and again with three CVs (CV= −40V, −60V, and −80V) using a 2.5h method having a gradient of 6 to $30\%$ B [61]. Mass spectrometric data for phosphopeptides fractions in kidney tissue were collected on an Orbitrap Lumos mass spectrometer. Mass spectrometric data were collected in CID mode and then processed with FAIMS/hrMS2 using our optimized workflow for multiplexed phosphorylation analysis with a method incorporating two CVs (CV= −45 and −70V). Detailed parameters for MS2 and MS3 are embedded in the RAW files. ## Mass spectrometry data analysis Mass spectra data were processed using a Comet-based pipeline. Spectra were converted to mzXML using a modified version of ReAdW.exe. Database search included all entries from an indexed Ensembl database version 90 (downloaded:$\frac{10}{09}$/2017). This database was concatenated with one composed of all protein sequences in the reversed order. Searches were performed using a 50-ppm precursor ion tolerance for total protein-level analysis. The product ion tolerance was set to 1.000 Da for MS3-based analysis and 50ppm for MS2-based analysis, respectively. TMT tags on lysine residues, peptide N termini (+304.207 Da for heart and kidney tissues and +229.163 Da for liver tissue), and carbamidomethylation of cysteine residues (+57.021 Da) were set as static modifications, while oxidation of methionine residues (+15.995 Da) was set as a variable modification. In addition, for phosphopeptide analysis, phosphorylation (+79.966 Da) on serine, threonine, and tyrosine were included as variable modifications. Peptide-spectrum matches (PSMs) were adjusted to FDR < 0.01. PSM filtering was performed using a linear discriminant analysis (LDA), as described previously, while considering the following parameters: XCorr, ΔCn, missed cleavages, peptide length, charge state, and precursor mass accuracy. For TMT-based reporter ion quantitation, we extracted the summed signal-to-noise (S:N) ratio for each TMT channel and found the closest matching centroid to the expected mass of the TMT reporter ion. For protein-level comparisons, PSMs from all three tissues were identified, quantified, and collapsed to a peptide FDR < 0.01 and then collapsed further to a final protein-level FDR < 0.01, which resulted in a final peptide level FDR <0.001. Moreover, protein assembly was guided by principles of parsimony to produce the smallest set of proteins necessary to account for all observed peptides. PSMs with poor quality, MS3 spectra with TMT reporter summed signal-to-noise of less than 100, or no MS3 spectra were excluded from quantification. We provide an estimate for the probability of correct localization for each phosphorylation site using AScore algorithm [62]. Eighty-four percent, $89\%$ and $90\%$ of the quantified phosphopetide used for analysis have an AScore greater than 13 in heart, liver, kidney tissues, respectively ($P \leq 0.05$). All the information were uploaded to figshare (https://figshare.com/projects/Multi-omics_analysis_identifies_drivers_of_protein_phosphorylation/137673, Under the folder titled “Raw summary of protein and phosphopeptides quantitation” - siteQuant5100.csv). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with dataset identifiers PXD032843. ## Sample preparation for transcriptomics analysis Livers, hearts, and kidneys were dissected from each CC mouse, flash frozen, and stored at −80°C. Once all samples were collected, frozen tissues were pulverized in liquid nitrogen, divided into aliquots, and then sent to the Genome Technologies service (Jackson Laboratory) for RNA extraction and RNA-seq analysis. Total RNA was extracted and purified using the MagMAX mirVana Total RNA Isolation Kit (Thermo Fisher) and the KingFisher Flex purification system (Thermo Fisher). Briefly, pulverized tissue samples were lysed in TRIzol (Thermo Fisher Scientific), chloroform was then added to the TRIzol homogenate, and the RNA-containing aqueous layer was removed for RNA isolation, following the manufacturer’s protocol. RNA concentration and quality were assessed using the Nanodrop 8000 spectrophotometer (Thermo Scientific) and Total RNA Nano assay (Agilent Technologies). Libraries were constructed using the KAPA mRNA HyperPrep Kit (Roche) following manufacturer’s protocols. Briefly, poly-A mRNA was selected from total RNA using oligo-dT magnetic beads, followed by RNA fragmentation, first- and second-strand cDNA synthesis, ligation of Illumina-specific adapters containing unique dual index barcode sequences for each library, and PCR amplification. Library quality was assessed using the D5000 ScreenTape (Agilent Technologies) and concentration measured with the Qubit dsDNA HS Assay (Thermo Fisher). Finally, pooled libraries were sequenced on the NovaSeq 6000 platform (Illumina) using the S1 Reagent Kit v1, yielding 20–40M (target 30M) 1 × 100bp single-end (SE) reads per sample. ## Filtration of peptides that contain polymorphism Peptides that contain polymorphisms, i.e., coding variants, bias protein abundance estimation in genetically diverse samples because peptides with differing sequences are not quantified simultaneously. A mouse with an alternative allele with respect to the B6 reference mouse genome will have reduced intensity or even non-detection for the reference peptide. This bias could then be propagated to estimates of protein abundance or phosphopeptide abundance, which can either obscure the signal of a true QTL or induce a false local QTL as a flag of the polymorphism. Therefore, we removed all polymorphic peptides based on the genome sequences of the founder strains. We filtered out peptides with known polymorphisms in the 8 founder strains of the CC. This is a more stringent filter than was applied in our previous study of the liver data [25] and thus there are minor differences in the protein quantification. Before filtering, 153,856 unique peptides were quantified across 28 TMTs. In total, 6841 ($4.4\%$) unique peptides were filtered out due to not being identical in all 8 strains. For each phosphopeptide quantified, the sequences of the corresponding protein were extracted from the founder strain genomes. To ensure that polymorphic peptides did not drive phQTL signal, we required phosphopeptides to have sequences of three amino acids adjacent to both sides that were present in all the founder strain genome sequences. ## Peptide normalization and protein abundance estimation Peptides, including phosphopeptides, were standardized within TMT batch. The intensity for each peptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j from sample \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i was scaled by the ratio of the maximum cumulative peptide intensity in the batch to the cumulative peptide intensity for individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{ij}^{\prime}= {\varphi }_{i}{y}_{ij}$$\end{document}yij′=φiyij where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varphi }_{i}= \frac{\underset{i^{\prime} \in B[i]}{\mathrm{max}}\left({\sum }_{J}{y}_{{i}^{\prime}j}\right)}{{\sum }_{J}{y}_{ij}}$$\end{document}φi=maxi′∈B[i]∑Jyi′j∑Jyij. The abundance for each protein \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j from an individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i was estimated by summing the intensities of its component peptides (after removal of polymorphic peptides), then scaled relative to the abundance from the bridge sample and log transformed: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{ij}^{\prime\prime}= {\text{log}}_{2}\left(\frac{{\sum }_{M} {y}_{im}^{\prime}+1}{{\sum }_{M}{y}_{b[i]m}^{\prime}+1}\right)$$\end{document}yij″=log2∑Myim′+1∑Myb[i]m′+1 where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$m = 1$, \dots, M$$\end{document}$m = 1$,⋯,M indexes the peptides that map to protein \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{b\left[i\right]m}^{\prime}$$\end{document}ybim′ is the intensity of peptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m$$\end{document}m for the corresponding bridge sample from individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i’s batch. We pre-adjusted for the effect of TMT batch using a linear mixed effect model (LMM) to allow strain pairs that span two TMT batches to be summarized to the strain level for downstream QTL analysis. The following LMM was fit for each protein \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j: 1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime(j)}=\mu + {\text{sex}}\left[i\right]+{\text{strain}}\left[i\right]+ {\text{batch}}\left[i\right]+ {\varepsilon }_{i}$$\end{document}yi″(j)=μ+sexi+straini+batchi+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime(j)}$$\end{document}yi″(j) is the abundance of protein \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j for individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μ is the shared intercept, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sex}}\left[i\right]$$\end{document}sexi is the contribution of sex for individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i (fit as a fixed effect), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{strain}}\left[i\right]$$\end{document}straini is the contribution of the CC strain of individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i (fit as a random effect), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{batch}}\left[i\right]$$\end{document}batchi is the contribution of individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i’s TMT batch (fit as a random effect), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varepsilon }_{i}$$\end{document}εi is the error for individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varepsilon }_{i} \sim {\text{N}}\left(0, {\sigma }^{2}\right).$$\end{document}εi∼N0,σ2. All downstream analyses were performed on quantities after subtracting off the batch effect: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime\prime(j)}= {y}_{i}^{\prime\prime(j)}$$\end{document}yi″′(j)=yi″(j) - \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\text{batch}}[i]$$\end{document}batch^[i], where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\text{batch}}[i]$$\end{document}batch^[i] is the best linear unbiased prediction (BLUP) for the TMT batch of individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i. The LMM was fit using the lme4 R package4. Proteins that were unobserved for $50\%$ or more of samples were removed from further analysis. ## Phosphopeptide normalization and adjustment for protein abundance Phosphopeptides were processed similarly to protein abundance, but without the peptide-to-protein summation step. For each phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j, we normalized as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime(j)}= {\mathrm{log}}_{2} \left(\frac{{y}_{i}^{\prime(j)}+1}{{y}_{b[i] }^{\prime(j)}+1} \right)$$\end{document}yi″(j)=log2yi′(j)+1yb[i]′(j)+1, which were then batch adjusted as in Equation 1. Phosphopeptides that were unobserved for $50\%$ or more of samples were removed from further analysis. To distinguish genetic effects on phosphopeptides independent of the proteins from which they were derived, which we refer to as parent proteins, we also pre-adjusted for the effect of parent protein abundance by taking residuals from a linear model: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime\prime(j)}=\mu +{\text{parent}}\left[ij\right]+{\varepsilon }_{i}$$\end{document}yi″′(j)=μ+parentij+εi, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{parent}}\left[ij\right]$$\end{document}parentij is the contribution of the abundance of the parent protein for phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j for individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i (fit as a fixed effect). The residuals for phosphopeptides were then calculated as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime\prime(\text{resid }j)}= {y}_{i}^{\prime\prime\prime(j)}$$\end{document}yi″′(residj)=yi″′(j) − \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\text{parent}}[ij]$$\end{document}parent^[ij]. Parent protein abundances were estimated from their component peptides as previously described, but we first filtered out any peptides with a phosphorylation site. ## Heritability estimation We estimated heritability for protein and phosphopeptide abundance in each of the tissues using an LMM. For a given protein or phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j from a specified tissue, we fit: 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}^{\prime\prime\prime(j)}=\mu + {\text{sex}}\left[i\right]+{\text{kinship}}\left[i\right]+{\varepsilon }_{i}$$\end{document}yi″′(j)=μ+sexi+kinshipi+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{kinship}}[i]$$\end{document}kinship[i] is a random effect representing cumulative additive genetic effects and should thus capture similarities due to overall relatedness, modeled across individuals as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{kinship}} \sim \text{N(}{0}\text{, }{\text{G}}{\tau }^{2}\text{)}$$\end{document}kinship∼N(0, Gτ2). Bold text denotes vector and matrix quantities. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{G}}$$\end{document}G is a realized genomic relationship matrix, estimated from markers across all chromosomes, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }^{2}$$\end{document}τ2 is the variance component underlying the kinship effect. Heritability is estimated as the proportion of variation due to genetic effects: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}= \frac{{\tau }^{2}}{{\tau }^{2}+ {\sigma }^{2}}.$$\end{document}h2=τ2τ2+σ2. The qtl2 R package was used to fit the LMM and extract the heritability estimate3. ## Sex effects on protein and phosphopeptide abundance Proteins and phosphopeptides that exhibited differential abundance between the sexes within a tissue, i.e., sex effects, were identified using the same LMM described in Equation 2 for heritability estimation. We instead compared it to a null model excluding the sex term and summarized the statistical significance with a likelihood ratio test p-value. The LMMs were fit using the qtl2 R package, using maximum likelihood estimates (MLE) rather than restricted maximum likelihood estimates (REML), as is appropriate for testing a fixed effect term. For a given outcome type (proteins or phosphopeptides), summary type (averages or differences), and tissue, significant sex effects were declared based on FDR < 0.1 using the BH method5. ## QTL analysis For QTL analysis, we first summarized CC strain pairs as averages and differences (male − female) of the abundance of proteins and phosphopeptides. Phosphopeptides were adjusted for parent proteins as before, but at the strain level. We mapped QTL for proteins (pQTL), phosphopeptides (phQTL), and adjusted phosphopeptides (adj-phQTL). QTL for CC strain differences represent sex-by-genotype interactions where QTL effects differ between sexes. Founder haplotype probabilities for CC strain genomes were estimated by averaging the probabilities for the male and female at marker positions from MiniMUGA. For each protein or phosphopeptide (unadjusted or adjusted) that was quantified in a tissue, we performed a genome-wide QTL scan by testing a QTL effect at positions across the genome. We fit a similar LMM to the heritability model in Equation 2 for each protein or phosphopeptide for a given tissue: 3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(j)}=\mu + {\text{QTL}}_{p}\left[i\right]+{\text{kinship}}_{c\left[p\right]}\left[i\right]+{\varepsilon }_{i}$$\end{document}y¯i″′(j)=μ+QTLpi+kinshipcpi+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(j)}$$\end{document}y¯i″′(j) is the abundance summary (average or difference) for protein or phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j of CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{QTL}}_{p}\left[i\right]={{\varvec{d}}}_{ip}^{T}{{\varvec{\beta}}}_{\text{QTL}}$$\end{document}QTLpi=dipTβQTL is the effect of putative QTL at marker \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p for CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{d}}}_{ip}^{T}$$\end{document}dipT representing the founder haplotype probability vector for CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i at marker \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p (e.g., ordering the founder strains as AJ, B6, 129, NOD, NZO, CAST, PWK, and WSB, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{d}}}_{ip}^{T}=[0 1 0 0 0 0 0 0]$$\end{document}dipT=[01000000] for a CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i that is B6/B6 at marker \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{\beta}}}_{\text{QTL}}$$\end{document}βQTL is an eight-element vector of founder allele effects, fit as fixed effects. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{kinship}}_{c[p]}[i]$$\end{document}kinshipc[p][i] term is similar to the kinship effect in the heritability model (Equation 2), though instead modeled as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{kinship}}_{c[p]} \sim \text{N(}{0}\text{, }{\mathbf{G}}_{c[p]}{\tau }^{2}\text{)}$$\end{document}kinshipc[p]∼N(0,Gc[p]τ2), where the realized genetic relationship matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{c[p]}$$\end{document}Gc[p] used when testing markers as QTL on chromosome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c$$\end{document}c is estimated by excluding all markers from chromosome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c$$\end{document}c, i.e., the leave-one-chromosome-out (LOCO) method, to avoid the kinship term absorbing some of the QTL effect and reducing mapping power [63]. The kinship effect is used in mapping to account for potential population structure [64–67]. The strength of QTL significance was summarized by comparing the likelihood of Equation 3 to the likelihood of the null model excluding the QTL term, referred to as the log-odds (LOD) score. Mapping QTL for adjusted phosphopeptides is analogous to including the parent protein as a covariate in Equation 3 (and its null model). All genome scans for protein abundance and phosphopeptides were performed in the qtl2 R package [56]. ## QTL significant thresholds We estimated significance thresholds for QTL specific to individual proteins and phosphopeptides using permutations [68]. By performing 10,000 permutations for each outcome separately, outcome-specific thresholds account for differences in distribution, levels of missing data, and differing kinship effects. Permutation genome scans used the model in Equation 3, but with the data permuted by rearranging the CC strain labels on founder haplotype probabilities, thus breaking the QTL signal but not the kinship. We used a genome-wide error rate correction across marker loci and then applied an FDR correction to account for multiple testing across each outcome type within a tissue [69]. Specifically, for each outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j within a tissue, we fit a generalized extreme value distribution (GEV) from the 10,000 maximum LOD scores from the permutations [70, 71]. For a given tissue and outcome type (strain averages or strain differences of proteins, phosphopeptides, and adjusted phosphopeptides), we calculated genome-wide permutation p-values as 4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{j}^{\text{perm}}=1- {F}_{j}\left(\text{max LOD}\left[j\right]\right)$$\end{document}pjperm=1-Fjmax LODjwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${F}_{j}$$\end{document}Fj is cumulative density function for the GEV of outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{max LOD}[j]$$\end{document}max LOD[j] is the maximum LOD score observed for outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j. We then used the Benjamini-Hochberg (BH) procedure5 to calculate FDR q-values from the permutation p-values for a given tissue and outcome type. To estimate significance thresholds that are FDR-adjusted and outcome-specific, we applied interpolation to approximate permutation p-values to q-values < 0.1. Significance thresholds on the LOD score scale were then calculated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{j}^{\text{FDR < 0.1}}={F}_{j}^{-1}\left(1- {p}_{$q = 0.1$}^{\text{interp}}\right),$$\end{document}λjFDR < 0.1=Fj-11-pq=0.1interp, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{j}^{\text{FDR < 0.1}}$$\end{document}λjFDR < 0.1 is the $10\%$ FDR significance threshold for outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${F}_{j}^{-1}$$\end{document}Fj-1 is the inverse cumulative density function for the GEV of outcome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{$q = 0.1$}^{\text{interp}}$$\end{document}pq=0.1interp is the interpolated permutation p-value for q-value = 0.1. ## Defining local/distant status of QTL As previously1, we defined detected QTL as “local” if their genomic coordinates were within 10 Mbp upstream or downstream of the middle of the coding gene and “distant” otherwise. We use the local/distant terminology instead of cis/trans because our definition is defined entirely by position and not genetic mechanism (e.g., cis regulatory elements). We used the broad 10 Mbp local window is broad, but the CC genomes have larger LD blocks than highly recombinant populations, such as the related Diversity Outbred population. Furthermore, we compared the effects of aligned QTL across tissues and sought to avoid aligned QTL being defined as local in one tissue but distant in another. However, the broad local window could misclassify some trans-acting QTL as local. ## Consistency of QTL across tissues We evaluated the consistency of matched QTL (based on related outcomes and co-mapping to the same genomic region) by comparing their allele effects. We compared local and distant QTL across tissues (matching based on protein or phosphopeptide ID). We also compared co-mapping phQTL for unadjusted phosphopeptides to the corresponding local pQTL of the phosphorylation site’s parent protein for a given tissue. For matched local QTL, we only required them to be detected to be defined as co-mapping; for matched distant QTL, we also required that they were within 10 Mbp of each other. Founder allele effects were estimated at the detected QTL marker, representing the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{\beta}}}_{\text{QTL}}$$\end{document}βQTL term from the model in Equation 3. To stabilize the effects, they were modeled as a random effect: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{\beta}}}_{\text{QTL}}\sim {\text{N}}(0, \mathbf{I}{{\varvec{\tau}}}_{\text{QTL}}^{2})$$\end{document}βQTL∼N(0,IτQTL2), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{I}$$\end{document}I is the 8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 8 identity matrix and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{\tau}}}_{\text{QTL}}^{2}$$\end{document}τQTL2 is a variance component underlying the allele effects. Allele effects were then estimated as BLUPs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\widetilde{{\varvec{\beta}}}}_{\text{QTL}}$$\end{document}β~QTL), using the qtl2 R package3. The consistency of allele effects was summarized as the Pearson correlation coefficient between matched QTL: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${r}_{\text{QTL1, QTL2}}={\text{cor}}({\widetilde{{\varvec{\beta}}}}_{{\text{QTL}}_{1}}, {\widetilde{{\varvec{\beta}}}}_{{\text{QTL}}_{2}})$$\end{document}rQTL1, QTL2=cor(β~QTL1,β~QTL2), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{QTL}}_{1}$$\end{document}QTL1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{QTL}}_{2}$$\end{document}QTL2 represent a co-mapping matched pair. ## Mediation of phQTL through parent protein abundance We assessed whether detected phQTL were mediated through their parent proteins. For each phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j with a detected phQTL in a specified tissue, we fit the following mode:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(j)}=\mu + {\text{QTL}}\left[i\right]+{\text{parent}}\left[ij\right]+{\text{kinship}}_{c[p]}\left[i\right]+{\varepsilon }_{i}$$\end{document}y¯i″′(j)=μ+QTLi+parentij+kinshipc[p]i+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(j)}$$\end{document}y¯i″′(j) is the unadjusted abundance summary for phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j with the phQTL for CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{QTL}}\left[i\right]$$\end{document}QTLi is as defined in Equation 3 but fixed at the peak marker for the detected phQTL being evaluated, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{parent}}\left[ij\right]$$\end{document}parentij is the contribution of the abundance of parent protein of phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j to strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i, modeled as a fixed effect. We expanded the set of phQTL to include leniently detected ones (FDR < 0.5) for the evaluation of mediation through their parent proteins, providing a clearer picture of the large-scale mediation trends. A mediation LOD score for phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j in a specified tissue, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{LOD}}_{j}^{\text{parent}}$$\end{document}LODjparent, was calculated by comparing Equation 5 to a null model excluding the QTL term. To summarize across phQTL in a tissue, a Delta LOD was calculated by taking the difference between the mediation LOD score and the original LOD score of the phQTL. We note that a similar approach could be used to formally assess mediation of sex effects on phosphopeptides through parent proteins. ## Mediation of distant QTL For each distant QTL detected in the CC tissues, we performed a mediation analysis analogous to the QTL genome scans [22, 24, 25, 72]. Instead of scanning through genetic markers as putative QTL, we scanned through putative mediators (from transcripts or proteins) of the specified distant QTL. A model similar to Equation 3 was fit:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(t)}=\mu + {\text{QTL}}[i]+{\text{mediator}}_{q}[i]+{\varepsilon }_{i}$$\end{document}y¯i″′(t)=μ+QTL[i]+mediatorq[i]+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{y} }_{i}^{\prime\prime\prime(t)}$$\end{document}y¯i″′(t) is the abundance summary (average or difference) for the target protein or phosphopeptide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document}t with the distant QTL for CC strain \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{QTL}}\left[i\right]$$\end{document}QTLi is as defined in Equation 3 but fixed at the peak marker for the detected distant QTL, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{mediator}}_{q}[i]$$\end{document}mediatorq[i] is the contribution of the mediator \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q$$\end{document}q to individual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i, fit as a fixed effect. The significance of the QTL term in Equation 6 is evaluated by comparing to the null model excluding the QTL term, producing a mediation LOD score: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{LOD}}_{q}^{\text{med}}$$\end{document}LODqmed. These summaries represent the distant QTL’s LOD score conditioned on each candidate mediator individually. We also note that the kinship effect is excluded from Equation 6 and its null model to simplify computation. Mediation scans were performed using the intermediate R package (https://github.com/churchill-lab/intermediate). We assume that the vast majority of evaluated mediators for a specified distant QTL are not the true mediator, and thus the distribution of conditional LOD scores can be used as an empirical null distribution, approximately centered around the initially detected LOD score of the distant QTL. We calculate the z-scores of the conditional LOD scores and define strong candidate mediators as those with z < −8. Mediators are also expected to co-map a local QTL to the distant QTL. For distant pQTL, we evaluated proteins as mediators, whereas for distant phQTL, we evaluated both transcripts and proteins. For candidate mediators highlighted in the “Results”, we estimated the strength of the relationships among QTL, mediator, and target based on proportion variance explained (PVE), calculated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{PVE = 1 }- \, \frac{{\text{RSS}}_{1}}{{\text{RSS}}_{0}}$$\end{document}PVE = 1-RSS1RSS0. For the relationship between the QTL and mediator, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RSS}}_{1}$$\end{document}RSS1 is the residual sum of squares from the QTL model (Equation 3) for the mediator and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RSS}}_{0}$$\end{document}RSS0 is the residual sum of squares for the null model excluding the QTL term. For the relationship between mediator and target, the effect of the mediator on the target is evaluated rather than the QTL. We calculated a corresponding p-value for each relationship comparing the alternative and null models using ANOVA. ## Transcriptomics profiling For each tissue, we aligned the RNA-seq reads using bowtie [73] to the pooled transcriptomes of the eight founder strains (Ensembl v84), and the alignments input to the genome reconstruction by RNA-Seq (GBRS) software to estimate total gene counts using EMASE. We used a variance stabilizing transformation [74] for the total gene counts for each tissue. As with protein and phosphopeptide abundance, the normalized expression for each gene was summarized at the CC strain level as averages and differences. Genes with no expression in $50\%$ or more of samples were removed from further analysis. We also mapped eQTL using a similar approach as used for pQTL and phQTL, which we do not report here but make available at GSE199702. ## Supplementary Information Additional file 1: Figure S1. Overview of protein and phosphopeptide quantification. Figure S2. Sex effects and heritability on the abundance of proteins and phosphopepitdes across tissues. Figure S3. pQTL and phQTL mapping from CC strains in heart, kidney and liver tissues. Figure S4. Mediation of phQTL through the abundance of their parent proteins (substrates). Figure S5. NZO allele drives the low abundances of MCAT pS41 in CC strains. Figure S6. pQTL and phQTL were identified to regulate phosphopeptide abundance together. Figure S7. Phosphorylation sites on one protein can be regulated coordinated and not coordinated. Additional file 2: Table S1. Information about the 58 CC strains included in this study. Additional file 3: Table S2. Sex effect for proteins and phosphorylation sites in three tissues in CC strains. Additional file 4: Table S3. Heritability of proteins and phosphorylation sites in three tissues in CC strains. Additional file 5: Table S4. pQTL and phQTL summaries for three tissues in CC strains. Additional file 6: Table S5. 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--- title: 'Maternal weight, gut microbiota, and the association with early childhood behavior: the PREOBE follow-up study' authors: - Ana Nieto-Ruiz - Tomás Cerdó - Belén Jordano - Francisco J. Torres-Espínola - Mireia Escudero-Marín - María García-Ricobaraza - Mercedes G. Bermúdez - José A. García-Santos - Antonio Suárez - Cristina Campoy journal: Child and Adolescent Psychiatry and Mental Health year: 2023 pmcid: PMC10031971 doi: 10.1186/s13034-023-00589-9 license: CC BY 4.0 --- # Maternal weight, gut microbiota, and the association with early childhood behavior: the PREOBE follow-up study ## Abstract ### Background and aim Maternal overweight and breastfeeding seem to have a significant impact on the gut microbiota colonization process, which co-occurs simultaneously with brain development and the establishment of the “microbiota-gut-brain axis”, which potentially may affect behavior later in life. This study aimed to examine the influence of maternal overweight, obesity and/or gestational diabetes on the offspring behavior at 3.5 years of age and its association with the gut microbiota already established at 18 months of life. ### Methods 156 children born to overweight (OV, $$n = 45$$), obese (OB, $$n = 40$$) and normoweight (NW, $$n = 71$$) pregnant women participating in the PREOBE study were included in the current analysis. Stool samples were collected at 18 months of life and gut microbiome was obtained by 16S rRNA gene sequencing. Behavioral problems were evaluated at 3.5 years by using the Child Behavior Checklist (CBCL). ANOVA, Chi-Square Test, ANCOVA, Spearman’s correlation, logistic regression model and generalized linear model (GLM) were performed. ### Results At 3.5 years of age, Children born to OV/OB mothers showed higher scores in behavioral problems than those born to NW mothers. Additionally, offspring born to OB mothers who developed gestational diabetes mellitus (GDM) presented higher scores in attention/deficit hyperactivity and externalizing problems than those born to GDM OV/NW mothers. Fusicatenibacter abundance found at 18 months of age was associated to lower scores in total, internalizing and pervasive developmental problems, while an unidentified genus within Clostridiales and Flavonifractor families abundance showed a positive correlation with anxiety/depression and somatic complaints, respectively. On the other hand, children born to mothers with higher BMI who were breastfed presented elevated anxiety, internalizing problems, externalizing problems and total problems scores; likewise, their gut microbiota composition at 18 months of age showed positive correlation with behavioral problems at 3.5 years: Actinobacteria abundance and somatic complaints and between Fusobacteria abundance and withdrawn behavior and pervasive developmental problems. ### Conclusions Our findings suggests that OV/OB and/or GDM during pregnancy is associated with higher behavioral problems scores in children at 3.5 years old. Additionally, associations between early life gut microbiota composition and later mental health in children was also found. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13034-023-00589-9. ## Introduction Women of childbearing age have not been spared in the obesity epidemic worldwide. In fact, maternal obesity or overweight are among the most common conditions present during pregnancy in the developed world, affecting up to $47\%$ of women [1]. Obesity in pregnancy has been associated with an increased risk of serious adverse outcomes, including miscarriage, fetal congenital anomaly, thromboembolism, gestational diabetes mellitus (GDM), preeclampsia, postpartum hemorrhage (PPH), wound infections, stillbirth and neonatal death and a lower breastfeeding rate [1–3]. The consequences of fetal exposure to the intrauterine conditions associated with maternal obesity may also extend to the neonate, as infants born to obese women appear to be at increased risk for a number of congenital anomalies, including neural tube defects and macrosomia [4, 5]. Additionally, it is important to note the effect of “nutrition programming” [6]. In fact, there is increasing evidence about the influence of maternal nutritional status and diet during pregnancy and infant type of feeding during the first months of life on child development and health in later adulthood [7]. Early programmed effects on anthropometric, metabolic, and neurological development have been suggested [8–10]. In this regard, milk from obese or gestational diabetic mothers presented changes in the concentration of several bioactive components [11] that could influence short- and long-term infant health status [12]. Furthermore, several studies have linked maternal and child weight through the transmission of maternal commensal microbiota, which is probably colonized during gestation [13–15]. Interestingly, Guilley et al. examined maternal human milk oligosaccharides (HMOs) between overweight/obese and normoweight mothers and observed an alteration in their composition. Furthermore, offspring from obese mothers presented a lower abundance of short-chain fatty acid (SCFA)-producing bacteria and lower fecal butyric acid levels, with a prediction of elevated adiposity at 12 months [16]. It follows that maternal obesity might also “program” offspring for lifelong obesity and associated metabolic and mental disorders [9, 17–19]. On the other hand, GDM is estimated to affect up to $14\%$ of pregnancies [20] and is increasing worldwide in the wake of the increase in obesity [21]. Maternal consequences include an increased rate of hypertensive disorders during pregnancy and a future risk of type 2 diabetes mellitus (T2DM) as well as other aspects of metabolic syndrome, such as obesity, cardiovascular morbidities, recurrent GDM [1, 22] and a variation in the gut microbiota composition of the offspring during the first week of life and 9 months after birth [23]. Moreover, there are maternal complications secondary to delivering a neonate that is macrosomic or large for gestational age (LGA), such as an increased rate of cesarean delivery, postpartum hemorrhage and birth trauma [22]. The WHO [24] estimates that one in five children and adolescents experience mental health problems, which are known to predict other negative outcomes in later life, including noncompletion of schooling, physical health problems, drug and alcohol misuse, marital difficulties, increased mortality, injury risk, and involvement in the criminal justice system [25–28]. According to several authors, many risk factors during the prenatal period (a critical window for later behavioral development) could contribute to the development of future mental diseases [29–31]. Research also demonstrates the importance of environmental influences (e.g., prenatal and perinatal risk factors) on the causation of externalizing behavior [32]. One reason that may cause an increase in these risk factors is the inflammatory milieu present in maternal obesity and GDM during gestation [33, 34]. It has been proven that a fetus maturing under those conditions (obesity and/or GDM) has a higher risk of developing different neurodevelopmental disorders during childhood [35], such as attention problems, hyperactivity and anxiety [36–39]. Furthermore, adverse birth outcomes, including low birth weight and preterm birth, exposure during pregnancy to maternal smoking, alcohol consumption, GDM and psychological stress, have been identified as the most important pre- and perinatal factors associated with attention-deficit/hyperactivity disorder (ADHD) [40–42]. In a recent review, the association between maternal obesity and neurodevelopmental and psychiatric morbidity in offspring was investigated [43]. Brion et al. found significant associations between maternal overweight and externalizing and total problems at 3 years old in the Dutch Generation R cohort but not in the Avon Longitudinal Study [44]. Furthermore, Rodríguez et al. reported that a higher maternal prepregnancy body mass index (BMI) (overweight and obesity) was associated with core symptoms of ADHD in school-age children [45]. On the other hand, one hypothesis to explain the influence of maternal weight on their children is the transmission of obesogenic microbes from the mother to her offspring [13]. In this connection, several studies have shown that maternal prepregnancy obesity imprints a selective gut microbial composition during late infancy [46, 47]. During the first 3 years of life, children’s brains growing rapidly parallel to deep gut microbiota establishment and development through communication along the “gut-brain axis” has been postulated as one plausible mechanism influencing infant neurodevelopment [48, 49]. Experiments in animal models have shown that the maturation process of the gut microbiota coincides with intense synaptogenesis and pruning in the cerebral cortex, ending in adolescence [50–52]. Therefore, it is increasingly considered that gut microbes are part of the unconscious system influencing early neurodevelopment with potential later psychiatric expressions. In this regard, the theory that health across the lifespan is shaped during early sensitive windows, known as the developmental origins of health and disease, is a focus of pediatric molecular epidemiology, and the microbiome likely plays a crucial role in the most significant problems of behavior, such as externalizing (aggression, conduct problems, hyperactivity and inattention) and internalizing problems (emotional and affective problems, anxiety and depression) at 3 to 5 years old. Studying early biomarkers and symptomatology is of crucial importance because it could help to understand the underlying etiology of behavioral problems in preschool children [53, 54]. The objective of this study was to evaluate the influence of preconceptional maternal BMI and/or GDM on child behavior at 3.5 years old and to identify other possible influencing factors, including early gut microbiota composition and functionality and breastfeeding. ## Study design and subjects The PREOBE study design, the characteristics of the pregnant women and their compliance have been described previously [18]. Briefly, 331 pregnant women were recruited between 2008 and 2012 through collaboration with the Clinical University Hospital San Cecilio and the Mother–Infant University Hospital of Granada, Spain, and their peripheral health centers. The inclusion criteria were single pregnancy at 12 to 34 weeks of gestation (before 20 weeks), aged 18 to 45 years, no simultaneous participation in another study, no drug treatment, no vegan diet, and no diagnosed diseases other than obesity, overweight or GDM. Based on their prepregnancy BMI, pregnant women were assigned to one of the following three groups: 1. normoweight (18.5 ≤ BMI < 25), $$n = 71$$; 2. overweight (25 ≤ BMI < 30), $$n = 45$$; and 3. obese (BMI ≥ 30, $$n = 40$$) (Fig. 1). Women who developed GDM during pregnancy remained in their group depending on prepregnancy BMI as follows: 1. normoweight group with GDM: $$n = 20$$; 2. overweight with GDM: $$n = 14$$; and 3. obese with GDM: $$n = 14$$ (Fig. 1). According to the hospital routines, mothers diagnosed with GDM were invited to participate in an endocrine nutritional program to optimize glucose control using nutritional and lifestyle recommendations and, in some cases, medical treatment. Overweight and obese mothers without GDM received no intervention or dietary recommendations except the regular ones. Fig. 1This figure shows drop-outs since delivery to 3.5 year old. D = drop-outs and E = exclusions (1 infant at delivery due to congenital heart disease and one before the six months follow up due to severe immunodeficiency). CBCL = Child Behavior Checklist; GDM = Gestational diabetes mellitus ## Ethics, consent, and permissions Ethical approval was obtained from the Research Ethics Committee of the University of Granada. The Bioethical Committees for Clinical Research of the Clinical University Hospital San Cecilio and the Mother–Infant University Hospital of Granada, Spain, approved the project. A member of the research group provided full information about the project, and written informed consent was obtained from all women prior to entering the study. The project was registered at www.ClinicalTrial.gov (no.: NCT01634464). ## Data collection during pregnancy In this study, baseline and background characteristics of pregnant women and their offspring, including maternal age, weight gain during pregnancy, preconceptional maternal weight, height, and BMI, maternal education level, maternal intelligence quotient (IQ) assessed with Catell’s Culture Fair Intelligence Test (g factor) [55, 56], smoking habit and alcohol consumption during pregnancy, gestational age at delivery, neonatal anthropometric measures (birth weight, length and head circumference), Apgar scores, family situation, type of delivery, baby gender, and parity, were collected using questionnaires and medical records. In addition, at three months of age, mothers were interviewed by an expert pediatrician about infant diet, which was categorized as breastfeeding, formula feeding or mixed feeding. ## Child behavior checklist (CBCL) Parents completed the Child Behavior Checklist for Ages 1½–5 (CBCL) (Spanish validated version) [57] when their children were 3.5 years old. This test includes 101 items divided into two scales (“internalizing problems” and “externalizing problems”) and a total score. The internalizing problems scale is divided into four syndrome subscales: emotionally reactive behavior, anxiety/depression, somatic complaints, and withdrawn behavior. The externalizing problems scale is divided into two syndrome subscales: attention problems and aggressive behavior. Additionally, it assesses sleep, affective problems, anxiety, pervasive developmental problems, ADHD and oppositional defiant problems. The sum of the items of each CBCL scale provides a score that becomes the basis for the assignation of the children into one of the following three groups: normal, borderline and clinical (pathological) [57]. ## DNA Extraction from stool samples Genomic deoxyribonucleic acid (DNA) was extracted from the fecal bacteria of these infants ($$n = 64$$) at 18 months of age, as previously described [58]. Briefly, fecal samples were resuspended in 1 mL of TN150 buffer (10 mM Tris–HCl with a pH of 8.0 and 150 mM NaCl). Zirconium glass beads (0.3 g) and 150 mL of buffered phenol were added, and bacteria were disrupted with a mini bead beater set to 5000 rpm at 4 °C for 15 s (Biospec Products, Bartlesville, OK, USA). After centrifugation, genomic DNA was purified from the supernatant using a phenol‒chloroform extraction. Quality was checked by agarose gel electrophoresis and quantified with a QuantiT PicoGreen dsDNA assay kit (Invitrogen, Darmstadt, Germany). ## 16S rRNA gene sequencing and data processing Genomic DNA from fecal bacteria was used as a template for 16S ribonucleic acid ribosomal (rRNA) gene amplification using 27F and 338R universal primers and two consecutive polymerase chain reactions (PCRs) to integrate Illumina multiplexing sequences as previously described [59]. The library was prepared by pooling equimolar ratios of amplicons and was sequenced using an Illumina MiSeq platform (Genetic Service, University of Granada, Granada, Spain). Reads were demultiplexed and sorted, and paired ends were matched to yield 240 nt reads. The dataset was filtered, and OTUs were defined at $99\%$ similarity with the MOTHUR programs unique.seqs and pre.cluster [60]. Taxonomic classifications of OTUs were assigned using the naïve Bayesian algorithm CLASSIFIER of the Ribosomal Database Project [61]. OTUs were considered unassigned when the confidence value score was lower than 0.8 and were annotated using upper taxonomic ranks. ## Statistical analysis All statistical analyses were performed using the SPSS statistical software package for Windows (version 24.0; IBM SPSS Inc., Chicago, IL, USA). Normally distributed variables are presented as the mean and standard deviation (SD), and nonnormal variables are presented as the median and interquartile range (IQR). Categorical variables are shown as frequencies and percentages. Differences in CBCL scores among the three PREOBE groups were evaluated using an analysis of variance (ANOVA) or Kruskal‒Wallis rank-sum test for nonnormal continuous variables and a chi-square or Fisher’s test for categorical variables. To determine differences according to the development of GDM, a univariate analysis of variance was performed. When significant differences were found between groups, a posteriori Bonferroni correction was used to identify significant pairwise group differences. A logistic regression model (enter method) was used to calculate odds ratios (ORs) and $95\%$ confidence intervals (CIs) of having a normal/clinical pathologic value in the different groups of study using the normoweight group as a reference. Moreover, variables showing significant group differences were adjusted to univariate general linear models (ANCOVA) and multivariate logistic regression models. Spearman’s correlation analyses were performed to estimate the associations between preconceptional maternal BMI, breastfeeding, and child behavioral problems. In a secondary analysis, to establish the influence of confounder variables regardless of group, we used a logistic regression (Wald test). For the appropriate analysis of gut microbiota compositional data, we performed all statistical analyses and graphical representations in R [62] using the packages tidyverse [63], corrplot [64], psych and stats [65]. The contribution of metadata variables to microbiota community variation was determined by distance-based redundancy analysis (dbRDA) on genus-level Aitchison distance (Euclidian distance between samples after centered log-ratio transformation, as recommended for compositional data) with the capscale function in the vegan R package [64]. Correlations between gut microbial composition and CBCL scale were identified using Spearman’s nonparametric correlation test. The Bonferroni procedure (FDR < 0.01) was used to correct for multiple testing in correlations between taxa and CBCL scores. Associations between taxa and CBCL scores after screening the effect of microbiota covariates were assessed by fitting a generalized linear model (GLM) with the glm R function. Unless otherwise stated, the significance cutoff was set at $p \leq 0.05$ or false discovery rate (FDR) < 0.05 when multiple test correction was applied. ## Characteristics of the PREOBE study participants at 3.5 years old Of the 331 pregnant women included in the PREOBE study, 156 were included in the present study after exclusions and dropouts. Children’s behavior was assessed at 3.5 years old using the CBCL test (Fig. 1). Background and baseline characteristics of the mothers and their offspring are shown and compared among the three PREOBE groups in Table 1. Statistical analysis revealed significant differences between study groups in weight gain during pregnancy, maternal educational level and maternal IQ. In fact, normoweight pregnant women presented more weight gain during pregnancy than obese pregnant women ($$p \leq 0.008$$). Moreover, normoweight pregnant women showed a higher cultural level than obese pregnant women ($$p \leq 0.001$$) and higher IQ than overweight pregnant women ($$p \leq 0.025$$). On the other hand, obese mothers had an increased risk of GDM ($35\%$) compared with overweight ($31.11\%$) or normoweight mothers ($28.17\%$), although the difference was not significant. Finally, differences in neonatal birth weight were marginally significant among groups ($$p \leq 0.065$$), as was the type of feeding at 3 months ($$p \leq 0.060$$). The skewed variables were included in the analyses. Table 1General characteristics of the PREOBE mother–child pairs who participated in the children behavioral follow up at 3.5 years of ageNormal weight ($$n = 71$$)Overweight ($$n = 45$$)Obese($$n = 40$$)P1Maternal age (y)231.93 ± 3.9032.69 ± 4.5631.55 ± 5.430.489Pre-conceptional BMI (kg/m2)222.09 ± 1.72a27.31 ± 1.32b34.47 ± 4.19c < 0.001GWG (kg)211.32 ± 6.50a9.95 ± 6.07a,b6.64 ± 7.58b0.008GDM3Yes$28.17\%$$31.11\%$$35.00\%$0.754Parity$3055.71\%$$52.27\%$$40.00\%$0.275 > $144.29\%$$47.73\%$$60.00\%$Family situation3Single/Separate$1.41\%$$2.27\%$$5.00\%$0.290Lives in partnership/ Married$98.59\%$$97.73\%$$92.50\%$Others$0.00\%$$0.00\%$$2.50\%$Maternal Educational level3Primary/Secondary$39.44\%$a$61.36\%$a,b$75.00\%$b0.001University$60.56\%$a$38.64\%$a,b$25.00\%$bMaternal IQ (points)2108.87 ± 12.16a102.16 ± 13.73b106.11 ± 12.51a,b0.025Paternal IQ (points)2106.08 ± 12.80101.71 ± 12.61106.53 ± 12.310.286Smoking during pregnancy3No$85.71\%$$90.00\%$$91.43\%$0.746Yes$14.29\%$$10.00\%$$8.57\%$Alcohol consumption during pregnancy3No$95.24\%$$100\%$$94.29\%$0.384Yes$4.76\%$$0.00\%$$5.71\%$Type of delivery3Eutocia$67.92\%$$54.17\%$$73.08\%$0.156Dystocia$15.09\%$$12.50\%$$0.00\%$Cesarean$16.99\%$$33.33\%$$26.92\%$NeonateGestational age (wks)239.58 ± 1.1639.37 ± 1.4339.87 ± 1.380.218Birth weight (g)23329 ± 3853269 ± 5163492 ± 4810.065Birth length (cm)250.53 ± 1.4850.00 ± 1.9851.01 ± 2.410.119Apgar 1′49[0]9[0]9[0]0.262Apgar 5′410[0]10[0]10[0]0.978Sex3Boy$49.30\%$$48.89\%$$60.00\%$0.495Girl$50.70\%$$51.11\%$$40.00\%$Type of feeding at 3 months3Breast-feeding$61.19\%$$58.97\%$$37.50\%$0.060Mixed$16.42\%$$17.95\%$$15.00\%$Infant formula$22.39\%$$23.08\%$$47.50\%$GWG Gestational Weight Gain, GDM Gestational Diabetes Mellitus, P Level of significance for overall differences between PREOBE-groups. 1Mean ± Standard Deviation for normally distributed continues variables; 2Percentage for categorical variables; 3Median and Interquartile Range for non-normally distributed continuous variablesStatistical analysis performed: ANOVA: Analysis of variance for normally distributed variables. Kruskal–Wallis rank-sum test for non-normal continuous variables. Chi-square test for categorical variables. Values who do not share the same suffix (abc) are significantly different in a Bonferroni post hoc test. Bold: p-value < 0.05 ## Effects of maternal prepregnancy BMI on children’s CBCL scores at 3.5 years old The effects of maternal prepregnancy overweight or obesity on children’s CBCL scores at 3.5 years of age are shown in Table 2. We found that children born to overweight mothers showed higher scores in anxiety ($$p \leq 0.027$$) and total problems ($$p \leq 0.039$$) than children born to normoweight mothers, although significance was lost after adjustment for confounders (weight gain during pregnancy, maternal educational level and maternal IQ). Moreover, differences between children’s scores were seen in anxiety/depression ($$p \leq 0.044$$) and internalizing problems ($$p \leq 0.048$$); however, the p values did not remain significant after a Bonferroni post hoc test ($$p \leq 0.083$$ and $$p \leq 0.199$$, respectively). Additionally, the results for emotionally reactive and externalizing problem scores were not significant ($$p \leq 0.056$$ and $$p \leq 0.062$$, respectively), even after adjustment for confounding variables ($$p \leq 0.065$$ and $$p \leq 0.199$$, respectively).Table 2Effects of maternal pre-pregnancy BMI on children’s CBCL scores at 3.5 years of ageCCL Scores at 3.5 yearsNormal weight ($$n = 71$$)Overweight ($$n = 45$$)Obese($$n = 40$$)PunadjPadjEmotionally reactive54.80 ± 5.8557.51 ± 6.7456.83 ± 6.400.0560.065Anxious/depressed54.68 ± 5.54a57.47 ± 7.36a56.65 ± 6.18a0.0440.083Somatic complaints55.37 ± 6.4557.56 ± 7.7256.68 ± 6.910.2420.640Withdrawn56.48 ± 6.5258.98 ± 7.8856.13 ± 6.310.0980.532Sleep problems55.92 ± 8.1057.33 ± 8.0655.75 ± 6.150.5470.296Attention problems53.66 ± 5.0554.96 ± 4.7154.20 ± 4.600.3760.725Aggressive behaviour53.46 ± 3.9655.31 ± 5.9055.30 ± 5.250.1370.234Internalizing problems52.99 ± 9.00a57.27 ± 10.16a55.10 ± 9.40a0.0480.199Externalizing problems50.25 ± 7.5353.29 ± 7.9153.05 ± 7.600.0620.345Total problems52.04 ± 8.11a56.04 ± 9.17b54.90 ± 8.85a,b0.0390.243Affective problems55.93 ± 6.1158.13 ± 7.5256.75 ± 6.090.2130.387Anxiety problems54.83 ± 6.44a58.33 ± 8.91b57.88 ± 7.85a,b0.0270.472Pervasive developmental problems56.21 ± 6.4458.78 ± 7.6456.85 ± 7.130.1530.913Attention deficit/ hyperactivity problems53.35 ± 4.7354.98 ± 4.9155.10 ± 5.500.1140.758Oppositional defiant problems53.63 ± 4.2254.78 ± 6.3055.90 ± 5.800.2410.378Data are expressed as Mean ± Standard DeviationPunadj = Level of significance unadjusted—Analysis of variance (ANOVA). Values not sharing the same suffix (ab) were significantly different in a Bonferroni post hoc testPadj = Level of significance adjusted by potential confounders—Analysis of covariance (ANCOVA) for the group differences using univariate general linear model including main effects from the following possible confounder: Weight gain during pregnancy, maternal educational level and maternal IQ ($$n = 156$$)Bold: p-value < 0.05 Subsequently, we categorized the CBCL children’s scores according to normal, borderline and clinical pathology outcomes (Table 3). In general, children born to overweight or obese mothers more frequently showed scores classified as borderline or clinical pathology. The analysis showed that children born to overweight mothers presented more borderline anxiety/depression ($$p \leq 0.018$$) and clinical pathology such as externalizing problems ($$p \leq 0.020$$) and total problems ($$p \leq 0.031$$) than children born to healthy normoweight mothers. Concerning oppositional defiant problems, children born to normoweight mothers were more frequently classified as normal than children born to overweight mothers ($$p \leq 0.008$$). Furthermore, children born to obese mothers presented more borderline anxiety problems ($$p \leq 0.006$$) than those born to healthy normoweight mothers. Table 3Effects of maternal pre-pregnancy overweight or obesity on children’s CBCL scores at 3.5 years of age compared to those born to healthy normoweight pregnant women (controls)Normal weight ($$n = 71$$) (%)Overweight ($$n = 45$$) (%)Obese($$n = 40$$) (%)pEmotionally reactiveNormal87.3277.7885.000.276Borderline12.6822.2212.50Clinical Pathology0.000.002.50Anxious/depressedNormal92.96a75.56b90.00a,b0.018Borderline4.23a22.22b10.00a,bClinical Pathology2.822.220.00Somatic complaintsNormal88.7377.7877.500.210Borderline8.4513.3320.00Clinical Pathology2.828.892.50WithdrawnNormal94.37a80.00a90.00a0.039Borderline0.00a2.22a5.00aClinical Pathology5.63a17.78a5.00aSleep problemsNormal88.7382.2295.000.303Borderline1.414.442.50Clinical Pathology9.8613.332.50Attention problemsNormal92.9695.5697.500.945Borderline5.634.442.50Clinical Pathology1.410.000.00Aggressive behaviourNormal98.5988.8995.000.055Borderline1.4111.115.00Clinical Pathology0.000.000.00Internalizing problemsNormal76.0651.1167.500.055Borderline8.4515.565.00Clinical Pathology15.4933.3327.50Externalizing problemsNormal94.37a77.78b77.50b0.020Borderline2.826.6712.50Clinical Pathology2.82a15.56b10.00a,bTotal problemsNormal84.51a60.00b65.00a,b0.031Borderline7.0411.1110.00Clinical Pathology8.45a28.89b25.00a,bAffective problemsNormal90.1484.4492.500.378Borderline5.632.222.50Clinical Pathology4.2313.335.00Anxiety problemsNormal92.96a80.00a,b75.00b0.006Borderline0.00a0.00a,b10.00bClinical Pathology7.0420.0015.00Pervasive developmental problemsNormal85.9271.1182.500.393Borderline8.4517.7810.00Clinical Pathology5.6311.117.50Attention deficit/ hyperactivity problemsNormal95.7795.5690.000.366Borderline2.822.2210.00Clinical Pathology1.412.220.00Oppositional defiant problemsNormal100.00a88.89b92.50a,b0.008Borderline0.006.677.50Clinical Pathology0.004.440.00Data are shown as percentages and p-values were obtained after Chi square test. Values who do not share the same suffix (ab) are significantly different in a Bonferroni post hoc test. Bold: p-value < 0.05 ## Impact of GDM on children’s behavioral problems at 3.5 years old Additionally, we studied the impact of the development of GDM in overweight, obese or normoweight pregnant women on children’s CBCL scores. The statistical analysis revealed significant differences only in offspring of the obese mothers group, as the children born to obese mothers with GDM presented higher scores in aggressive behavior ($$p \leq 0.008$$) and oppositional defiant problems ($$p \leq 0.004$$) than the children born to obese mothers without GDM (see Additional file 1: Table S1). Moreover, when we performed the analysis of CBCL scores by categorizing into clinical clusters, we found that the children born to obese mothers with GDM presented more clinical pathology regarding externalizing problems ($$p \leq 0.015$$) and more borderline ADHD symptoms ($$p \leq 0.011$$) than the children born to obese mothers without GDM (see Additional file 2: Table S2). ## Effects of maternal metabolic status and potential confounders on behavioral development in PREOBE children at 3.5 years of age Logistic regression models, calculating the OR and $95\%$ CI for CBCL scores of children born to obese and overweight groups, respectively, against the normoweight group as a reference, are presented in Additional file 3: Table S3. We found an increased risk in children born to overweight and obese mothers vs. those children born to normoweight mothers of having externalizing problems (OR 4.786, $95\%$ CI 1.400–16.364, $$p \leq 0.013$$; OR 4.863, $95\%$ CI 1.390–17.014, $$p \leq 0.013$$, respectively), total problems (OR 3.636, $95\%$ CI 1.513–8.740, $$p \leq 0.004$$; OR 2.937, $95\%$ CI 1.178–7.326, $$p \leq 0.021$$, respectively) and anxiety problems (OR 3.300, $95\%$ CI 1.028–10.592, $$p \leq 0.045$$; OR 4.400, $95\%$ CI 1.383–13.994, $$p \leq 0.012$$, respectively). A similar increased risk was seen in children born to overweight vs. normoweight mothers regarding the anxiety/depression score (OR 4.271, $95\%$ CI 1.372–13.289, $$p \leq 0.012$$), withdrawn behavior score (OR 4.187, $95\%$ CI 1.205–14.550, $$p \leq 0.024$$) and internalizing problems score (OR 3.038, $95\%$ CI 1.366–6.757, $$p \leq 0.006$$). Nevertheless, when the logistic regression models were adjusted by confounding factors (weight gain during pregnancy, maternal educational level and maternal IQ), these differences disappeared. ## Correlation analysis between gut microbiota composition in children at 18 months of life and their CBCL scores at 3.5 years We initially examined gut microbiota covariation with CBCL scales in the context of known microbiota covariates, which included maternal age, smoking during pregnancy (yes/no), alcohol consumption during pregnancy (yes/no), neonate weight, gender, type of delivery (C-section or vaginal), gestational diabetes (yes/no), maternal pregestational BMI groups (normoweight, overweight and obese) and type of feeding up to the third month. We determined the proportion of interindividual variation in microbiota composition explained by covariates using a dbRDA at the genus level with Aitchison distance. This confounder analysis revealed a significant association with maternal pregestational BMI, which was considered in downstream statistical analyses to avoid covariate effects (Fig. 2).Fig. 2Principal component analysis based on Aitchison distance of microbial community composition in 18-moths old infants grouped by mother pre-gestational BMI: normoweight, overweight and obese. Each colored point represents a sample. Distance between samples on the plot represents differences in microbial community composition at genus level, with $17.2\%$ of total variance being explained by the first two components Next, we identified associations of infant gut microbiota at the genus level with CBCL scales at 3.5 years (Spearman’s correlation, Bonferroni FDR < 0.01, Fig. 3). The internalizing problems scores were negatively correlated with Fusicatenibacter and Butyricicoccus within Firmicutes. Within the subscales, the anxiety/depression and somatic complaints scores were positively correlated with an unclassified genus in Clostridiales (unclass_Clostridiales) and with Flavonifractor, respectively, both belonging to Firmicutes. Somatic complaints, emotionally reactive problems, and withdrawn behavior scores were negatively correlated with Fusicatenibacter. On the “externalizing problems scale”, the aggressive behavior scores were negatively correlated with Barnesiella (Bacteroidetes) and Ruminococcus (Firmicutes). On independent scales, the sleep problems scores were negatively correlated with Megasphaera (Firmicutes). Attention deficit hyperactivity problems and aggressive behavior scores were negatively correlated with Barnesiella. Pervasive developmental problems scores were negatively correlated with Fusicatenibacter. Interestingly, the overall total problem scores were negatively correlated with Fusicatenibacter and Butyricicoccus. Fig. 3Heatmap diagram of significant correlations between gut microbial composition and CBCL scale. Green squares represent significant associations by fitting a generalized linear model (GLM) between taxa and CBCL scores after partially out the effect of microbiota covariables Fitting GLMs between CBL subscales and microbial genera (FDR < 0.05; Fig. 3) while screening the maternal pregestational BMI covariate contribution confirmed significant correlations between the abundances of three genera and CBCL scores. An inverse association with mental health problems remained largely significant for the Fusicatenibacter genus. Negative correlations between Fusicatenibacter abundances were confirmed with total problems, internalizing problems and its subscales somatic complaints and withdrawn behavior, and pervasive developmental problems. Positive correlations between the abundances of unclass_Clostridiales and Flavonifractor and anxiety/depression and somatic complaints subscales, respectively, remained significant. ## Influences of maternal pregestational BMI on breastfeeding and their consequences on behavioral development in PREOBE children at 3.5 years of age and gut microbiota composition at 18 months Finally, we tested whether maternal pregestational BMI had any effects on breastfeeding and its association with later behavioral development. Remarkably, Spearman’s correlation analysis revealed that higher maternal pregestational BMI was associated with higher anxiety (rs = 0.321; $$p \leq 0.003$$), internalizing (rs = 0.291; $$p \leq 0.006$$), externalizing (rs = 0.255; $$p \leq 0.018$$) and total problems (rs = 217; $$p \leq 0.045$$) in their children at 3.5 years old only in the group of children who were breastfed during their first 3 months of life. No correlations were found in the group of children fed with infant formula or mixed feeding (Fig. 4).Fig. 4Spearman correlation analysis between pre-conceptional maternal BMI and anxiety (A), internalizing (B), externalizing (C) and total (D) problems. rs: correlation coefficient Rho Spearman To identify the possible gut bacteria that could influence these results, we analyzed the gut microbiota composition of children at 18 months born to obese/overweight mothers who were exclusively breastfed during the first 3 months of life. At the phylum level, we detected a positive correlation between Actinobacteria and somatic complaints, Fusobacteria and withdrawn behavior and Fusobacteria and pervasive developmental problems. At the family level, 6 families (Coriobacteriaceae, Leuconostocaceae, Ruminococcaceae, Unclass_Firmicutes, Fusobacteriaceae and Streptococcaceae) were positively correlated with somatic complaints, withdrawn behavior, sleep problems, internalizing problems, pervasive developmental problems, total problems and anxiety/depression. Finally, we detected positive and negative correlations between all CBCL subscales and several genera, highlighting a positive correlation between Flavonifractor and sleep problems and negative correlations between Fusicatenibacter and somatic complaints, affective problems and pervasive developmental problems (Fig. 5).Fig. 5Heatmap diagram of significant correlations between gut microbial composition from children born to obese mothers with breastfeeding and CBCL scale ## Discussion Obesity before and during pregnancy is the most important cause of increased systemic inflammation in the fetal compartment, and exposure to a proinflammatory milieu might produce alterations in brain structure and later neurocognitive function and psychopathology [66–68]. Experimental studies in animals concluded that exposure to high levels of proinflammatory cytokines produced negative consequences in neurogenesis, apoptosis, neurotransmitter levels and myelination [69], leading to a decrease in gray and white matter volumes, abnormal development of fetal brain and later behavioral problems [70], and even increased risk for neuropsychiatric disorders [71]. In this regard, some studies in animals showed that the offspring of mothers who are obese during pregnancy showed reduced central serotonergic and dopaminergic signaling in brain regions (nucleus accumbeus, hippocampus and prefrontal cortex) associated with cognitive development and psychiatric problems in humans [72–74]. In the present study, we provide evidence that children born to obese and overweight mothers presented higher scores in the CBCL test and increased risk for developmental behavior problems at 3.5 years old than those born to healthy normoweight mothers. Recently, a meta-analysis supported the idea that offspring born to mothers who were overweight or obese prior to pregnancy have an increased risk of compromised neurodevelopmental outcomes, such as ADHD, autism spectrum disorder developmental delay and emotional/behavioral problems [39]. Moreover, Jo. et al. determined that children whose mothers were severely obese before pregnancy had an increased risk of unfavorable development, such as emotional symptoms, peer problems, ADHD, autism or developmental delay [75]. Another study showed that learning and behavioral disabilities, as well as ADHD, autism, pervasive development disorder, oppositional defiant disorder or another developmental delay, occurred more often in children who were born to severely obese mothers [76]. In addition, Mina et al. showed that 3- to 5-year-old children born to very severely obese mothers have higher scores for externalizing and total problems, anxiety/depression, aggressive behavior and other syndromes and more DSM-oriented affective problems, anxiety and ADHD problems in CBCL [77]. Affective problems, including major depressive disorder and dysthymic disorder, are the most common mental problems in children and adolescents [78, 79]. Recent studies have shown that factors present in fetal life could affect the development of common mental disorders, such as depression [80, 81]. Empirical data suggest that obesity and metabolic disorders, such as GDM, are associated with a higher risk of depression. Recent studies suggest a possible link between maternal metabolic conditions and programming of the fetal brain in the uterus, which could predispose the fetus to suffering from emotional problems in childhood [82]. Robinson et al. confirmed the relationship between maternal prepregnancy BMI (obesity and overweight) and the development of affective and emotional problems [78]. Additionally, maternal prepregnancy obesity and overweight were found to predict a higher risk for inattention and emotional regulation problems when children were 5 years old [83]. Moreover, interestingly, our results indicate that children born to obese mothers with GDM presented higher scores in psycho-behavior problems than children born to obese mothers without GDM or overweight/healthy normoweight mothers who developed GDM. Therefore, maternal diabesity represents the worst-case scenario for the fetus, increasing the odds of developing behavioral problems during childhood. Our results seem to be in agreement with other previously published studies showing that lower IQ, language disability, attention problems, impulsivity, and behavioral problems are linked to GDM [84, 85]. In addition, other studies found that children of diabetic mothers had higher rates of ADHD symptoms [42, 86]. The intrauterine conditions associated with childhood obesity are characterized by a series of prenatal factors, such as suboptimal maternal diet and/or nutritional deficiencies, diabetes mellitus, psychosocial stress, increased levels of proinflammatory cytokines, and obstetric complications. These factors are associated with deficits in brain growth in the offspring and are attributed to fetal programming, including brain inflammation, behavioral alterations in the offspring and later mental health [31, 87, 88]. The maturing brain is a target for these environmental insults, and anything affecting the developing brain, such as elevated circulating levels of glucose or fatty acids, has the potential to determine childhood and adult behavior [89, 90]. Particularly, in obese and diabetic pregnant women, an increased inflammatory milieu appears during gestation that might represent a biological condition involved in the genesis and development of behavioral and psychological problems in the offspring. Among other possible mechanisms proposed to underlie the risk of neurodevelopmental morbidity are the dysregulation of leptin signaling in the developing brain, serotonergic and dopaminergic signaling and impaired reward circuitry or alteration of brain-derived neurotrophic factor-mediated synaptic plasticity [43]. Psycho-behavior research has provided evidence of an association between maternal prepregnancy BMI and emotional and behavioral problems in children [91], and a higher risk of externalizing (ADHD and aggressive behavior) and internalizing problems are related to prepregnancy adiposity [92]. However, as the higher risk for externalizing problems appears early during development, some recent research has shown the increasing risk for internalizing problems later in life. It is not yet clear whether the delayed expression of internalizing symptoms is caused by stress linked to externalizing problems in early life, programming of internalizing problems during pregnancy or heterotypic continuity [87]. Nevertheless, our data showed higher scores in internalizing symptoms such as anxiety/depression and externalizing problems in children 3.5 years old born to overweight and obese mothers with GDM, respectively, suggesting that psycho-emotional and behavioral problems may be programmed in early life. It is important to note that maternal psychosocial variables, such as maternal IQ, educational level, and breastfeeding, were associated with lower psycho-behavioral problems, influencing child behavior development more frequently. There is increasing evidence on the influence of the type of feeding during the first months of life on infant development as well as on later health in adult life[7]. During the first 1000 days of life, the intensity of growth makes the brain particularly vulnerable to adverse nutritional stimuli and has a direct impact on cognitive and behavioral development [93], and this period is a critical window in the establishment of the gut microbiota community, which has been linked with later neurodevelopment skills and obesity status [94]. Breastfeeding is the gold standard of nutrition for optimal development, and the benefits on cognitive function are clear [95], but there are fewer studies about its role in emotional regulation and behavior development. In this regard, breastfeeding has been associated with a lower risk of behavioral problems in childhood. Parker et al. found that breastfeeding absence was associated with increased internalizing, externalizing, and overall behavioral problems as well as the diagnosis of ADHD [96]. In another study, children who were breastfed and whose mothers actively engaged with them showed the lowest risk of internalizing problems at 6 years old [97]. However, some studies have not found an association between breastfeeding and behavioral problems during early childhood [98]. Kwok et al. found inconsistent associations between breastfeeding and several early adolescent mental health factors, where confounding factors, such as socioeconomic status and maternal educational level, play an important role in the establishment of a good behavioral system and mental health [99]. Along these lines, several studies have shown that a large number of social and parental educational factors might influence child development independently of the breastfeeding effect [95]. Wigg et al. noted that when confounders such as social advantage, maternal education and intelligence, and the quality of a child’s developmental experiences were taken into account, the differences between bottle-fed and breastfed groups disappeared [100]. Most likely, there is an association between social, genetic and nutritional factors that could be essential for optimal brain development. The benefits of breastfeeding could be related to a stronger psychological attachment between mother and child neurodevelopment and not only to breast milk intake. Quinn et al. found that children who were breastfed presented better scores in language than formula-fed children, but this association was reduced though remaining significant when a large number of confounding social and parental factors were considered [95]. In this sense, milk from obese or gestational diabetic mothers might contain a lower abundance of protective factors than milk from normoweight mothers [101, 102]. Likewise, the omega-6/omega-3 ratio is increased, while the fatty acid (docosahexaenoic acid, eicosapentaenoic acid and docosapentaenoic acid) concentration and carotenoid (lutein) concentration are decreased [103]. Interestingly, and according to our results, breastfeeding in the offspring of overweight and obese mothers appears to be related to higher anxiety, internalizing problems, externalizing problems and total problems in children at 3.5 years old. During early childhood, many behavioral problems that determine the development of mental pathologies in adult life, as well as higher rates of school dropout, substance abuse, problems with justice and suicide, can be identified. The emotional and psychological state of the child might be inferred through internalization problems, such as anxiety, depression, somatic complaints, withdrawn behavior or affective problems [104]. In contrast, externalizing problems manifest as aggressive behavior, opposite defiance or inattention. Thus, there is a clear distinction between externalizing and internalizing disorders. Despite the overlap between both, as children with internalizing problems might have disruptive behaviors with the environment and vice versa, children with externalizing problems could suffer several internal emotional problems. Therefore, it is necessary to perform a behavioral global study to understand its etiology and long-term consequences [105–107]. In addition, advances in 16S rDNA gene sequencing have recently shown the association of the gut microbiota with the pathophysiology of neurological disorders, such as anorexia nervosa, major depressive disorder (MDD), bipolar disorder, anxiety, psychosis, and schizophrenia. Fecal microbiota transplants from patients with psychiatric conditions resulted in the development of behavioral and physiological responses in germ-free mice, suggesting that the gut microbiota may be involved in neurological disorders and may serve as a biomarker[108]. Interestingly, evidence from epidemiological studies indicates that maternal prepregnancy obesity is also associated with increased risks for autism spectrum disorder, cognitive dysfunction, attention-deficit hyperactivity disorder, and other mental disorders [109]. In fact, our previous studies showed that maternal prepregnancy obesity may imprint a selective gut microbial composition during late infancy with distinct functional performances [47], and perhaps this factor would modulate the development of fine motor skills [110]. In the present study, we observed negative correlations between Fusicatenibacter and several CBCL scores. The Fusicatenibacter genus comprises one single cultured Fusicatenibacter species, F. saccharivorans, a strict anaerobic sugar fermenter and producer of propionate and acetate[111]. Dong et al. showed that the abundance of Fusicatenibacter was significantly lower in patients with MDD and general anxiety disorder, where a significant negative correlation between Fusicatenibacter abundance and thyroid hormone FT4 levels was observed[112]. Valles-Colomer et al. identified significant associations between the abundances of 10 genera and quality-of-life scores, including both mental and physical scores, where Fusicatenibacter was positively correlated with quality-of-life scores. Nevertheless, Medawar et al. observed a positive correlation between Fusicatenibacter abundances and unhealthy eating behavior, higher subjective hunger ratings and lower fecal concentrations of propionate and acetate [113]. Another genus linked to neurodevelopmental disorders was Flavonifractor, whose abundances were positively correlated with somatic complaints in our study. While Rothenberg et al. observed a positive correlation between Flavonifractor abundance and mental developmental index scores in children at 3 years of age [114], Luna et al. conducted a study showing higher levels of Clostridiales, including Flavonifractor plautii, in children with autism spectrum and functional gastrointestinal disorders[115]. Likewise, in our study, an unassigned genus within Clostridiales was positively correlated with anxiety/depression scores. Bacteria within Clostridiales are predominant members of the gut microbiota that are more abundant in children with neurodevelopmental disorders[116]. Furthermore, Rhee et al. analyzed the association of serum microbial DNA composition with depressive and anxiety symptoms in patients, observing a positive association between the Desulfovibrionaceae family and Clostridiales Family XIII with the total Beck Anxiety Inventory score[117]. Nevertheless, in adults, Li et al. showed a significant depletion of 6 genera within Clostridiales in multiple psychiatric diseases that was associated with dysfunction in amino acid and carbohydrate metabolism [118]. Our results suggest an association between gut microbiota at 18 months of life and CBCL scores in 3.5-year-old children that varies by population characteristics, type of disorder and timing of microbiota assessment. Further accurate and reliable evidence is needed to clarify the potential role of early life gut microbiota imprinting and maturation in children with neurodevelopmental disorders. On the other hand, to improve prevention and intervention strategies, early detection of psychological and social factors that contribute to the development and maintenance of overweight and obesity, especially during pregnancy, is necessary because of the long-term consequences on children’s health. In this regard, our results highlight the importance of studying the influence of prepregnancy obesity and GDM on children’s future psycho-behavior and central nervous system development, considering their gut microbiota composition as a key modulator. In summary, our current study helps to fill the gap in examining the relationship between prepregnancy weight status and child behavior development and confirms previous results shown in other studies, as there is not enough evidence about the impact of maternal metabolic state on behavior problems in children between 2 and 5 years old, especially in GDM mothers. Moreover, our results provide evidence that children whose mothers are obese and present GDM have an increased risk of developing behavioral problems. Furthermore, we provide evidence that the early gut microbiota composition in infants is a possible behavioral modulator for the future design of preventive strategies. Finally, the aforementioned influences of nutritional and maternal sociodemographic factors might help to clarify the etiology of behavioral problems during childhood. In this regard, it is important to note that human milk of overweight and obese mothers seems to be related to higher behavioral problems in children at 3.5 years old. ## Strengths and limitations The main strength of this study is its longitudinal design, which allowed long-term monitoring. An important issue has been the possibility of performing gut microbiota analysis at 18 months and behavioral assessment in this special cohort, which allowed us to develop these pilot studies to formulate new hypotheses. Furthermore, the presence of a specific group of mothers who developed GDM made it possible to study the effects of GDM development according to preconceptional BMI. The use of CBCL scales, a highly reliable and valid measure of childhood behavior, permitted us to arrive at important conclusions. All of the children evaluated were healthy, and maternal IQ and education level were used as confounding factors. Notably, the present study underlined the importance of optimal implantation of the intestinal microbiota during the first months of life in the development of behavioral alterations in children, which can be associated with different mental illnesses later in life. In addition, this study considers the intestinal microbiota as a potential biomarker of long-term problems related to mental health. Among the limitations of the study, we should note that we have not included data about the mother's mental health status during pregnancy or stress, anxiety or depression, and other important variables such as diet or nutritional deficiencies. Moreover, we had no data on socioeconomic status at the moment of the evaluation, although other studies accounted for it and found it to be a significant predictor of mental health problems in children [119]. In addition, although our results regarding the offspring of obese mothers with GDM are in agreement with previously published studies [42, 86], caution should be taken because of the relatively small sample size of our groups. ## Conclusions According to our results, maternal overweight and obesity during pregnancy are significantly associated with elevated levels of behavior problems. Being obese with GDM during pregnancy increases behavioral problems in the offspring. This effect is not observed in overweight or normoweight mothers. Furthermore, the type of feeding during the first months of life, early gut microbiota composition and maternal psychosocial variables more commonly influence child behavior development. The results link early life gut microbiota composition with later mental health in children and state the importance of maternal metabolic status, suggesting a fetal programming of mental health and different nutritional and environmental factors in the causation of behavioral problems in children. Future research is needed to verify and clarify the mechanisms behind the observed associations. ## Supplementary Information Additional file 1: Table S1. Effects of development of gestational diabetes mellitus on children’s CBCL scores at 3.5 years old. Additional file 2: Table S2. Effects of development of gestational diabetes mellitus on children’s CBCL clinical-clusters at 3.5 years old. Additional file 3: Table S3. Logistic regression models assessing the odds of having the CBCL scores at 3.5 years old. ## References 1. Stang J, Huffman LG. Position of the academy of nutrition and dietetics: obesity, reproduction, and pregnancy outcomes. J Acad Nutr Diet (2016.0) 116 677-691. 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--- title: 'Association of skeletal muscle mass and its change with diabetes occurrence: a population-based cohort study' authors: - Yiting Xu - Tingting Hu - Yun Shen - Yufei Wang - Yuqian Bao - Xiaojing Ma journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC10031974 doi: 10.1186/s13098-023-01027-8 license: CC BY 4.0 --- # Association of skeletal muscle mass and its change with diabetes occurrence: a population-based cohort study ## Abstract ### Background Low muscle mass likely results in reduced capacity for glucose disposal, leading to a significant but under-appreciated contribution to increasing the risk of diabetes. But few prospective studies have investigated the association between the loss of muscle mass and the occurrence of diabetes. We aimed to investigate whether short-term changes in muscle mass affect the incidence of diabetes in a Chinese population. ### Methods This study included 1275 individuals without evident diabetes at baseline. In the baseline and re-examination, individuals completed the risk factors survey and underwent body composition measurement. Muscle mass index was defined as the percentage skeletal muscle mass, which was measured by an automatic bioelectric analyzer. ### Results After a median follow-up of 2.1 years, 142 individuals developed diabetes ($11.1\%$). There was an inverse association between basal skeletal muscle mass index and the risk of diabetes in participants with impaired glucose regulation but not in those with normal glucose tolerance. Multivariate-adjusted hazard ratios for the risk of developing diabetes were 0.85 ($95\%$ CI: 0.74–0.98) and 1.15 ($95\%$ CI: 0.98–1.34), respectively. Furthermore, Cox regression analysis revealed that a two-year change in skeletal muscle mass was also inversely associated with the incidence of diabetes in both participants with normal glucose tolerance and with impaired glucose regulation (HR: 0.76, $95\%$ CI: 0.65–0.89; HR: 0.81, $95\%$ CI: 0.71–0.91). ### Conclusions These findings emphasized the importance of early detection and control of muscle mass loss for the prevention of diabetes. ## Background The global prevalence of diabetes continues to increase rapidly, with 537 million people diagnosed worldwide in 2021. This number is projected to increase by $46\%$, reaching 783.2 million by 2045 [1, 2]. Part of the increase in diabetes incidence results from increasing prevalence of metabolic risk factors which are important drivers of diabetes development; therefore, it is of great importance to identify modifiable factors for the prevention of diabetes. Sarcopenia, described as a multidimensional condition requiring assessment of muscle mass, muscle strength, and physical performance [3–6], may have a significant but under-appreciated contribution to increasing the risk of diabetes [7–9]. Given that skeletal muscle is the largest insulin-sensitive tissue in the body and accounts for $80\%$ of glucose uptake under euglycemic and hyperinsulinemic conditions, low muscle mass likely results in reduced capacity for glucose disposal. However, the metabolic outcomes of sarcopenia have received less attention than the functional consequences in research literature. One study has reported that low muscle mass was associated with an increased risk of type 2 diabetes [10]; beyond that, few prospective studies have regarded this issue, and no prospective studies have investigated the association between change in muscle mass and the occurrence of diabetes. To fill these knowledge gaps, we aimed to prospectively examine the association of muscle mass change with an increased risk of diabetes in a community-based population. ## Study population The present study is a prospective, population-based cohort study in community-dwelling ambulatory adults aged over 20 years. Eligible participants were recruited through promotional posters in a local community health center in Shanghai. An original cohort of 2433 participants was enrolled in 2013–2014. All the participants gave their informed consent and underwent standardized health assessments for demographic data, lifestyle habits, medical history, laboratory testing, and body composition measurement. In 2015–2016, these participants were invited for a second examination that was similar to the previous. The current study was carried out in accordance with the principles of the Declaration of Helsinki and approved by the institutional review board at the Ethics Committee of Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. For this study, we included 1277 participants who were non-diabetic at baseline, completed the 2015–2016 examination, and with fasting and 2-h blood specimens and body composition data available. Of those participants, we excluded two with inadequate serum samples, resulting in a final analysis of 1275 participants. ## Glucose and body composition measurement Venous blood specimens were collected during a morning visit after a 10-h overnight fast. Participants also provided a 2-h plasma glucose specimen following a 75-g oral glucose tolerance test. Glucose, fasting insulin, and lipid profiles were analyzed using Hitachi 7600–120 automatic biochemical analyzer (Hitachi, Tokyo, Japan). Fasting and 2-h plasma glucose specimens were measured using the standard glucose oxidase method, and fasting insulin was measured using the electrochemiluminescence immunoassay method. Total cholesterol and triglyceride were measured via enzymatic procedures, and high-density lipoprotein cholesterol and low-density lipoprotein cholesterol were measured via direct assay method. Total body fat percentage was measured using an automatic bioelectric analyzer (BIA; TBF-418B; Tanita Corp., Tokyo, Japan). Skeletal muscle mass was calculated using the BIA equation of Janssen et al.: skeletal muscle mass (kg) = [(height2/BIA-resistance × 0.401) + (sex × 3.825) + (age × –0.071)] + 5.102, where height is in cm; BIA-resistance is in ohms; sex, men = 1 and women = 0; and age is in years. Skeletal muscle index was calculated by the conversion of absolute skeletal muscle mass (kg) to percentage skeletal muscle mass using the formula: muscle mass/body mass × 100. Relative muscle mass change was calculated by subtracting muscle mass index at baseline from measured muscle mass index at re-examination, divided by muscle mass index at baseline, multiplied by $100\%$. ## Definition of diabetes At baseline and subsequent follow-up examinations, diabetes was identified by self-report, the use of oral hypoglycemic agents and/or insulin, or oral glucose tolerance test with a fasting plasma glucose concentration of ≥ 7.0 mmol/l or a post-load glucose concentration of ≥ 11.1 mmol/l after a 75 g oral glucose tolerance test, or a glycated hemoglobin A1c concentration of ≥ $6.5\%$. Impaired glucose regulation was defined as fasting plasma glucose between 6.1 and 7.0 mmol/l, and/or 2-h plasma glucose between 7.8 and 11.1 mmol/l [11]. ## Assessment of main covariates All covariates included in this study were collected at baseline and re-examination. Information on age, sex, smoking status, physical activity, disease status, and medication use was collected from interviews using standardized questionnaires. Body weight, height, and blood pressure were obtained during physical examinations at a local community health center. BMI was calculated as weight in kilograms divided by height in meters squared. Homeostasis model assessment was used to estimate insulin resistance (HOMA-IR) and was calculated as fasting plasma glucose (mmol/L) × fasting insulin (mU/L)/22.5. Smoking was defined as the use of at least one cigarette per day for at least 6 months. Alcohol use was defined as the consumption of at least 20 g of alcohol per day for at least six months. Level of physical activity was classified as light, moderate, and high according to the 2001 International Physical Activity Questionnaire [12]. A dietary quality score was defined according to 5 healthy dietary behaviors collected by the food frequency questionnaire, including a high intake of fruits and vegetables (over 4.5 cups per day), fish (over two 3.5 oz servings per week), and soy food (over 25 g per day), and a low intake of sweetened beverages (less than 450 kcal per week) and red meat (less than 50 g per day). Each variable was scored as 0 or 1, and the total score was summed up individually ranging from 0 to 5. A higher score indicated a healthier diet [13]. ## Statistical analysis Continuous data were presented as mean (SD) or median (interquartile range) according to whether the variable was normally distributed. Categorical variables were described as number (proportion). Paired-Samples t test and Wilcoxon test were used to assess differences between baseline and re-examination for continuous variables, respectively, and Chi square test was used for categorical variables. Linear regression was applied to examine the associations between the change in skeletal muscle index and change in glucose, insulin, and HOMA-IR levels. Restricted cubic spline regression with three knots (5th, 50th, and 95th) was used to examine a dose–response relationship between the change in skeletal muscle index and the risk of newly diagnosed diabetes. Relative muscle mass change was classified as 4 categories: ⊿skeletal muscle mass index loss of more than $8\%$, change between − $8\%$ and < –$2\%$, change between − $2\%$ and < $2\%$ (muscle mass maintenance: reference category), and ≥ $2\%$. The Cox proportional hazards model was used to estimate hazard ratios (HRs) and $95\%$ confidence interval (CI) for the association of baseline skeletal muscle index and its change with the risk for diabetes incidence in participants with normal glucose tolerance or impaired glucose regulation. In the multivariate models, we adjusted for age, sex, systolic blood pressure, diastolic blood pressure, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking, alcohol consumption, dietary quality score, physical activity, and total body fat percentage. Age-, sex-, BMI-, and baseline skeletal muscle index-stratified analyses were conducted to evaluate whether the change in skeletal muscle mass index was associated with the occurrence of diabetes. Restricted cubic spline regression analyses were performed using the R software package, version 4.0.3 (R Foundation for Statistical Computing, Institute for Statistics and Mathematics, Wien, Austria); other statistical analyses were performed using IBM SPSS Statistics for Windows, version 20.0 (SPSS Inc., Chicago, NC, USA). Statistical significance was set at $P \leq 0.05$ (two-tailed). ## Characteristics at baseline and at re-examination A total of 1275 individuals, 496 men and 779 women, with an age range of 24 to 76 (mean 57.7 ± 7.03) years, were enrolled in this study. After a median follow-up of 2.1 years, 142 ($11.1\%$) individuals had developed diabetes; among the population, 53 ($6.1\%$) individuals had developed diabetes in those with normal glucose tolerance and 89 ($21.9\%$) individuals had developed diabetes in those with impaired glucose tolerance. General characteristics of the study population at baseline and re-examination are given in Table 1. Mean BMI, fasting plasma glucose, glycated hemoglobin A1c, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were all significantly higher at re-examination than at baseline, whereas mean diastolic blood pressure was lower in participants with normal glucose tolerance or impaired glucose regulation ($P \leq 0.01$). Among the participants with normal glucose tolerance, the mean 2-h plasma glucose was higher ($P \leq 0.01$), and the proportions of smoking and high physical activity were lower ($P \leq 0.05$) at re-examination. There were no significant differences in skeletal muscle mass change and relative skeletal muscle mass change between normal glucose tolerance and impaired glucose regulation groups (both $P \leq 0.05$).Table 1Characteristics of study participants at baseline and at re-examinationCharacteristicsNormal glucose tolerance ($$n = 868$$)Impaired glucose regulation ($$n = 407$$)BaselineRe-examinationBaselineRe-examinationMen, n (%)319 (36.8)177 (43.5)Age, years57.0 ± 7.0458.9 ± 7.0759.1 ± 6.8061.1 ± 6.83Body mass index, kg/m223.7 ± 3.1724.0 ± 3.3124.3 ± 2.9424.5 ± 2.98Total body fat percentage, %27.4 ± 8.1827.5 ± 8.3527.6 ± 7.9727.6 ± 8.11Skeletal muscle mass index, %32.0 ± 5.5731.8 ± 5.7032.1 ± 5.5132.0 ± 5.57Blood pressure, mmHg Systolic128 (118–140)129 (117–141)130 (120–144)132 (122–145) Diastolic79.0 (72.0–85.3)77.0 (70.0–84.0)80.0 (73.0–87.0)78.0 (72.0–86.0)*Fasting plasma glucose, mmol/L5.12 ± 0.445.66 ± 0.565.35 ± 0.646.07 ± 0.722-h plasma glucose, mmol/L6.05 ± 1.096.82 ± 1.918.81 ± 1.238.64 ± 2.39Glycated hemoglobin A1c, %5.50 ± 0.335.65 ± 0.405.67 ± 0.355.81 ± 0.42Total cholesterol, mmol/L5.13 ± 0.905.40 ± 1.025.10 ± 0.895.38 ± 0.97Triglyceride, mmol/L1.24 (0.87–1.76)1.33 (0.95–1.93)1.46 (0.97–2.04)1.52 (1.10–2.23)High-density lipoprotein cholesterol, mmol/L1.39 ± 0.351.45 ± 0.361.32 ± 0.331.38 ± 0.34Low-density lipoprotein cholesterol, mmol/L3.11 ± 0.773.26 ± 0.873.13 ± 0.753.25 ± 0.81*Smoking, n (%)175 (20.2)161 (18.5)76 (18.7)76 (18.7)Alcohol use, n (%)94 (10.8)88 (10.1)43 (10.6)44 (10.8)Physical activity, n (%) Light157 (18.1)163 (18.8)69 (17.0)62 (15.2) Moderate386 (44.5)437 (50.3)212 (52.1)237 (58.3) High325 (37.4)268 (30.9)126 (30.9)108 (26.5)Skeletal muscle mass change, kg− 0.13 (− 1.35–0.97)− 0.09 (− 1.39–1.09)Skeletal muscle mass index change, %− 0.44 (− 4.37–3.12)− 0.33 (− 4.42–3.40)$P \leq 0.05$, *$P \leq 0.01$ ## Associations of change in skeletal muscle mass index and changes in glucose As shown in Fig. 1, a decreasing trend of changes in fasting plasma glucose, fasting insulin, and homeostasis model assessment-insulin resistance index went along with an increasing change of skeletal muscle mass index after adjusted for age, sex and baseline skeletal muscle mass index, regardless of glucose tolerance status (all $P \leq 0.05$). Moreover, there was a significantly inverse association between the change of skeletal muscle mass index and the change of 2-h plasma glucose in the impaired glucose regulation group ($P \leq 0.010$), but the association was non-significant in the normal glucose tolerance group ($$P \leq 0.283$$).Fig. 1Associations of change in skeletal muscle mass index and changes in glucose parameters (A fasting plasma glucose change; B 2-h plasma glucose change; C insulin change; D HOMA-IR change) according to different glucose tolerance status ## Change in skeletal muscle mass index and diabetes occurrence in normal glucose tolerance and impaired glucose regulation Restricted cubic spline models showed that a decrease in skeletal muscle mass index was associated with a nearly 2-fold risk of developing diabetes in both participants with normal glucose tolerance and impaired glucose regulation (Fig. 2). Kaplan–Meier analysis* was further used to determine the association between skeletal muscle mass index change categories and cardiovascular events risk (Fig. 3). The results showed that participants with a relative muscle mass loss over $8\%$ had the highest cumulative incidence of cardiovascular events, while those with a relative skeletal muscle mass gain ≥ $2\%$ had the lowest cumulative incidence of cardiovascular events. Among the participants with normal glucose tolerance, Cox regression analyses showed that those with a relative muscle mass loss of more than $8\%$ had a 2.58-fold ($95\%$ CI: 1.12–5.96) risk of diabetes compared with those who had stable muscle mass; besides, those with a relative muscle mass change ≥ $2\%$ had $63\%$ decreased risk of diabetes compared with those with stable muscle mass (Table 2). Increasing continuous skeletal muscle mass index change was associated with a lower risk of diabetes incidence after multivariate adjustment (HR: 0.76, $95\%$ CI: 0.65–0.89; Table 2). Participants with impaired glucose regulation and with a relative muscle mass loss of more than $8\%$ had a 2.44-fold ($95\%$ CI: 1.11–5.35) risk of diabetes compared with those who had stable muscle mass. A 2-year change in continuous skeletal muscle mass index was significantly and inversely associated with the occurrence of diabetes (HR: 0.81, $95\%$ CI: 0.71–0.91; Table 2).Fig. 2Association between change in skeletal muscle mass index and the occurrence of diabetes in participants with normal glucose tolerance and impaired glucose regulationFig. 3Kaplan–Meier survival analysis of skeletal muscle mass index changes for cardiovascular eventsTable 2Multiple adjusted hazard ratio of diabetes by basal skeletal muscle mass index and its change among subjects with normal glucose tolerance or impaired glucose regulationCases of diabetesPearson-yearsMultiple adjusted hazard ratios ($95\%$ confidence intervals)Model 1Model 2Total ($$n = 1275$$) Basal skeletal muscle mass index14224210.98 (0.93–1.04)0.98 (0.89–1.09)⊿ Skeletal muscle mass index categories < –$8\%$212272.45 (1.42–4.23)a2.65 (1.52–4.62)a − $8\%$ to < − $2\%$447211.05 (0.69–1.62)a1.12 (0.72–1.72)a − $2\%$ to < $2\%$42698ReferenceReference ≥ $2\%$357750.59 (0.38–0.93)a0.60 (0.38–0.94)a Continuous ⊿ skeletal muscle mass index14224210.81 (0.74–0.88)a0.79 (0.72–0.87)aNormal glucose tolerance ($$n = 868$$) Basal skeletal muscle mass index5316471.03 (0.94–1.13)1.15 (0.98–1.34) ⊿ Skeletal muscle mass index categories < − $8\%$101462.64 (1.17–5.93)a2.58 (1.12–5.96)a − $8\%$ to < − $2\%$165120.81 (0.40–1.61)a0.79 (0.39–1.60)a − $2\%$ to < $2\%$17475ReferenceReference ≥ $2\%$105140.37 (0.17–0.83)a0.37 (0.17–0.82)a Continuous ⊿ skeletal muscle mass index5316470.75 (0.65–0.88)a0.76 (0.65–0.89)aImpaired glucose regulation ($$n = 407$$) Basal skeletal muscle mass index897740.95 (0.87–1.03)0.85 (0.74–0.98) ⊿ Skeletal muscle mass index categories < − $8\%$11801.90 (0.89–4.05)a2.44 (1.11–5.35)a − $8\%$ to < − $2\%$282101.38 (0.79–2.41)a1.64 (0.92–2.92)a − $2\%$ to < $2\%$25223ReferenceReference ≥ $2\%$252610.78 (0.44–1.38)a0.78 (0.43–1.40)a Continuous ⊿ skeletal muscle mass index897740.85 (0.76–0.96)a0.81 (0.71–0.91)aModel 1 was adjusted for age, sex, systolic blood pressure, diastolic blood pressure, triglyceride, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol. Model 2 was further adjusted for smoking, alcohol use, dietary quality score, physical activity, and total body fat percentageaThe model was further adjusted for basal skeletal muscle mass index ## Subgroup analyses Additionally, we stratified participants’ age, sex, mean BMI, and mean baseline skeletal muscle mass index to control the potential confounders. In these subgroups, participants with a relative muscle mass loss of more than $8\%$ had a 2 or 3 folds risk of developing diabetes compared with those who had stable muscle mass. The multivariate adjusted association between a 2-year change in skeletal muscle mass index and the risk of diabetes incidence remained statistically significant in stratified analyses (Table 3).Table 3Multiple adjusted hazard ratio for the incidence of diabetes by skeletal muscle mass index change in subgroups in total subjectsSubgroupMultiple adjusted hazard ratios ($95\%$ confidence intervals)⊿ skeletal muscle mass index categoriesContinuous ⊿ skeletal muscle mass index < − $8\%$− $8\%$ to < –$2\%$− $2\%$ to < $2\%$ ≥ $2\%$Age < 60 years2.16 (1.00–4.68)0.79 (0.42–1.51)Reference0.56 (0.29–1.13)0.78 (0.68–0.89) ≥ 60 years3.08 (1.32–7.17)1.66 (0.89–3.08)Reference0.65 (0.34–1.23)0.79 (0.68–0.92)Sex Men2.38 (0.77–7.39)1.24 (0.66–2.34)Reference0.45 (0.22–0.93)0.80 (0.68–0.94) Women2.95 (1.49–5.86)1.08 (0.58–1.99)Reference0.67 (0.36–1.26)0.76 (0.67–0.86)Body mass index < 25 kg/m22.36 (1.10–5.10)1.28 (0.71–2.33)Reference0.75 (0.41–1.37)0.84 (0.74–0.95) ≥ 25 kg/m23.21 (1.35–7.64)0.90 (0.46–1.75)Reference0.44 (0.21–0.91)0.76 (0.65–0.88)Basal Skeletal muscle mass index < $32\%$2.60 (1.27–5.32)0.96 (0.52–1.77)Reference0.59 (0.32–1.07)0.77 (0.67–0.89) ≥ $32\%$3.74 (1.48–9.46)1.37 (0.73–2.59)Reference0.53 (0.26–1.10)0.76 (0.66–0.88)The multiple adjusted model was adjusted for age, sex, systolic blood pressure, diastolic blood pressure, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, basal skeletal muscle mass index, smoking, alcohol use, dietary quality score, physical activity, and total body fat percentage ## Discussion The primary finding in the present study was that a 2-year change in skeletal muscle mass index in addition to its baseline status was associated with the diabetes occurrence in a community-based population. To the best of our knowledge, this is the first study to investigate the association between changes in muscle mass and the risk of diabetes incidence in a general Chinese population. These findings emphasize the importance of early detection and control of muscle mass loss for the prevention of diabetes. An increased risk of diabetes is not only associated with low muscle mass but can also develop silently in the face with increasing muscle mass loss. Previous studies revealed that aged skeletal muscle with decreased oxidative capacity led to altered mitochondrial biogenesis, which may be impaired by age-dependent accumulations of point mutations in human mitochondrial (mt) DNA in addition to pro-inflammatory processes [14, 15]. Both mitochondrial dysfunction and chronic low-grade inflammation are associated with insulin resistance. Although β-cell failure is the sine qua non for development of type 2 diabetes, skeletal muscle insulin resistance is considered to be the initiating or primary defect that is evident decades before β-cell failure and overt hyperglycemia develops [16–19]. It is likely that significantly lower skeletal muscle mass results in reduced capacity for glucose disposal, but few prospective studies have investigated the risk of incident diabetes in adults with sarcopenia. The present study demonstrated that baseline skeletal muscle mass index was an independent predictor of future diabetes incidence in adults with impaired glucose regulation; however, the said relationship was not found in adults with normal glucose tolerance probably due to the fewer metabolic risk factors in the population, and it might manifest with a relatively longer follow-up. This is consistent with previous findings from two population-based prospective studies conducted in America [20, 21]. One study concerned that men with insulin-resistance in the highest quartile had higher odds of $5\%$ or more loss of total lean mass and appendicular lean mass than those in the lowest quartile [20]. The other study found that greater muscle area was associated with a lower risk of diabetes for older women with normal weight [21]. However, muscle mass is fundamentally correlated with body size, indicating that subjects with a larger body size may have larger muscle mass; therefore, when evaluating the adequacy of muscle mass, skeletal muscle mass index has been used. Besides, few similar cohort studies have been conducted among the Asian populations. Only one Korean study conducted in 6895 middle-aged and older individuals found that low muscle mass index was an independent risk factor for type 2 diabetes [10]. The rate of muscle loss with age appears to be fairly consistent, approximately $1\%$–$2\%$ per year past the age of 50 years. This study demonstrated that the loss rate significantly higher than natural loss in muscle mass had an over 2-fold risk of developing diabetes after adjusting for baseline skeletal muscle mass index and other risk factors in adults with normal glucose tolerance and impaired glucose regulation. Additionally, a reduced risk of developing diabetes was accompanied by an increase in skeletal muscle mass index regardless of whether the population's baseline level was high or below average. This finding suggests that the short-term effect of a change in skeletal muscle mass index on diabetes occurrence is not less than the effect of its baseline status that may represent a long-term effect on diabetes. It is not too late to initiate control of muscle mass loss, even in adults with prediabetes. Our study has several limitations. First, magnetic resonance imaging is the gold standard for the measurement of skeletal muscle mass; however, it is not a convenient method for assessing skeletal muscle mass in a relatively large population-based study. Regarding the accuracy of BIA in the assessment, a previous study found that the correlation coefficient between BIA and magnetic resonance imaging was 0.93 and the standard error of the estimate for predicting skeletal muscle mass from BIA was $9\%$, which suggested a reasonable estimation in our study [22, 23]. Second, our results were limited to a single ethnic group. Since there may be relatively large differences in body composition even within Asian populations, it is difficult to generalize our findings to larger populations. Third, we found that an increase in skeletal muscle mass had no effect on the transformation from impaired glucose regulation to normal glucose tolerance. Ideally, further studies should extend follow-up time to verify the result. ## Conclusions In conclusion, our study demonstrated that in addition to baseline low muscle mass, 2-year change in skeletal muscle mass was also associated with the incidence of diabetes. The risk of diabetes occurrence is not only dependent on the presence of low muscle mass but also by the loss of muscle mass. ## References 1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract (2018.0) 138 271-281. DOI: 10.1016/j.diabres.2018.02.023 2. 2.Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843. 3. Rosenberg I. 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--- title: 'Online Arabic Beverage Frequency Questionnaire (ABFQ): evaluation of validity and reliability' authors: - Tahrir M. Aldhirgham - Lulu A. Almutairi - Atheer S. Alraqea - Amani S. Alqahtani journal: Nutrition Journal year: 2023 pmcid: PMC10031979 doi: 10.1186/s12937-022-00830-9 license: CC BY 4.0 --- # Online Arabic Beverage Frequency Questionnaire (ABFQ): evaluation of validity and reliability ## Abstract ### Background Obesity and chronic diseases are significant public health issues in the Middle East and North Africa region. A robust body of evidence demonstrated the association between beverage consumption, obesity, and chronic diseases. Therefore, the assessment of beverage consumption is gaining more interest in health policy development, food industry partnerships, research expansion and community involvement. Although beverage-consumption assessment tools have been developed for various populations, none were developed for the Arabic population. In this study, we developed and validated an online Arabic Beverage Frequency Questionnaire (ABFQ) to assess the total beverage intake among Arabic speaking population. ### Methods A cross-sectional validation study was conducted among healthy adults aged between 18 and 55 years. Participants ($$n = 49$$) completed a 24-item ABFQ on two occasions and provided one 24-h urine sample. For validity, total beverage consumption (ABFQ1) was assessed against a 24-h urine sample using an osmolality test and correlation analysis. Reliability was assessed by comparing the participants’ consumption in total and for every 24 individual items from ABFQ1 with the total and individual items in ABFQ2 using correlation and paired sample t-test. ### Results The average daily consumption of beverages was 1504 ml/day, while the average urine osmolality/kg was 614. The validity assessment between ABFQ and urine osmolality indicates a negative correlation. However, the correlation was week and not statistically significant (rs = -0.2, $$p \leq 0.12$$). In reliability test, correlation analysis was positive and acceptable in all beverage categories (rs = 0.4 − 0.9; all $p \leq 0.05$) except flavored milk (rs = 0.2; $p \leq 0.181$) and sweetened coffee (rs = 0.3; $p \leq 0.022$). Furthermore, no significant differences were found between the means of total consumption in both ABFQ1 and ABFQ2. ### Conclusions The finding of this study suggest that the ABFQ is a reliable reproducible tool for assessing beverage consumption among Arabic-speaking consumers. However, the survey could not be validated using 24-h urine osmolality only and other methods such as multi dietary records may use in future re-assessment. ## Introduction Globally, non-communicable diseases (NCDs), including cardiovascular diseases, cancers, chronic respiratory diseases and diabetes, cause 41 million deaths, corresponding to $71\%$ of all deaths annually [1]. In the Middle East and North African countries (MENA), the prevalence and mortality related to NCDs are similar to the global situation [2]. In addition, the region has six of the ten countries with the highest diabetes prevalence in the world [3]. Furthermore, obesity prevalence in most Arab countries grew to $55\%$ and $30\%$ in adult females and males, respectively [4]. NCDs have many metabolic risk factors, including obesity, hyperglycemia and hyperlipidemia that can be developed due to behavioral risk factors such as physical inactivity and unhealthy diet and beverage consumption. A considerable body of evidence demonstrated the association between beverage consumption and NCDs. For instance, high consumption of Sugar-sweetened beverages was found to be associated with obesity and diabetes mellitus in adults and children [5–12], hypertension, and coronary heart diseases [13–16]. Nevertheless, beverage intake is a significant contributor to health and well-being. For instant, water is essential for the human body’s biological functions, general health, and prevention of chronic disease [17, 18]. Moreover, caffeinated beverages have intake-based health risks and benefits, especially in patients with cardiovascular diseases and hypertension [19, 20]. Given the importance of beverages as part of daily diet and their contribution to health and disease, an accurate assessment of beverage intake is required. Beverage consumption had been assessed previously using nutritional assessment tools, such as food diary recalls and food intake records (FIR) [21, 22]. However, these methods are designed to assess food intake in general and might be imprecise for beverage consumption assessment [23–25]. Accordingly, various beverage consumption frequency-based assessment tools have been developed to assess beverage consumption in languages other than Arabic [24–28]. The developed tools were paper-based surveys that relied on participants estimation of portion size. Recently, one online tool was developed to assess the Canadian population’s beverage consumption in the English language [29]. The Middle East and North Africa (MENA) region includes 21 countries with a total population of approximately 464 million [30]. Arabic is the main language, and Islam is the main religion in MENA countries. Additionally, they share similar social, political, economic, cultural and heritage [31]. Accordingly, some food and beverage consumption is prohibited, such as alcohol [32, 33]. Industrial food processing and international trade marketing also affected the dietary patterns, and all different types of food and beverage are available in the markets. However, the populations in these countries maintained many traditional beverages such as Laban (fermented milk), Cawah (different types of traditional Coffee drink), and zahorate (drink made of different types of herbs and/or plant leaves). Local traditional beverages differ among Arab countries in their consumption patterns, recipes, preparation methods and availability [31]. To this end, there are no reliable measures of beverage consumption validated in Arabic and specifically for use within the Arabic-speaking MENA region population. Therefore, this study aimed to develop and validate the first online Arabic Beverage Frequency Questionnaire (ABFQ) to assess the total beverage intake among Arabic speaking population. ## Study design A cross-sectional study to test the validity and reliability of a new developed ABFQ was conducted between January 30th and March 23rd, 2021, in Riyadh, Saudi Arabia. The study consisted of two parts, 1) a self-administered questionnaire that was filled up by the participants two times in different periods (ABFQ1, ABFQ2), and 2) a lab test of a 24-h urine sample collected from participants. The correlation between the average daily intake value (based on ABFQ1) and urine osmolality value on the same day of ABFQ1 (based on a 24-h urine sample) was evaluated for validity assessment similarly to Ferreira-Pêgo et.al. [ 2016] [24]. While reliability assessment was conducted by comparing the average daily consumption from ABFQ1 with the average daily consumption from ABFQ2. Figure 1. Provide more information on study design, participants, and the recruitment process. Fig. 1Study design, participants and recruitment process ## Participants A convenient sample of healthy adults ($$n = 51$$) aged between 18 and 55 was recruited from the local community of Saudi Food and Drug Authority (SFDA), i.e., the employee and their families because they may be among the best community group will follow the study instructions. Participants who were over 18 years up to 55 years were Arabic speaking and healthy (no history of chronic diseases such as heart disease, hypertension, diabetes, kidney diseases or health conditions that may influence the fluid intake or output either because of the condition, its treatment or complication) were included in the study. The study protocol was approved by the SFDA Institutional Review Board. ## Recruitment process Targeted participants were invited verbally to the study, and the study protocol was explained in detail by one of the trained research teams. Information provided about the method of communication, the need for filling two ABFQ versions, the duration between questionnaires, the need for urine sample collection, and the duration and instruction of urine sample collection. After Enrollment, participants were provided with a consent form, along with a urine sample container. On the next weekend (Saturday), the participants received the questionnaire through the short message service (SMS) containing the first ABFQ survey (ABFQ1) and were asked to collect the urine sample on the same day. The Urine sample collection determined on a weekend day (Saturday) to avoid any inconvenience during urine sample collection at work or university and to ensure completeness of the sample (Weekend days in Saudi Arabia is Friday and Saturday; most activities done on Friday). After 7 days, the participants received another link of ABFQ2 (second survey). A three-reminder message was sent to the participants who did not complete ABFQ2 before being withdrawn from the study. ## Urine samples collection The aim of using biological measures is to provide objective assessment and to help in avoiding the bias that may result from self-reporting [23, 25]. To the best of our knowledge, no golden standard biomarker reflects the change in hydration status according to changes in dietary fluids intake [34]. However, urine osmolality in a 24-h urine sample is considered the most suitable body hydration status biomarker for individuals because it represents the net sum of water gains, losses, and neuroendocrine regulatory responses [35]. To measure the urine osmolality in a 24-h urine sample, sample containers (2000 ml) handled to participants who were expressed interest in participating and provided verbal consent. Then, participants received verbal instructions on how to collect 24-h urine samples. Each participant was asked to collect the sample on the first next morning after discarding the first urine sample, then collect all-after samples for the next 24 h, including the first urine sample of the next day. Afterwards, participants dropped the sample containers at the previously determined collecting point. All samples were transferred to an approved independent laboratory with the capacity to deal with urine samples and the necessary facilities to run the 24-h urine osmolality test (Cryoscopic Osmometer). Targeting healthy adults (free of chronic diseases or health condition that may influence fluid intake or output) ensured that osmolality test will not be affected by kidney disfunction or other physiological condition. Also, ensured avoiding the use of medications and its possible effect on fluid output or urine osmolality. The study conducted at the middle of winter season (in Saudi Arabia, from December to April), therefore, questionnaire and urine sample test conducted in similar weather conditions and similar level of fluid consumption. The device used is OSMO STATION, OM-6060, an automatic osmometer that measures the osmolality in different samples, including blood and urine. The determination method is freezing point depression osmometry based on the principle of lowering the solution freezing point caused by the solute. A sample of the specimen (200 μl) to be analyzed is aspirated into the sample tube, which is then placed in the cooling chamber of the osmometer. The sample is super cooled below the freezing point. Then crystallization is initiated by rapidly vibrating the sample to seed it with air bubbles. After seeding, the sample temperature rises because of the heat of fusion released during the freezing process. The temperature rises until the equilibrium plateau is reached. During the equilibrium plateau, only a small fraction of the water is frozen. The sample continues to freeze as the temperature begins to decrease again because of the colder environment until it provides the osmolality value. Each sample container was labelled with a unique number entered by participants at the beginning of both questionnaires (ABFQ1, ABFQ2) to connect participants samples with both questionnaires. ## Arabic Beverage Frequency Questionnaire (ABFQ) The ABFQ is an online-based tool developed to assess the total beverage intake among Arabic-speaking consumers. The included beverage categories were as follows; water, fresh fruit juice, fresh vegetable juice, caned $100\%$ juice, sweetened juice, milk juice, low-fat milk, free fat milk, flavored milk, soft drinks, Iced tea, artificially sweetened soft drinks, artificially sweetened iced tea, unsweetened tea, sugar milk tea, unsweetened coffee, sweetened coffee, unsweetened black coffee, sweetened black coffee, flavored coffee, malt drinks, artificially sweetened energy drinks, sweetened energy drinks, sports drinks. In addition to two open-ended question sets to declare any other beverage that was not included in the questionnaire. Sweetened beverage meant beverage sweetened with sugar, while artificially sweetened beverage meant beverage sweetened with non-nutritive sweeteners. The beverage consumption assessment questions were adapted from previous studies [24, 25, 36]. However, the categories were adjusted based on energy and macronutrient content (calories and sugar content) using Food Data Central of USDA data [37], nutrition fact labels, and the calories content in the coffee shops and fresh juice shops menus. For each category, the consumption frequency was quantitatively assessed by asking, “during the last 30 days, how many times you drink…”. The response was categorized into (never, once a month, twice a month, three times a month, Once a week, 2–3 times a week, 4–6 times a week, once a day, twice a day, 3 times a day. Then, the amounts consumed were assessed by asking, “how much did you usually drink each time?” in combination with a series of pictures of the common serving size of each type of beverage, such as common cups, bottles, and containers with the volume in (ml) below the picture (see Fig. 2). Pictures adapted from product company websites or were taken for the study purpose. Option “More” was available in both questions with an open text box. During pilot testing of the ABFQ, the average administration time was 10 min. Fig. 2Example of portion size pictures used in ABFQ, A different portion size for water, B different portion size for soft drinks, C different portion size for juices To score the ABFQ, frequency (“How often”) is transformed to the unit of times per day, then multiplied by the amount consumed (“How much each time”) to find the beverage’ average daily intake in (ml) using the following formula: [(consumption frequency per month / 30 days) x amount consumed each time (ml) = average daily intake (ml/day)]. Total energy and sugar content of beverages could not be determined because of the lack of a local database of beverage nutrient content or food composition tables. ## Sociodemographic characteristics To assess other covariates, participants were asked about sociodemographic statuses, including age, gender, education, marital status, and chronic disease status and illness history. Furthermore, they were asked if they have any health condition that may influence the fluid intake or output because of the condition, its treatment or complication during the study period. Weight and height were also self-reported, which were used to calculate body mass index (BMI). BMI were categorized into 4 groups [underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (> 30.0 kg/m2)] according to World Health Organization (WHO) [38]. ## Data analysis Descriptive statistics (mean ± standard error of the mean; frequencies) were generated for demographic characteristics, average daily intake of total beverage and average daily intake of each beverage category (ml). To assess the validity, the average daily intake of total beverage (ml/day) was investigated by the ABFQ1 compared to urine osmolality in 24-h urine samples using correlational analyses (Spearman’s correlation). Moreover, to examine test–retest reliability, the ABFQ1 responses were compared with ABFQ2 responses using the same correlational analyses (Spearman’s correlation) and paired sample t-test. The significance in all statistical tests was set at $p \leq 0.05.$ Analysis conducted using Stata version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC). ## Result A total of 49 participants in ABFQ1 and 45 in ABFQ2 were included in the present analysis. At baseline, the mean age of participants was 32 ± 8 years (range 18 to 54 years). The body mass index was widely distributed (mean = 25.6 ± 4.3 kg/m2; range 16.9—35.5 kg/m2), while participants were primarily of “normal” BMI status ($55.1\%$). Of 49 participants, the most reported having a bachelor’s degree ($53\%$) and being unmarried ($65.3\%$). The duration between questionnaires responses ranges between 7 to 17 days (Median: 7 days). More general characteristics of the study participants are summarized in Table 1.Table 1General characteristics of the study participantsVariablesFrequency (n)Percentage (%)Questionnaire response ABFQ14996 ABFQ24588.2BMI Under weight12 Normal weight2755.1 Overweight918.3 Obese1224.4Age groups (years) 18 to 25918.3 26 to 352551 36 to 451122.4 46 to 5548.1Gender Male3571.4 Female1428.5Education level High School or less816.3 Bachelor’s degree2653 Postgraduate1530.6Marital status Married1632.6 Unmarried3265.3 Separated or widowed12BMI were categorized according to World Health Organization (WHO) into 4 groups [underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (> 30.0 kg/m2)] [38] ## Relative validity of the questionnaire (ABFQ1 vs urine osmolality test) The average daily consumption based on the ABFQ was 1504 ml/day, while the average urine osmolality/kg was 614 mOsm/kg. Correlational analysis between total fluid intake (ml/day) based on the ABFQ1 and 24-h urine osmolality (mOsm/kg) yields a negative correlation (rs = -0.2, $$p \leq 0.12$$) as would be expected for a possible biomarker of total fluid intake. However, the correlation were week and non-significant. See Table 2.Table 2Validity assessment (Spearman’s correlation)Mean (min–max)SDMedianrsP valueABFQ1 (ml/day)1504 (463–4198)7691383-0.20.12Urine osmol (mOsm/kg)614 (200–1068)234.1589 ## Test–retest reliability of the questionnaire (ABFQ1 vs ABFQ2) Correlation analysis for the questionnaire reliability was positive and acceptable in all beverage categories (rs = 0.4 − 0.9; all p ≤ 0.05) except flavored milk (rs = 0.2; $$p \leq 0.181$$) and sweetened coffee (rs = 0.3; $$p \leq 0.022$$) (Table 3). Sports drinks, energy drinks and soft drinks had the highest correlation value (rs = 0.8—0.9). The lowest correlation value (rs = 0.4) was found for caned $100\%$ juice, sweetened juice, milk base juice and sweetened black coffee. The difference between average total daily beverage intake from ABFQ1 and ABFQ2 is 11 ml. However, no significant differences were found between the means (see Table 4).Table 3Test–retest reliability between ABFQ1 and ABFQ2 among all beverage categoriesBeverage categoriesBeverage estimated consumption (ml/day)Reliability (test–retest)ABFQ1 ($$n = 49$$)ABFQ2 ($$n = 45$$)ABFQ1 vs ABFQ2Mean ± SDRangMean ± SDRangrsP-valueWater690 ± 56927–2400631 ± 47633–18000.5222 ≤.001Fresh fruit juice15 ± 300–20021 ± 750–5000.6806 ≤.001Fresh vegetables juice4 ± 260–1806 ± 350–2350.6585 ≤.001Caned $100\%$ juice7 ± 130–605 ± 140–830.4747 ≤.001Sweetened juice16 ± 730–50011 ± 220–830.43890.002Milk juice2 ± 90–602 ± 90–600.40350.006Low-fat milk46 ± 890–40028 ± 550–2500.7490 ≤.001Free fat milk2 ± 40–203 ± 130–780.7726 ≤.001Flavored milk2 ± 50–200 ± 20–130.20290.1812Soft drinks114 ± 2090–9901312 ± 2330–10650.8437 ≤.001Iced tea24 ± 870–55012 ± 430–2500.7329 ≤.001Artificially sweetened soft drinks34 ± 660–23746 ± 880–3550.8260 ≤.001Artificially sweetened iced tea------Unsweetened sugar tea52 ± 900–32565 ± 1090–4000.7985 ≤.001Sweetened tea with/without milk103 ± 1490–65095 ± 1570–6500.8106 ≤.001Unsweetened Arabic coffee/Turkish coffee96 ± 1730–100092 ± 2350–15000.5923 ≤.001Sweetened Arabic coffee/Turkish coffee3 ± 160–1005 ± 210–1000.33930.022Unsweetened black coffee149 ± 2210–946194 ± 2520–9460.6862 ≤.001Sweetened black coffee35 ± 950–47227 ± 1110–7080.40170.006Flavored coffee65 ± 1710–94644 ± 880–3540.6001 ≤.001Malt drinks8 ± 190–838 ± 190–830.7828 ≤.001Artificially sweetened energy drinks6 ± 370–25012 ± 750–5000.5 ≤.001Sweetened energy drinks14 ± 730–50015 ± 570–3300.8775 ≤.001Sport drinks4 ± 210–13312 ± 750–5000.9998 ≤.001Total beverage1504 ± 769463–41981477 ± 750267–36340.7 ≤.001Table 4Test–retest reliability of total daily beverage intake between ABFQ1 and ABFQ2 (paired t-test)Mean Volume (ml/day)SESD$95\%$ CIP valueABQ115041107851284—17250.86ABQ214771127501252- 1702 ## Discussion This study aimed to develop and validate the first online Arabic Beverage Frequency Questionnaire (ABFQ) to assess the total beverage intake. The validity assessment between ABFQ and urine osmolality indicates a negative correlation. However, the correlation was week and not statistically significant (rs = -0.2, $$p \leq 0.12$$). In addition, a strong reliability correlation of total beverage between ABFQ1 and ABFQ2 was found (rs = 0.7; $p \leq 0.05$). Our validation finding is influenced by the small sample size ($$n = 49$$) and the variation in participants' characteristics [39]. Also, urine osmolality as a hydration biomarker may be influenced by other factors such as diet, body size, sweat loss and intensive exercises [40–42]. The osmolality of a 24-h urine sample indicates 24-h hydration status and determines the functional surplus of fluids [24, 43]. It can be a quantitative measure to assess beverage questionnaire validity because it negatively correlates with fluid intake in the healthy general population [40–42]. Daily fluid intake directly affects urine osmolality due to antidiuretic hormone influence (ADH). ADH is responsible for fluid reabsorption rate in the kidney; therefore, when fluid intake increases, the antidiuretic level decreases; the kidney reabsorption of fluid decreases; and the urine osmolality increases [40–42]. In this study, the correlation between urine osmolality and average daily intake of total beverage is weak ($r = 0.2$), possibly due to different factors. Primarily, urine osmolality was confirmed to be an excellent indicator of 24-h hydration status [35, 44], and in the validation assessment, we compared 24-h urine osmolality against one day of fluid intake based on the average of 30 days intake recall. A similar correlation (r2: 0.20; $p \leq 0.001$) between urine osmolality, age and average fluid intake was reported by Ferreira-Pêgo et.all [2016] [24]. They also reported a bland–Altman parameter estimate of 0.22 between average fluid intake and 24-h urine volume. Food contains different levels of fluid and moister that contribute to total daily fluid intake depending on the individual and population variation in the type and quantities of foods. Many countries assessed the food contribution to the total fluid intake, such as China($40\%$), US($19\%$), Mexico ($34.5\%$), UK($27\%$) and France ($36\%$) [45]. In Saudi Arabia and Arab countries, there are no data on the food contribution to the total daily fluid intake. Accordingly, assessing the effect of food intake is not possible. Moreover, fruit and vegetables are among the highest food in fluid content (70–$95\%$) [44]. However, Arab countries reported a very low fruit and vegetable consumption [46, 47]. Another factor is having undiagnosed health conditions or undiagnosed kidney dysfunction within participants. However, the urine osmolality range was within the normal range (50–1200 mOsm/kg [48]). Also, participants may not have completed urine samples even though we targeted a well-educated group of participants and collected data on the weekend to avoid any inconvenience at work or university. Other factors include the general bias of self-reported recall food assessment tools [49]. Besides, the measurement error may result from using an image-based dietary assessment method and providing pictures of available portions size and bottles [50]. Although ABFQ included all beverages consumed by the general population in Arabic countries, it may underestimate the consumption of beverages such as an alcoholic beverages. Alcoholic beverages are prohibited in the religious conviction of Arab countries, and they are either prohibited, restricted or regulated [32, 33]. This study found that for almost all beverage categories and total daily fluid volume, ABFQ was significantly correlated despite the time interval between the ABFQ1 and ABFQ2 (7–20 days). The time interval in our study remained acceptable for repeated frequency measures [39]. Accordingly, ABFQ can be a reliable repeated tool to assess beverage intake and change in consumption patterns over time. Limited variation in the study sample may caused the weak correlation between the two assessments (ABFQ1 and ABFQ2) of flavored milk and sweetened Arabic coffee/Turkish coffee consumption. Sweetened coffee consumption is reported in young adults [51], and in this study, the majority of the study sample ($73\%$) are within the age range of 26–45 years. Flavored milk is also more common in other age groups, such as children. Sweetened iced tea is low in calories (25–40 kcal/100 ml) despite its sugar content (5-6 g/100 ml); therefore, unsweetened iced tea was not reported in both assessments. Specific Beverage consumption assessment tools were developed for other populations in English and Spanish language. ABFQ is the first tool developed in Arabic and assessed for validity and reliability among the Arabic-speaking population. Nevertheless, a few studies have investigated the consumption of beverages among Arab populations [52–56]. All studies measured the total food consumption by using Food Frequency Questionnaire and one or multiple FIR [52–57]. Only one study used a specific beverage questionnaire [58]. However, the study had some limitations; mainly, the questionnaire was translated from English then the responses collected using paper survey, therefore had limited beverage categories and relied on the individual’s self-estimation of portion size without including the available portion size pictures in the markets. Developing the ABFQ in an online questionnaire method is one of the main strengths of this study. This is because it allows access to different regional and understudied populations, as well as allows assistance for groups with low health literacy or low education levels groups by providing pictures as guidance of estimating the consumption. Moreover, it being interactive with participants through pictures, videos, and displayed text with or without audio. Besides that, the general advantage of the online questionnaire such as ensuring complete responses, allowing written and visual prompts, allowing immediate and direct responses, allowing accurate scoring and high participant involvement [34]. For example, the recent online beverage Frequency Questionnaire evaluated using pictures to guide the estimation of portion size and container size [36]. One online Arabic questionnaire is used to assess the consumption of soft drinks and the related factor that influence their consumption [55]. Arabic-speaking countries, especially gulf region countries (the United Arab Emirates, Kingdom of Bahrain, Kingdom of Saudi Arabia, Sultanate of Oman, State of Qatar and State of Kuwait [59]), have had a common lifestyle over the years. However, Beverage types, consumption behavior, and drinking utensils vary in these countries. In ABFQ, we sought to enhance the self-recall of portion size and reduce the self-estimation by representing the portion size with pictures and amounts in volume. We showed all local and traditional beverage special cups and utensils found in MENA and gulf population households and markets. For example, Arabic coffee is consumed in most those countries in a special small cup (approximately 50 ml). Another example is the tea which is consumed in some countries in a special small cup (approximately 125 ml), while in others, it is consumed in a regular cup (200 ml). The study has some limitations; Firstly, we used the biological indicators only to validate the questionnaire, yet, analyzing FIR based on local food composition data was not possible because such data was unavailable. Accordingly, FIR in our study would only increase the burden on participants and may affect the consumption recall causing overestimation or underestimation. While the biological value reports the actual biological status. 24- hour Urine osmolality is found to be the best applicable method to assess hydration status [35]. Other limitations of this study include the convenience and small sample size that allowed for variation between study participants and reduced the statistical power. Also, it caused low consumption reports for some categories that are known for high consumption, such as sports drinks (2 ± 5 ml/day), Sweetened energy drinks (14 ± 73 ml/day), and Free sugar energy drinks (6 ± 37 ml/day). Nevertheless, since this study is a validation study, no specific sample size number is required. Similar published studies reported a sample size ranging from 50 to 160 participants [24, 25, 27, 29, 36, 60]. The future version of this survey may refine beverage groups further based on calories and sugar content from a local database. Milk and Laban has a wide range of products and varies widely from country to country. For example, there is the fresh, long life, flavored, fat reduced or removed and others. It can be gathered in one group or separated according to the target population and local markets. Another possible categorising approach can be based on the sales data to find the most consumed data then assess their consumption. All to achieve better-tailed assessment tools for such broad products. ## Conclusion The present ABFQ appears to be highly reliable reproducible tool for assessing the intake of different types of beverages among Arabic-speaking consumers. 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--- title: Associations between social media addiction, psychological distress, and food addiction among Taiwanese university students authors: - Po-Ching Huang - Janet D. Latner - Kerry S. O’Brien - Yen-Ling Chang - Ching-Hsia Hung - Jung-Sheng Chen - Kuo-Hsin Lee - Chung-Ying Lin journal: Journal of Eating Disorders year: 2023 pmcid: PMC10031987 doi: 10.1186/s40337-023-00769-0 license: CC BY 4.0 --- # Associations between social media addiction, psychological distress, and food addiction among Taiwanese university students ## Abstract Social media addiction has been found to have psychological and physiological impacts on individuals’ health. In order to better understand the role of social media addiction, the present study constructed a model to investigate the potential mechanism of social media addiction in affecting the individuals’ food addiction level. The findings showed a clear pathway between social media addiction and food addiction with the involvement of psychological distress. Accordingly, we suggested that individuals with the potential risk of social media addiction should pay attention to their psychological status and food intake. The potential effect of weight-related stigmatization would also need to be considered, strategies such as mindfulness or food consumption monitoring would be beneficial to address the issues. ### Background Worldwide, $60\%$ of people use social media. Excessive and/or addictive use of social media termed “problematic social media use”, has been reported to negatively influence psychological and physiological health. Therefore, we proposed an illustrated model to investigate the associations between social media addiction, psychological distress and food addiction among Taiwanese university students. ### Methods A total of 598 participants (mean age = 22.8 years) completed an online survey comprising the Bergen Social Media Addiction Scale (BSMAS) assessing social media addiction, the Depression Anxiety and Stress Scale (DASS-21) assessing psychological distress, and the Yale Food Addiction Scale 2.0 (YFAS 2.0) assessing food addiction. ### Results Structural equation modeling showed the significant associations between BSMAS and DASS-21 (standardized coefficient [β] = 0.45; $p \leq 0.01$) and between DASS-21 and YFAS 2.0 (β = 0.43; $p \leq 0.01$). In addition, mediation effect with 100 bootstrapping samples showed the indirect effect of DASS-21 in the association between BSMAS and YFAS 2.0 ### Conclusions The present study details the relationships between social media addiction and psychological distress as well as food addiction. The results suggest the need for interventions aimed at reducing these negative outcomes. Coping strategies for improving self-control or reducing weight-related stigma, such as food consumption monitoring or mindfulness, could be adopted for at-risk individuals to address these problems. ## Introduction With the development of information technology, Internet-based social media networking has rapidly increased [1]. A recent report indicated that $59.3\%$ of the total global population or approximately 4.7 billion people were social media users in 2022 [2]. Both positive and negative effects of social media use have been found among social media users. Positive effects such as joy and relaxation [3], information exchange [4], and increased physical activity [5] were found in some groups of social media users. However, negative social media effects such as psychological distress [3], excessive use [6], and sedentary lifestyle [7] were also reported in small groups of individuals leading to poorer mental health [8]. The group of individuals experiencing negative outcomes from social media use is considered to have a specific type of problematic Internet use (i.e., problematic social media use, [PSMU]). The present study sought to build on several previous studies in the field [9–11] by focusing on social media use as individuals’ recreational social interaction via online platforms, such as web sites or smartphone applications that contain user-generated content. The rapidly increasing number of social media users [2] has made PSMU a global issue that cannot be ignored. PSMU is defined as the excessive interaction and networking on social media networking, to the extent that these behaviors impair other important activities such as education, work, interpersonal relationship, and/or psychological health and well-being [6, 12–14]. Prior cross-sectional research has documented that PSMU was associated with poor sleep quality [6, 15], psychological function [6, 16, 17], as well as other addictive behaviors [14, 17]. Furthermore, some longitudinal research showed a prolonged influence of PSMU on increasing sleep disturbance, depression and anxiety [18, 19], even suicidal-related outcomes [20]. Among these negative influences, the association between PSMU and psychological distress (e.g., depression or anxiety) is well-reported [6, 16–19]. One study reported that psychological distress derived from PSMU is more likely to be developed via internal (e.g., loneliness) rather than external process (e.g., social isolation) [14]. More specifically, individuals’ self-regulation and use-expectation were reported to mediate the significant association between PSMU and depressive symptoms [21]. In addition, individuals with PSMU reported sleep interruption [6, 15, 19] and lower physical activity level [22], which may further worsen their mental health status [6, 15]. They also tend to engage in excessive self-comparison and prejudiced self-image [23], as a result of continually comparing themselves to the idealized portraits posted on social media [14, 23]. These physical and psychological impacts may exacerbate vulnerability and increase the risk of developing psychological distress [14]. Psychological health is one of the major health issues which may impair individuals’ functional performance [24]. Individuals with mental health disturbances may be impaired in employment, educational, or relationship domains [24, 25], subsequently exacerbating their perceived stress and risk of burnout [25]. For those with relatively poor emotional regulation, instinctive avoidance [26] of this unpleasant feeling may act as a coping strategy [24] and prompt the development of addictive behaviors such as problematic use of the Internet [27] or food addiction [28, 29]. Studies have reported a robust association between mental health symptoms and food addiction [28, 29]. Food addiction refers to individuals’ uncontrollable desire to obtain food [29]. Specifically, as acts of comfort-seeking behavior, emotional eating and other disordered eating behaviors are commonly observed as a stress-alleviating strategy [30, 31]. Psychological distress (e.g., depression or anxiety) is highly correlated to the development of food addiction [29, 31] due to increased vulnerability [29]. There are more than 21.4 million (equal to $89.4\%$ of the total population) social media users [32] in Taiwan in 2022, which is higher than global statistics ($59.3\%$) in 2022 [2]. Additionally, when compared to other age groups, young adults, especially those of university age [33], are much likelier to develop problematic Internet use, because university students may be living independently for the first time [34]. Without monitoring and supervision by parents [34], along with peer pressure to use technology and engage in social comparison behavior (i.e., comparing ourselves to others) [35], university students may lose their boundaries or restrictions and become more vulnerable to developing addictive use (e.g., of the Internet or food intake) than older or younger peers [33]. However, to the present authors’ knowledge, currently there is no study investigating the potential association between social media addiction, psychological distress and food addiction. There is also a lack of research investigating the connections between problematic social media use and food addiction level among the Taiwanese population. Therefore, we proposed a model (Fig. 1) that aimed to investigate the relationship between social media addiction, psychological distress, and food addiction. Moreover, mediation effects of psychological distress were examined when the aforementioned relationships were tested. Accordingly, we hypothesized that [1] social media addiction is positively correlated to psychological distress; [2] psychological distress is positively associated with food addiction; [3] psychological distress mediates the relationship between social media addiction and food addiction. Fig. 1Proposed model to illustrate the potential mechanism and the mediation effect of social media addiction affecting food addiction. Solid line indicates direct effect; dash line indicates indirect effect. BSMAS = Bergen Social Media Addiction Scale; DASS-21 = Depression Anxiety and Stress Scale; YFAS 2.0 = Yale Food Addiction Scale 2.0 ## Participants Participants who met the following inclusion criteria were recruited into the present study: (i) being 20 years old or above; (ii) registered in the program (either undergraduate or postgraduate regardless of study major) of any university in Taiwan when they completed the survey; (iii) had at least one active social media account (e.g., Facebook or Instagram); and (iv) able to read Chinese. The majority of participants were male students ($65.38\%$) with a relatively young age (mean age = 22.8 years; SD = 3.75; ranged from 20 to 45) and an average body mass index (BMI) of 21.98 kg/m2 (SD = 3.71). Specifically, 87 participants ($14.5\%$) were defined as low weight (BMI lower than 18.5); 393 participants ($65.7\%$) were defined as average weight (BMI ranged from 18.5 to 24.9); 118 participants ($19.7\%$) were defined as high weight (BMI higher than 25.0) [36]. ## Demographics and social media usage Demographics information was collected, including age, gender, along with weekly time spent on social media. Moreover, self-reported height and weight were used to calculate BMI (kg/m2). ## Social media addiction The Bergen Social Media Addiction Scale (BSMAS) [37] was used to assess social media addiction. The BSMAS contains 6 items with a 1 to 5 Likert-like scale (1 = seldom; 5 = very often). The item scores were summed to generate a total BSMAS score ranged from 5 to 30, with higher scores indicating more severe social media addiction. A sample item is “I feel an urge to use social media more and more”. The psychometric properties (including construct validity, concurrent validity, test–retest reliability, and internal consistency) of the Chinese version of the BSMAS have been found satisfactory in prior research [38] and demonstrated an excellent internal consistency in the present study (Cronbach’s alpha [α] = 0.96). ## Psychological distress The Depression Anxiety and Stress Scale (DASS-21) [39] was used to assess psychological distress (i.e., depression, anxiety and stress). The DASS-21 contains 21 items (seven items for each type of distress) with a 0 to 3 Likert-like scale (0 = never; 3 = almost always). In the present study, the overall psychological distress score was used. Therefore, the 21 item scores were summed and multiplied by 2 to generate a total score ranged from 0 to 126 [40]. A higher score indicates more severe psychological distress. A sample item is “I found it difficult to relax”. The psychometric properties (including construct validity, concurrent validity, test–retest reliability, and internal consistency) of the Chinese version of DASS-21 have been found satisfactory in prior research [41] and demonstrated an excellent internal consistency in the present study (α = 0.98). ## Food addiction The Yale Food Addiction Scale 2.0 (YFAS 2.0) [42] was used to assess food addiction. The YFAS 2.0 contains 35 items with a 0 to 7 Likert-like scale (0 = never; 7 = everyday). YFAS 2.0 adopted an unique scoring method [42] with the 35 items converted into 11 symptoms with a 0–1 dichotomous scale (0 indicates non-endorsed; 1 indicates endorsed) to obtain a total scores ranging from 0 to 11. A higher score suggests a more severe level of food addiction. A sample item is “I have problems with my family and friends because of how much I ate”. The psychometric properties (including construct validity, concurrent validity, test–retest reliability, and internal consistency) of the Chinese version of the YFAS 2.0 have been found satisfactory in prior research [43], with good internal consistency in the present study (α = 0.87). ## Procedure An online survey hosted on Google Forms was distributed using snowball sampling from August to September, 2021. More specifically, the survey link was sent to various university departments and those who received the survey link were encouraged to disseminate the survey information via the weblink or QR code. The consent form was shown on the first page of the online survey. By clicking the “agree” icon indicated the participants gave their informed consent to participate in the present study. In addition, the participants received 100 New Taiwan dollars reimbursement (approximately $3.3 US) after they completed all the survey questions. The present study was approved by the Institutional Review Board in the Chi Mei Medical Center (IRB Serial No.: 11007-006) and the Human Research Ethics Committee in the National Cheng Kung University (Approval No.: NCKU 144 HREC-E-109-551-2). ## Statistical analysis Demographics and the scores on the three measures were first summarized using descriptive analysis. Then, the correlation coefficients between variables were computed using Pearson’s correlation. Structural equation modeling (SEM) with the estimator of diagonally weighted least squares was used to examine whether the data fit with the proposed models. Because the proposed models contained mediation effect, 100 bootstrapping samples were set to examine the mediation effect. Fit indices of comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA) and standardized root mean squared residual (SRMR) were used to verify if the model was supported. The levels of both CFI and TLI should be > 0.95 and those of RMSEA and SRMR should be < 0.08 [44]. In addition, a total of 2 competing models considering the present of different direct effects were illustrated for model comparisons. The model with lower expected cross validation index (ECVI) is suggested to be the best fitting model. A conceptual model for SEM testing is illustrated in Fig. 1 and the following describes the structures of the 2 competing models. Model 1: Model without correlation between social media addiction and food addiction; Model 2: Based on Model 1 with one additional correlation between social media addiction and food addiction (this is also the proposed model with all the hypotheses mentioned in the Introduction). The SEM was performed using the lavaan package in the R software [45] and the remaining data analyses were performed using the SPSS 22.0 (IBM, Corp., NY: Armonk). ## Results Table 1 displays the background of participants ($$n = 598$$) and the mean scores of measures. The present sample reported an average 3.21 h of daily social media use. Moreover, the mean scores of the measures were 15.90 (SD = 4.78) out of 30 for BSMAS, 26.94 (SD = 25.39) out of 126 for DASS-21, and 1.92 (SD = 2.99) out of 11 for YFAS 2.0. Table 2 displays the correlations between the studies variables. In sum, all three measures were significantly correlated to BMI ($r = 0.10$–0.22) and each other ($r = 0.44$–0.54). In addition, BSMAS was significantly correlated with time spent on social media ($r = 0.29$, p-values < 0.01).Table 1Participants’ characteristics ($$n = 598$$)Mean (SD) or n (%)Age (year)22.8 (3.75)Gender (male)391 (65.38)Body mass index (kg/m2)21.98 (3.71)Time spend on social media (hrs/day)3.21 (2.44)BSMAS score (possible score range: 5–30)15.90 (4.78)DASS-21 score (possible score range: 0–126)26.94 (25.32)YFAS 2.0 score (possible score range: 0–11)1.92 (2.99)BSMAS Bergen Social Media Addiction Scale, DASS-21 Depression Anxiety and Stress Scale, YFAS 2.0 Yale Food Addiction Scale 2.0Table 2Correlations between study variables ($$n = 598$$)12345671Age–2Gender− 0.02 (0.72)–3BMI0.10 (0.01)0.26 (< 0.01)–4Time spend on social media− 0.04 (0.30)− 0.06 (0.28)0.04 (0.30)–5BSMAS0.000 (0.99)− 0.01 (0.78)0.10 (0.02)0.29 (< 0.01)–6 DASS-210.01 (0.77)0.01 (0.83)0.13 (< 0.01)0.03 (0.46)0.44 (< 0.01)–7YFAS 2.00.01 (0.75)0.04 (0.35)0.22 (< 0.01)0.07 (0.08)0.45 (< 0.01)0.54 (< 0.01)–BSMAS Bergen Social Media Addiction Scale, DASS-21 Depression Anxiety and Stress Scale, YFAS 2.0 Yale Food Addiction Scale 2.0. Significant correlations are shown in bold Table 3 demonstrates the fit indices between the two competing SEM models. Briefly, Model 2 demonstrated a relatively good fit when compared to all the other models, with the support of all fit indices (CFI = 0.995; TLI = 0.995; RMSEA = 0.02; SRMR = 0.06; and ECVI = 4.85). The SEM results, shown in Fig. 2, demonstrated the significant correlations of BSMAS to DASS-21 (standardized coefficient [β] = 0.48; $p \leq 0.01$) and DASS-21 to YFAS 2.0 (β = 0.43; $p \leq 0.01$). In addition, BSMAS has an additionally significant association with YFAS 2.0 (β = 0.39; $p \leq 0.01$). Moreover, the mediation effect of DASS-21 (β = 0.21; $p \leq 0.01$) was found in explaining the association between BSMAS and YFAS 2.0 when age, gender and BMI were controlled. More specifically, the unstandardized coefficient ($95\%$ bootstrapping CI) was 0.15 (0.14, 0.16) for indirect effect between BSMAS and YFAS 2.0 via DASS-21. Lastly, BSMAS indirectly mediated the YFAS 2.0 via the DASS-21 with the unstandardized coefficient ($95\%$ bootstrapping CI) of 0.39 (0.36, 0.42).Table 3Fit indices of two competing modelsModel 1Model 2χ2 (df)3526.73 [2010]2622.16 [2009]p-value <.01 <.01CFI0.9880.995TLI0.9870.995RMSEA0.040.02SRMR0.070.06ECVI6.364.85Model 1: Proposed model without the correlation between social media addiction and food addictionModel 2: Proposed modelCFI comparative fit index, TLI Tucker–Lewis index, RMSEA root mean square error of approximation, SRMR standardized root mean square residual, ECVI expected cross validation indexFig. 2Mediation effect with 100 bootstrapping samples of investigated variables. Age and gender were controlled in the model ($$n = 598$$). Solid line indicates direct effect; dash line indicates indirect effect. a Coefficients reported using standardized coefficient. * $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001$; b coefficients reported using unstandardized coefficients with $95\%$ confidence interval in parentheses. BSMAS = Bergen Social Media Addiction Scale; DASS-21 = Depression Anxiety and Stress Scale; YFAS 2.0 = Yale Food Addiction Scale 2.0 ## Discussion The present study investigated the potential mechanism by which social media addiction is associated with food addiction among Taiwan university students, and the findings supported all hypotheses. The results showed the significant associations between social media addiction, psychological distress and food addiction, in which mediation effects of psychological distress was significant. Among the two competing models, Model 2 outperformed Model 1. Model 2 performed better because it contains all the hypotheses and an additional correlation between social media addiction and food addiction. Therefore, the correlation between social media addiction and food addiction could be mediated via psychological distress. The level of social media addiction was significantly associated with individuals’ psychological distress in the present study. Previous studies reported that among different types of problematic use of the Internet, PSMU has the most salient effect on dysregulating emotional management [46, 47]. More specifically, individuals with poor psychological well-being tend to adopt social media use as a coping strategy [48] and may feel compelled to frequent use in order to avoid their problems [49]. The situation becomes a vicious circle, because the behavior of using social media as a form of avoidance may increase vulnerability of individuals to developing PSMU [48]. In turn, the addictive behavior continues to degrade their psychological health [18, 19], which results in a higher level of addictive behavior [49]. This pattern was supported by the present finding. Other studies additionally suggested a causal link between PSMU and depressive symptoms [50] and demonstrated that it is “the number of used social media platforms” rather than “the total used time”, that is associated with psychological symptoms such as depression, anxiety [51], and body image [52]. The results suggest that more detailed consideration is needed for the investigation of potential social media factors that promote psychological distress [51]. The significant association between psychological distress and food addiction found in the present study corroborate previous findings [28, 29, 31, 53, 54]. Two issues may contribute to this association: [1] lack of control over eating and [2] weight stigma. Self-control skills could be a potential explanation for the association [53] because lack of self-control can result in impulsivity and contribute to food addiction [54–56]. Psychological distress (e.g., resulting from receiving negative information from social media) may trigger excessive emotional eating to cope with stress, which could result in food addiction, particularly in the presence of poor self-control skills [57–59]. Another important factor explaining our finding is weight-related stigma. The correlation between weight-related stigma and food addiction had been well-reported [60]. For example, the fear of being stigmatized predicted greater food addiction [61]. Additionally, weight stigmatization may cause psychological distress [62]. Furthermore, the internalization of weight-related stigma and psychological distress may exacerbate the relationship between weight stigma and disordered eating [63]. Social media addiction may increase body dissatisfaction and lead to the internalization of weight-related stigma, which may result in the psychological distress and further amplify food addiction. Indeed, our findings showed that social media addiction was associated with psychological distress and food addiction. Therefore, interventions such as psychological acceptance or mindfulness therapy may reduce the negative impact of weight stigmatization [62]. The present study demonstrated a possible mechanism of PSMU in relation to food addiction among university students, which included the involvement of psychological distress. However, the present study had several limitations. First, the self-reported variables may be subject to social desirability bias (e.g., participants might have underestimated their social media usage) or recall bias (e.g., participants might not accurately remember their height and weight). Second, the unique characteristics of this university sample may limit generalizability to populations with different age. Third, the cross-sectional design of the present study cannot test the causal directionality of this mechanism and a longitudinal study is merited to provide more information. Despite that, the present findings still provided evidence of the possible negative correlates of social media addiction. Strategies aimed at promoting emotional regulation, self-control skills or reducing the weight stigma, such as mindfulness exercises [53, 62], cognitive restructuring [64], or food consumption monitoring [65], can be taught to the individuals with social media addiction, to lower these unwanted consequential effects. ## Conclusions The present study investigated the potential mechanism of social media addiction in relation to food addiction among Taiwanese university students. The results showed an association between social media addiction and psychological distress, with psychological distress linked to food addiction. The present study suggests several important negative correlates of social media addiction. Interventions to reduce social media addiction and food addiction might alleviate the negative consequences of these behaviors. Strategies to improve self-control skills or reduce weight stigmatization, such as food-intake monitoring, mindfulness and cognitive restructuring, can be taught to individuals with food addiction to neutralize its negative consequences. ## References 1. Carr CT, Hayes RA. Social media: defining, developing, and divining. Atl J Commun.* (2015.0) 23 46-65. DOI: 10.1080/15456870.2015.972282 2. 2.DataReportal. Global social media statistics 2022. https://datareportal.com/social-media-users. Accessed 30 Oct 2022. 3. Geirdal AO, Ruffolo M, Leung J, Thygesen H, Price D, Bonsaksen T. 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--- title: 'ALDOC- and ENO2- driven glucose metabolism sustains 3D tumor spheroids growth regardless of nutrient environmental conditions: a multi-omics analysis' authors: - Claudia De Vitis - Anna Martina Battaglia - Matteo Pallocca - Gianluca Santamaria - Maria Chiara Mimmi - Alessandro Sacco - Francesca De Nicola - Marco Gaspari - Valentina Salvati - Francesca Ascenzi - Sara Bruschini - Antonella Esposito - Giulia Ricci - Eleonora Sperandio - Alice Massacci - Licia Elvira Prestagiacomo - Andrea Vecchione - Alberto Ricci - Salvatore Sciacchitano - Gerardo Salerno - Deborah French - Ilenia Aversa - Cristina Cereda - Maurizio Fanciulli - Ferdinando Chiaradonna - Egle Solito - Giovanni Cuda - Francesco Costanzo - Gennaro Ciliberto - Rita Mancini - Flavia Biamonte journal: 'Journal of Experimental & Clinical Cancer Research : CR' year: 2023 pmcid: PMC10031988 doi: 10.1186/s13046-023-02641-0 license: CC BY 4.0 --- # ALDOC- and ENO2- driven glucose metabolism sustains 3D tumor spheroids growth regardless of nutrient environmental conditions: a multi-omics analysis ## Abstract ### Background Metastases are the major cause of cancer-related morbidity and mortality. By the time cancer cells detach from their primary site to eventually spread to distant sites, they need to acquire the ability to survive in non-adherent conditions and to proliferate within a new microenvironment in spite of stressing conditions that may severely constrain the metastatic process. In this study, we gained insight into the molecular mechanisms allowing cancer cells to survive and proliferate in an anchorage-independent manner, regardless of both tumor-intrinsic variables and nutrient culture conditions. ### Methods 3D spheroids derived from lung adenocarcinoma (LUAD) and breast cancer cells were cultured in either nutrient-rich or -restricted culture conditions. A multi-omics approach, including transcriptomics, proteomics, and metabolomics, was used to explore the molecular changes underlying the transition from 2 to 3D cultures. Small interfering RNA-mediated loss of function assays were used to validate the role of the identified differentially expressed genes and proteins in H460 and HCC827 LUAD as well as in MCF7 and T47D breast cancer cell lines. ### Results We found that the transition from 2 to 3D cultures of H460 and MCF7 cells is associated with significant changes in the expression of genes and proteins involved in metabolic reprogramming. In particular, we observed that 3D tumor spheroid growth implies the overexpression of ALDOC and ENO2 glycolytic enzymes concomitant with the enhanced consumption of glucose and fructose and the enhanced production of lactate. Transfection with siRNA against both ALDOC and ENO2 determined a significant reduction in lactate production, viability and size of 3D tumor spheroids produced by H460, HCC827, MCF7, and T47D cell lines. ### Conclusions Our results show that anchorage-independent survival and growth of cancer cells are supported by changes in genes and proteins that drive glucose metabolism towards an enhanced lactate production. Notably, this finding is valid for all lung and breast cancer cell lines we have analyzed in different nutrient environmental conditions. broader Validation of this mechanism in other cancer cells of different origin will be necessary to broaden the role of ALDOC and ENO2 to other tumor types. Future in vivo studies will be necessary to assess the role of ALDOC and ENO2 in cancer metastasis. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13046-023-02641-0. ## Background Metastasis is a multi-step process that includes the degradation and the detachment from the extracellular matrix (ECM) of the primary site, the invasion of vascular and lymphatic vessels, and the formation of secondary tumors in remote sites [1–3]. Among these events, the detachment from the ECM and the survival within the circulation in the absence of cell–cell and cell-ECM stimuli are crucial factors determining metastatic outcome [4, 5]. ECM-independent survival is a stressful event during which cells suffer from loss of integrin-mediated growth signals, cytoskeletal reorganization, diminished nutrient uptake, and increased reactive oxygen species (ROS) production [6]. The vast majority of cancer cells fail to adapt to these damaging events and, consequently, undergo various forms of cell death, such as anoikis, autophagy, and cell cycle arrest [5, 7]. However, a small percentage of cancer cells, provided with stem cell properties and invasion capabilities, by virtue of their powerful ability to adapt, to reprogram cellular energetics and signaling pathways, evade cell death and thus drive tumor progression [8]. To make a few examples, metastatic cancer cells abnormally enhance the autocrine signaling of growth factors, namely fibroblast growth factor (FGF) and epidermal growth factor (EGF), to activate the pro-survival PI3K/Akt, Ras/MAPK, NF-κB, and Rho-GTPase signaling pathways [9]. Moreover, metastatic cancer cells leave the primary site in the form of clusters instead of single units, and clusters have been reported to restrain anoikis by re-establishing cell–cell contacts [2]. Once in the bloodstream, tumor cells closely interact with activated platelets, whose release of tumor grow factor- beta (TGF-beta) also protects against the lack of cell-ECM interactions present in circulation, by inducing a mesenchymal-like phenotype [10, 11]. The activation of platelets also implies the release of fibrinogen and tissue factor, which protect circulating tumor cells against immune clearance [12]. During the last decade, growing evidence highlighted that ECM detachment is tightly associated with drastic cancer cell metabolic alterations, to the point that metabolic dependences may provide potential targets to restrain tumor progression. Metabolic reprogramming is widely recognized as a hallmark of cancer. In this regard, it has been demonstrated that cancer cells preferentially utilize the glycolytic pathway to produce large amounts of lactate even in the presence of oxygen, a phenomenon known as the “Warburg effect”. However, depending on the tumor type and the nutrient environmental conditions, cancer cells may rely on mitochondrial oxidative phosphorylation (OxPhos) or glutamine metabolism to sustain the malignant phenotype. On the other hand, both the presence and the abundance of nutrients within the local microenvironment may determine the metabolic phenotype that cancer cells adopt to accomplish each stage of the metastatic process. Indeed, ECM detachment causes defective glucose utilization, reduces pentose phosphate pathway (PPP), diminishes adenosine triphosphate (ATP) production, and increased ROS generation [13–15]. Despite advances in this field during the last decade, nutrient demands and related mechanisms that sustain the survival of cancer cells following ECM detachment have not been sufficiently elucidated. Moreover, whether these requirements are cancer type-dependent or rather more general phenomena are issues still insufficiently understood. In this study, we used a multi-omics approach to widely explore the molecular mechanisms utilized for anchorage-independent cancer cell growth in response to a diverse availability of growth factors and nutrients. To this, we set up an in vitro experimental system based on the growth of 3D tumor spheroids derived from lung adenocarcinoma (LUAD) and breast cancer cell lines in customized nutrient- and growth factors-rich or -restricted culture media. ## Cell lines and culture conditions The human H460 and HCC827 LUAD and MCF-7 and T47D breast cancer cell lines were purchased from the America Type Culture Collection (ATCC-LGC Promochem, Teddington, UK). For 2D culture conditions, H460, HCC827 and T47D cells were grown in RPMI1640 medium (Sigma-Aldrich, St. Louis, MO, USA) while MCF7 were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) medium (Sigma-Aldrich, St. Louis, MO, USA). Both media were supplemented with $10\%$ fetal bovine serum (FBS) (Invitrogen, San Diego, CA) and $1\%$ (v/v) penicillin and streptomycin (Sigma-Aldrich, St. Louis, MO, USA). All cell lines were maintained at 37 °C in humidified $5\%$ CO2 atmosphere. Cells were passed twice per week using trypsin, thus leading gentle cell detachment. Cell lines were tested for mycoplasma contamination and STR profiled for authentication. 3D tumor spheroids were grown in two different culture conditions: i) a customized nutrient-rich spheroid medium (3D_SM), consisting of DMEM/F-12 (Sigma-Aldrich, St. Louis, MO, USA) supplemented with $0.5\%$ Glucose (Sigma-Aldrich, St. Louis, MO, USA), 2.5 mM L-Glutamine (Thermo Fisher Scientific, Waltham, MA, USA) [16], $2\%$ B-27, 5 μg/ml Heparin, 20 μg/ml Insulin (Thermo Fisher Scientific, Waltham, MA, USA), 20 ng/ml EGF (Thermo Fisher Scientific, Waltham, MA, USA), 20 ng/ml Recombinant Human bFGF (Thermo Fisher Scientific, Waltham, MA, USA), $0.1\%$ Bovine Serum Albumin (BSA) (Sigma-Aldrich, St. Louis, MO, USA) and $1\%$ (v/v) of penicillin/streptomycin 100U/ml, as previously described by Lobello et al. [ 17]; ii) customized nutrient-restricted RPMI or DMEM culture media supplemented with only $2\%$ FBS (3D_FBSlow). Overall, the final concentrations of D-Glucose and L-Glutamine in each 3D culture condition are reported in Table 1.Table 1Concentrations of D-Glucose and L-Glutamine of the cell culture media used for 3D conditions3D culture conditionCell lineD-GlucoseL-GlutamineSphere Medium (SM)H460, HCC827, MCF7, T47D45 mM4.99 mMRPMI FBSlowH460, HCC827, T47D25.11 mM3.99 mMDMEM FBSlowMCF711.24 mM2.05 mM Briefly, 20,000 cells/mL were resuspended in an appropriate amount of each medium and seeded onto ultra-low attachment plates (Corning Costar, MA, USA) to form 3D structures. After 4 days, the collected tumor spheroids were resuspended in appropriate volume of culture medium and counted by using Leica Thunder Dmi8 microscope according to the following formulas:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$sphere\;concentration=sphere\;count\;\div counting\;volume\;(\mu L)$$\end{document}sphereconcentration=spherecount÷countingvolume(μL)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$total sphere count=sphere concentration\times total volume$$\end{document}totalspherecount=sphereconcentration×totalvolume Their diameters were measured through the internal image measuring feature normalized to 100 3D spheroids using imaging software Zen (Leica). Data are reported as mean ± Standard Deviation (SD). ## RNA-seq Total RNA was extracted using Qiazol (Qiagen, IT), purified from DNA contamination through a DNase I (Qiagen, IT) digestion step and further enriched by Qiagen RNeasy columns for gene expression profiling (Qiagen, IT) [18]. Quantity and integrity of the extracted RNA were assessed by NanoDrop Spectrophotometer (NanoDrop Technologies, DE) and by Agilent 2100 Bioanalyzer (Agilent Technologies, CA), respectively. RNA libraries for sequencing were generated in triplicate using the same amount of RNA for each sample according to the Illumina TruSeq Stranded Total RNA kit with an initial ribosomal depletion step using Ribo Zero Gold (Illumina, CA). The libraries were quantified by qPCR and sequenced in paired-end mode (2 × 75 bp) with NextSeq 500 (Illumina, CA). ## RNA-seq bioinformatics analysis For each sample generated by the Illumina platform, a pre-process step for quality control was performed to assess sequence data quality and to discard low-quality reads. Primary analysis was carried out with Nextflow nf-core/rnaseq pipeline [19, 20]. Secondary analysis, including differential expression analysis, functional enrichment and inter-comparison GO data visualization were entirely carried out with an in-house package, auto-GO [1], which makes use of the DeSeq2, Enrichr and tidyverse [21–23]. Differentially expressed genes (DEGs) were considered strongly regulated with the DESeq2 results table filtered via absolute log2(Fold Change) > 1 and padj < 0.05. All the functional enrichment was carried out via the enrichR libraries “GO_Cellular_Component_2021”, “GO_Biological_Process_202” and “KEGG_2021_Human”. Significance for functional cluster was set at padj < 0.1. ## Protein digestion Protein digestion was performed by filter-aided sample preparation (FASP) as previously described [24]. An aliquot of the digest (50 μL) was purified by SCX StageTips [24]. Peptides were eluted from StageTips using 7 μL of 500 mM ammonium acetate, $20\%$ acetonitrile (v/v). The eluate was mixed with 45 μL of $0.5\%$ formic acid to lower the organic content below $3\%$ before nanoLC-MS/MS analysis. For generating the spectral library, 8 μL aliquots were withdrawn from each sample and pooled into a single sample. The mix was then loaded onto two separate SCX StageTip fabricated by stacking two plugs of SCX material (Empore extraction disks, Millipore) for higher capacity. Stepwise elution in 8 fractions was achieved by adding eluents of increasing ionic strength. Eluents contained $20\%$ acetonitrile, $0.5\%$ acetic acid (except fraction 8) and increasing amounts of ammonium acetate: 40, 70, 100, 150, 200, 250, 350, 500 mM. The eluates of both StageTips were combined, evaporated, resuspended in 20 μL of mobile phase A and analysed by nanoLC-MS/MS. ## NanoLC-MS/MS analysis NanoLC-MS/MS analysis was performed on EASY1000 LC system coupled to Q-Exactive “classic” mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Peptides were separated using an in-house made analytical column packed with 3 μm-C18 silica particles. A 2 μL aliquot was injected for each sample analysed in data-independent mode (DIA). The analytical system and nanoLC-MS/MS conditions were previously described [24]. Gradient elution of peptides was achieved at 300 nL/min using a 120 min gradient (from $4\%$ B to $28\%$ B in 90 min, then from $28\%$ B to $50\%$ B in 30 min). Mobile phase A consisted of $97.9\%$ water, $2\%$ acetonitrile, $0.1\%$ formic acid, whereas mobile phase B consisted of $19.9\%$ water, $80\%$ acetonitrile, and $0.1\%$ formic acid. The nanoLC effluent was directly electrosprayed into the mass spectrometer in positive ion mode (1800 V). The mass spectrometer operated in DIA mode, using 26 sequential acquisition windows covering an m/z range of 350-1200 [24]. For library generation, the 8 fractions obtained by StageTip fractionation were analysed using identical chromatographic conditions and operating the mass spectrometer in data-dependent mode using a TOP12 method: a full MS scan at resolution of 70,000, with AGC value at 1.0 × 106 and m/z range of 350–1800, followed by 12 MS/MS scans acquired at 35,000 resolutions using AGC value of 1.0 × 105. Normalized collision energy was set at $25\%$, isolation window was 1.6 m/z and maximum injection time was 120ms for MS/MS scans. Finally, dynamic exclusion was set at 20.0 s. Injected amounts were 4 μL for fractions 1-5 and 8 μL for fractions 6-8. ## Proteomics data processing Library generation was achieved in Spectronaut Pulsar (Biognosys, v.13) using default parameters [25]. MS/MS spectra were searched against the Uniprot human protein database accessed on May 20th, 2020 (74,823 sequences). DIA data analysis was performed on the same platform (Spectronaut) using default parameters. The number of peptides used for quantification was between 1 and 10 (unique peptides), data filtering was based on q-value and no normalization factor was adopted in Spectronaut. Then, we used the default Spectronaut long format input to remove low-intensity ions and perform median normalization using iq package [26]. The generated protein table with log2 ratios without missing values was used for differential protein expression analysis by limma [27]. Differential expressed proteins (DEPs) were selected by an absolute log2 \|FC\|> 1 and based on a p-value ≤ 0.01. Finally, DEPs were intersected with DEGs resulting from an absolute log2 \|FC\|> 1 and p-value < 0.05 filter. Pathway enrichment analysis was performed using GSEABase [28] annotations and clusterProfiler [29]. A Benjamini–Hochberg FDR cutoff of 0.05 was used for the analysis. ## Hydrophilic metabolites extraction and quantification Cell metabolic profiling was conducted on H460 and MCF7 cells grown as 2D, as well as 3D_SM and 3D_FBSlow tumor spheroids. A total of 9 sample (3 technical replicates × 3 biological replicates) for each of the 6 cultures (2 in 2D and 4 in 3D) was analyzed. For the whole procedure HPLC-grade solvents and ultrapure Milli-Q water were used. Hydrophilic metabolites extraction was accomplished following the protocol of Yuan et al. [ 30]. Briefly, hydrophilic metabolites were extracted from 1 × 106 cells by: (i) addition of 4 ml of $80\%$ (vol/vol) methanol:water (cooled to − 80 °C) containing 65 ng of Reserpine as internal standard (IS), (ii) transfer of the cell lysate/methanol mixture to conical tubes and (iii) centrifugation at 14,000 g for 5 min at 4–8 °C to pellet the cell debris. ( iv) The pellet was re-extracted with 0.5 ml of $80\%$ (vol/vol) methanol/water and (v) the obtained supernatants were united and dried by SpeedVac without heating. Cells and cell lysates were maintained refrigerated on dry ice during the extraction procedure. Each dried extract was reconstituted in 100 μL of methanol: water (50:50 v/v) mixture before LC–MS/MS analysis. Five microliters of each sample were injected into the mass spectrometer (QTRAP® 3200 System, Sciex), which includes an HPLC module (Exion LC-100 HPLC, Shimadzu) for quantification of metabolites. A Luna HILIC-NH2 column, 2,6 μm, 50 × 2,1 mm (Phenomenex) coupled with a SecurityGuard Cartridge HILIC-NH2, 2,1 mm column (Phenomenex) was used for chromatographic separation. Mobile phase A was composed of 20 mM ammonium acetate, in water: acetonitrile, $95\%$ (vol/vol); the pH was adjusted to 8 with ammonium hydroxide before the addition of CH3CN; mobile phase B was $100\%$ acetonitrile. Oven temperature was 25 °C. The chromatographic gradient, from 85 to $2\%$ mobile phase B in 25 min at 0.35 mL/min flow rate, was adapted by Bajad et al. [ 31]. The eluting metabolites were analyzed with the mass spectrometer operating in positive and negative mode, using the Multiple Reaction Monitoring (MRM) approach for targeted profiling. Optimized electrospray ionization parameters were electrospray voltage of respectively 5000/-4500 V in positive/negative mode, temperature of 350 °C, curtain gas of 30 psi, nebulizer gas (GS1) and auxiliary gas (GS2) of 40 and 40 psi, respectively. Dwell time for each MRM transition was 5 ms. Compound dependent parameters, i.e., Declustering Potential (DP), Entrance Potential (EP), Collision Cell Entrance Potential (CEP), Collision Energy (CE) and Collision Cell Exit Potential (CXP) were adapted from an internal Sciex report [32]. and validated on a subset of 24 commercial molecular standards, including all classes of target metabolites. The instrument was mass calibrated with a mixture of polypropylene glycol (PPG) standards. Quality controls and carry-over checks were included with each samples batch. Acquisition was performed by Analyst 1.6.3 software (Sciex). ## Metabolomic data processing All statistical and correlation analyses were done using MetaboAnalyst 5.0 [33]. Data were normalized versus the IS Reserpine. Hierarchical cluster analysis was performed after autoscaling of data, selecting Euclidean Distance as similarity measure parameter and Ward’s linkage as clustering algorithm. Student’s t test or one-way ANOVA test, followed by post-hoc analyses, were used to compare the relative concentration of metabolites respectively among two or more groups; the p-value significance threshold was set at 0.05. ## 3D cell viability assay Cell titer-Glo 3D (Promega) was used to determine viability of 3D cells plated in single wells of a 96 well ultra-low-attachment culture plate. The assay was performed in triplicate according to manufacturer’s instructions. Samples were read on the GloMax Explorer Luminometer (Promega) [34]. ## Scanning Electron Microscopy (SEM) analysis Glutaraldehyde-fixed samples were rinsed with a cacodylate buffer and then dehydrated with an increasing ethanol percentage (30–$90\%$ in water for 5 min, twice $100\%$ for 15 min), treated in a Critical Point Dryer (EMITECH K850), sputter coated with platinum-palladium (Denton Vacuum DESKV), and observed with Supra 40 FESEM (Zeiss). ## ALDOC and ENO2 transient silencing siRNA transfections were performed as previously described [35, 36]. For each gene we used three different siRNAs: siENO2 (Assay ID s4685, Assy ID 10894, Assy ID 121347); ALDOC (sc-270351, Santa Cruz) and (Assay ID 15795 and Assay ID 121526, Thermo Fisher Scientific, Waltham, MA, USA). Control siRNAs were purchased from (Thermo Fisher Scientific, Waltham, MA, USA). DNA transfections were performed with Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. ## RNA extraction, cDNA synthesis and real time PCR RNA was harvested with TRIzol (Thermo Fisher Scientific, Waltham, MA, USA) as previously described [37–43]. Total RNA (1 μg) was digested with gDNAse Eraser and reverse-transcribed with PrimeScript RT reagent Kit, (Takara Bio Inc). The expression levels of ALDOC and ENO2 were analyzed by using 7500 Step One Plus (Applied Biosystems). The primer sequences used are: ALDOC forward 5-CATTCTGGCTGCGGATGAGTC-3, reverse 5-CACACGGTCATCAGCACTGAAC-3; ENO2 forward 5-AGCCTCTACGGGCATCTATGA-3, reverse: 5-TCAGTCCCATCCAACTCC-3. H3 was included as housekeeping for normalization of real time data [44]. ## Intracellular glucose and lactate quantification assays Analysis of intracellular glucose and lactate quantities were performed by using Glucose-Glo assay and Lactate-Glo Assay, respectively (Promega, Madison, WI, USA). Briefly H460 and MCF7 cells were seeded in 6-well plates and then transfected with the two siRNAs specific for ALDOC and ENOs. After transfection, both cell lines were cultured in 96-well ultra-low-attachment plates. Analysis was performed through GloMax Explorer Luminometer (Promega). Data were normalized to cell number. Analyses were performed in triplicate and results are reported as mean ± SD. ## Extracellular lactate quantification assays The quantification of L-lactic acid present in the culture media was performed by using Emogas analyzer Gem 5000, according to the manufacturer’s instructions. The amount of L-lactic acid produced by the cells in each sample was calculated subtracting the amount of L-lactic acid in the media (without cells) from the amount of lactate in the media from each sample. ## Statistical analysis Data were analysed in GraphPad Prism 9. Comparison of more than two groups was performed using one-way ANOVA analysis. Student t test was used for two-groups comparisons. A p-value < 0.05 was considered statistically significant. Integrated analysis of RNA-seq and Proteomics was carried out via the novel STAR protocol presented by Yang et. al [45] which employs a mixture of differential analysis techniques and custom R statistical mining on both datasets. ## Design and setting of the study This study aimed to gain insight into the molecular mechanisms allowing cancer cells to survive and proliferate under detached conditions, regardless of both tumor-intrinsic variables and nutrient culture conditions. To this purpose, we used 3D tumor spheroids as in vitro experimental models to mimic anchorage-independent cancer cell growth as well as to mimic fluctuation in nutrient and oxygen availability that cells undergo as tumor mass grows and expands in vivo. 3D tumor spheroids derived from LUAD, and breast cancer cell lines were grown in two culture conditions: i) sphere medium (SM), which mimics an environment rich in major nutrients (glucose and L-glutamine) and growth factors (Epidermal Growth Factor, EGF, and basic Fibroblast Growth Factor, bFGF) and ii) RPMI or DMEM FBSlow, supplemented with only $2\%$FBS, mimicking a nutrient-restricted culture condition. A wide multi-omic approach, based on the integration of transcriptomic, proteomic, and metabolomic analyses, was used to identify the common molecular changes occurring during all the transitions from adherent 2D to 3D cultures, regardless of the tumor type and nutrient culture availability. Small interfering RNA-mediated loss of function assays were used to validate the role of the identified differentially expressed genes and proteins in LUAD and breast cancer cell lines (Fig. 1).Fig. 1Workflow of the multi-omics integrative analysis. Biological System: H460 LUAD and MCF7 breast cancer cell lines were cultured in 2D and 3D conditions. 3D tumor spheroids were grown either in a nutrient-rich (sphere medium, SM) or in a nutrient-restricted (FBSlow) culture media. Input: a total of 6 samples (H460 2D, H460 3D_SM, H460 3D_ FBSlow, MCF7 2D, MCF7 3D_SM, MCF7 3D_ FBSlow) were characterized through transcriptomic, proteomic and metabolomic analyses; differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) between 3D vs 2D samples were further analyzed by using Gene Ontology (GO). Integration: DEGs and DEPs were integrated to identify a common signature of DEGs and DEPs in all the 2D to 3D transitions. ALDOC and ENO2 were found up-regulated in all 3D vs 2D culture conditions. Validation: siRNA-mediated knock down of ALDOC and ENO2 were performed to functionally validate the effects of these two enzymes on 3D tumor spheroids growth. Output: ALDOC and ENO2 represent putative drivers of the metabolic reprogramming responsible for the sphere-forming ability of H460 and MCF7 cells ## RNA-seq analysis highlights that H460 and MCF7 require the rewiring of genes involved in the metabolic programmes to grow in 3D culture conditions Cancer cells grown in 3D cultures show distinct gene expression patterns when compared to the same parental cells grown in 2D conditions [46–48]. Indeed, differential extracellular interactions with ECM as well as different nutrient availability within 3D models change intracellular signal transduction, culminating in the activation of a unique set of transcription factors and in significant changes of the transcriptomic profiles [49, 50]. Here, we first shed light on the transcriptional reorganization associated with 3D cell growth in nutrient-rich or nutrient-restricted culture conditions. H460 and MCF7 cells (1.5 × 104/mL) were grown in non-adherent conditions either in nutrient-rich sphere medium (SM) or in a nutrient-restricted culture medium (FBSlow). After 4 days, which was previously established as the optimal time frame to collect first generation of tumor spheroids [51], we performed RNA-seq analysis of H460 and MCF7 grown either as a monolayer (2D) or as 3D_SM and 3D_ FBSlow. Overall, differential expression analysis (DEA) highlighted a total of 2169 DEGs when comparing H460 2D vs 3D_SM and 1478 DEGs when comparing H460 2D vs 3D_FBSlow (Table S1, in Additional file 1). For MCF7 cells, 1925 DEGs emerged from the comparison between 2D vs 3D_SM while 2222 DEGs resulted from 2D vs 3D_FBSlow (Table S1, in Additional file 1). Then, we sought to find a common transcriptional signature associated with 3D tumor spheroid growth, namely genes and processes significantly up or down regulated in the transition from 2 to 3D, regardless of the cell type and the culture media utilized. A signature of 100 genes was found to be commonly regulated in all the systems; among these, 84 genes were commonly up regulated while 16 were commonly down regulated in all 3D vs 2D conditions (Fig. 2A). Interestingly, functional enrichment analysis on the common DEGs highlighted that among the enriched biological processes, most of them (17 out of 20) were associated with cellular metabolism. In particular, Glycolysis/Gluconeogenesis appeared the most consistently enriched metabolic pathway because of the up regulation of 6 out of 10 glycolytic enzymes (hexokinase 2, HK2; pyruvate kinase muscle isozyme, PKM; phophoglycerate kinase 1, PGK1; aldolase C, ALDOC; enolase 2, ENO2; glyceraldehyde 3-phosphate dehydrogenase, GAPDH) and of phosphoglucomutase 1 (PGM1). Notably, both HK2 and PKM encode for muscle-specific isoenzymes involved in the regulation of two irreversible steps of glycolysis [52]. ALDOC and ENO2 encode for neuronal-specific aldolase and enolase isoforms [53, 54]. HK2 catalyses the first priming and irreversible reaction of glycolysis, the conversion of the substrate glucose into glucose-6-phosphate, ALDOC is the key enzyme of the fourth step of glycolysis, during which fructose -1,6-bisphosphate is converted to gylceraldehydes-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP). GAPDH, PGK1, ENO2, and PKM catalyze 4 out of the 5 reactions of the energy-releasing phase of glycolysis [55, 56]. PGM1 belongs to the phosphohexose mutase family and catalyzes the transfer of phosphate between the 1 and 6 positions of glucose; as such, it is involved in both the synthesis and degradation of glycogen [53, 57–59]. As the second most significantly affected biological process in the 2D to 3D transition, the cellular response to hypoxia (HIF-1 signalling pathway) was enriched by the up regulation of BNIP3, BNIP3L, EGLN3, FAM162A, HILPDA, PGK1, RORA, NDRG1 (Fig. 2B-C) (Tables S2-3, in Additional files 2 and 3). In addition to the glycolytic enzyme PGK1, EGLN3 is a member of the 2-oxoglutarate (2OG)–dependent dioxygenases family responsible for the prolyl hydroxylation of HIF-1//2 and for the regulation of cell apoptosis in response to hypoxia [60]. Similarly, BNIP3, BNIP3L, and FAM162A are involved in the regulation of cell death in response to hypoxic conditions [61, 62]. In particular, the BH3-only proapoptotic genes BNIP3 and BNIP3L enhance autophagy and, in particular, mitophagy to overcome cell death and guarantee survival under hypoxic conditions [63]. N-myc downstream-regulated gene-1 (NDRG1) is a hypoxia inducible-protein involved in the p53-mediated activation of the caspase cascade; furthermore, it influences the epithelial to mesenchymal transition (EMT) as it is required for the vesicular recycling of e-cadherin and for the cadherins switching [64, 65]. The hypoxia-inducible and lipid droplet-associated protein HILPDA is known to promote lipid droplets formation in response to hypoxia as well as to autophagic flux induced by nutrient deprivation [66]. RORA is a hypoxia-induced member of the retinoic acid-receptor-related orphan receptor α superfamily; unlike the other members of this family, RORA binds to the promoter of cell cycle-related genes and N-myc, thus affecting cell growth and tumorigenesis [67]. Finally, the GO cell component analyses highlighted that PKM, ALDOC, AMPD3, PGM1, EFEMP2, and RAB3A, are up-regulated in all 3D vs 2D culture conditions, consistently enriched the ficolin-1-rich granule lumen and extracellular vesicles (EVs) (Table S4, in Additional file 4). Together with the already described PKM, ALDOC and PGM1, the Adenosine Monophosphate Deaminase 1 (AMPD3), encoding for the red blood cells (RBC)-specific member of the adenosine monophosphate (AMP) deaminase family, catalyzes the irreversible hydrolytic deamination of AMP to inosine monophosphate (IMP), thus it is involved in purine nucleotide, uric acid, and carbohydrate metabolism [68]. Recent reports indicate that, in RBCs, AMPD3 can be activated by the increased intracellular levels of ROS and calcium, along with decreased intracellular pH [69]. The exact role of AMPD3 in cancer is instead still unclear; however, since it controls the intracellular levels of AMP, it is reasonable to hypothesize that it might affect AMP-activated protein kinase (AMPK). AMPK is largely recognized as a key energy sensor. In response to diverse stressors, such as glucose starvation, hypoxia, and oxidative damage, it activates ATP-producing pathways [70]. In agreement, according to several studies, AMPK deficiency renders cancer cells more vulnerable to the stresses induced by cell detachment [71]. EFEMP2 (EGF Containing Fibulin Extracellular Matrix Protein 2) gene encodes for a member of fibulin glycoprotein family, involved in the stabilization of the ECM structure; indeed, it is necessary for elastic fiber formation, and it is involved in collagen fibril assembly. So far, the role of EFEMP2 in tumorigenesis is found to be “context-specific”; indeed, while in cervical cancer, ovarian cancer, and glioblastoma it has been associated with tumor progression and poor prognosis, in endometrial cancer it has been found to inhibit EMT, tumor invasion and metastasis [72]. Finally, Rab3A belongs to the small Ras-like GTPase superfamily and functions as a key regulator in transporting cellular products into secretory vesicles and lysosomes [73]. Normally Rab3A is predominantly expressed in the neural system; however, it has been found aberrantly overexpressed in breast cancer where it is associated with a more malignant phenotype and in hepatocellular carcinoma where, instead, it inhibits metastasis via enhancing mitochondrial oxidative metabolism [74].Fig. 2Transcriptomic analysis of H460 and MCF7 cell lines grown in 2D and 3D culture conditions. A Heatmap of 100 DEGs in all 3D vs 2D conditions of both cell lines. Color intensity is proportional to the magnitude of changes. Relative expression levels are shown in red (upregulation) and blue (downregulation). B GO analysis of cellular component, C biological process, and D KEGG pathway analysis of DEGs in all 3D vs 2D conditions of both cell lines. The dot size denotes the number of DEGs, while colors correspond to the adjusted p-value range Overall, RNAseq data suggest that the ability of cancer cells to survive and grow in 3D culture conditions requires the rewiring of intracellular metabolic pathways and the control of redox homeostasis most likely in response to the decreased oxygen levels. ## Proteomic analysis confirms that H460 and MCF7 cells reprogram their glucose metabolism to survive in all 3D culture conditions Once identified the gene expression signature associated with 3D tumor spheroid growth, we analyzed the proteomic profiles of H460, and MCF7 3D tumor spheroids grown either in SM or in FBSlow conditions and compared them to their relative 2D cultures. By using an absolute log2 \|FC\|> 1 and a p-value < 0.01, we identified a total of 534 DEPs in H460 3D_SM vs H460 2D, $$n = 413$$ DEPs in H460 3D_FBSlow vs H460 2D, $$n = 216$$ DEPs in MCF7 3D_SM vs MCF7 2D, and $$n = 222$$ DEPs in MCF7 3D FBSlow vs MCF7 2D (Table S5, in Additional file 5). Among these, 2 proteins (MRPL41 and MRPL24) were down regulated while 7 proteins (ALDOA, ALDOC, NOL3, ENO2, SH3BGRL, DBI, HEBP2) were up regulated in both H460 and MCF7 3D vs 2D conditions (Fig. 3A). Both the commonly down regulated proteins MRPL41 and MRPL24 are component of mitochondrial ribosomes (mitoribosomes) large 39S subunit and are involved in the synthesis of mitochondrial electrons transport chain (ETC) components [75, 76]. Among the commonly up regulated proteins, as already discussed above, the two isoenzymes ALDOA and ALDOC as well as ENO2 are glycolytic enzymes, NOL3 acts as apoptosis repressor, often in response to hypoxia, by inhibiting the release of cytochrome c from mitochondria [77], the Acyl-coA-binding protein DBI is a lipogenic factor that regulates fatty acids metabolism [78], the heme binding protein 2 (HEBP2) is involved in heme metabolism but it also enhances the outer and inner mitochondrial membrane permeabilization, especially under oxidative stress conditions [79]. The SH3 Domain Binding Glutamate Rich Protein Like (SH3BGRL) is located within the extracellular vesicles and as a scaffold protein it mediates many protein–protein interactions; however, its role in cancer is still largely undefined [80]. In agreement, KEGG enrichment analysis revealed that the common DEPs mainly affected metabolic and bioenergetic processes (i.e., GO Generation of precursor metabolites and energy, GO Monosaccharide biosynthetic process, GO Ribose phosphate metabolic process, KEGG Glycolysis and gluconeogenesis), exocytosis, cell adhesion processes (i.e., GO Cell adhesion molecule binding, GO Cadherin binding) and cellular response to oxidative stress (i.e., GO Cell redox homesostasis, GO regulation of response to oxidative stress) (Fig. 3B). Interestingly, when RNAseq and proteomic data were intersected, ALDOC, ENO2, and NOL3 emerged as significantly up regulated with a log2\|FC\|> 1 and p-value < 0.05 in all 3D vs 2D culture conditions both at gene and protein levels (Fig. 3C). Furthermore, we employed a novel bioinformatic protocol, able to jointly analyze transcriptomics and proteomics data, to gain additional insight on the relationship among the two omic profiles [45]. The analysis shows how mRNA/protein correlation levels span from 0.3 to 0.5 in different samples, with the H460_2D ranking at the top (Fig. S1, in additional file 6). These values are what expected in literature analysis, showing that only the subgroup of highly expressed genes show a strong correlation with protein levels [81]. Interestingly, a different overview of correlation levels in gene clusters show how clusters differentiate from small hyper-concordant groups (rho > $0.8\%$) to non-concordant outliers (rho < $0.2\%$) (Fig. 3D). It is safe to assume that the non-concordant outliers are more influenced in post-transcriptional modifications. Fig. 3Proteomic analysis of H460 and MCF7 cell lines grown in 2D and 3D culture conditions. A Heatmap of 3DEPs in 3D vs 2D conditions of both cell lines. Common DEPs in all 3D vs 2D conditions are labeled with (). Color intensity is proportional to the magnitude of changes. Relative expression levels are shown in red (upregulation) and blue (downregulation). B KEGG pathway analysis of DEPs in all 3D vs 2D conditions of both cell lines. The dot size denotes the number of DEPs, while colors correspond to the adjusted p-value range. C Dot plots showing ALDOC, ENO2, and NOL3 protein levels of H460 and MCF7 cell lines in 3D vs 2D conditions. D The distribution of gene-wise mRNA-protein correlations computed as Spearman’s Rho (x-axis). A histogram of 20 bins is shown with height of each bar proportional to the number of genes in each bin. The median correlation is depicted by a red vertical line Collectively, proteomic data confirmed that cancer cells reprogram their glucose metabolic to adapt, and thus to survive, to the altered oxygen homeostasis caused by cellular reorganization of within 3D tumor spheroids and that this is independent from both cell type and nutrient availability. ## Metabolic profiling of H460 and MCF7 tumor spheroids indicate a shift toward a more pronounced glycolitic phenotype regardless of the cell culture conditions Prompted by the information arising from RNAseq and proteomic analysis, we decided to investigate the metabolic shift associated with changes in nutrient availability in non-adherent conditions. To this aim, we performed targeted polar metabolomic profiling of H460 and MCF7 cells grown as 2D, as well as 3D_SM and 3D_FBSlow tumor spheroids. Collectively, the LC–MS platform enabled us to detect 80 metabolites (Table S6, in Additional file 7). A total of 66 metabolites were found significantly altered among the three cell culture conditions (2D, 3D_SM and 3D_FBSlow) with log2 \|FC\|> 1 and a p-value < 0.01. We observed that, as for the transcriptomic and proteomic profiles, the intracellular metabolomic profiles of H460 and MCF7 cells grown as 2D cultures were substantially different, as attested by the net clustering of samples shown in Fig. 4A. According to the literature, lung and breast cancer cells have different inherited metabo-phenotypes (metabotypes) and dependencies caused by the genetic background, the oncogenic evolution, and the interaction with the cellular niche [82]. H460 are primarily glycolytic cells [83]; MCF7, instead, are the most oxidative among the breast cancer cells, and overall display high flexibility in the substrate-driven ATP production [84]. In this regard, our data show that both H460 and MCF7 in 2D culture conditions consume glucose; however, the higher ratio isocitrate/citrate in MCF7 compared to H460 suggests a higher mitochondrial functionality in the breast cancer cell line than in the lung cancer cell line. In addition, as suggested by the higher amount of Ribose-5P, Xylulose-5P and Sedoheptulose-7P, MCF7 cells seem to promote anabolism through PPP for nucleotide synthesis, synthesis of serine and glycerol-3-P (Fig. 4A).Fig. 4Metabolomic analysis of H460 and MCF7 cell lines grown in 2D and 3D culture conditions. A Heatmap of 66 significantly altered metabolites in H460 and MCF7 cell lines in 2D vs 3D conditions. B Dot plots showing the 7 metabolites with the same trend of variation in all 3D vs 2D cultures of both cell lines. C KEGG pathway enrichment analysis of glycolysis/gluconeogenesis, showing in red ALDOC and ENO2 upregulation at both gene and protein levels. D-E Intracellular glucose and L-lactic acid amounts measured by luminometric assays and reported as relative light units (R.L.U.); quantification of L-lactic acid within the culture media (extracellular) performed by emogas analysis and expressed as mmol/l in H460 2D, H460 3D_SM, H460 3D_ FBSlow, MCF7 2D, MCF7 3D_SM, and MCF7 3D_ FBSlow. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: < 0.05, < 0.01, * < 0.001. ns: not significant The amount of intracellular polar metabolites significantly diverged along the transition from 2 to 3D models regardless of the cell type. Overall, we identified 66 altered metabolites; among these, 7 showed the same trend of variation in all 3D vs 2D cultures: D-Glucose monophosphate, D-Fructose monophosphate, D-hexose pool, UDP glucose, dIMP, L-Aspartic Acid and L-Serine were significantly down-regulated in 3D vs 2D while L-lactic acid was the only metabolite up-regulated in 3D H460 and MCF7 compared to their relative adherent cells (Fig. 4A-B). It is important to note that, in FBSlow culture condition, MCF7 produced a higher amount of L-lactic acid compared to H460 cells, thus further suggesting the occurrence of a significant shift toward a glycolytic phenotype in the breast cancer cell line compared to the LUAD cell line which instead appeared more glycolytic already in 2D conditions. The increased intracellular ratio L-lactic acid/Glucose monophosphate and D-Fructose monophosphate in all 3D tumor spheroids compared to their relative 2D cultures well agreed with the up-regulation of the glycolytic enzymes ALDOC and ENO2, at both gene and protein levels (Fig. 4C; Fig. S2 in additional file 8). To confirm the shift toward a more pronounced glycolytic phenotype suggested by the metabolomic analysis, we quantified both intracellular glucose and L-lactic acid in 2D and 3D conditions by using specific luminometric assays. Results reported in Fig. 4D-E show a significant intracellular accumulation of L-lactic acid in all 3D conditions (p-value < 0.05, p-value < 0.01, *p-value < 0.001) accompanied by a slight reduction in glucose (ns = not significant). In line with the increase of intracellular L-lactic acid levels, the analysis of L-lactic acid within the culture media (extracellular L-lactic acid) showed a significant release of this metabolite in H460 and MCF7 3D spheroids compared to their relative 2D counterparts (p-value < 0.05) (Fig. 4D-E). ## MCF7 show a greater ability to generate 3D tumor spheroids in nutrient-restricted culture conditions compared to H460 cells Gene and protein expression reorganization associated with 3D cell culture drive morphological and functional changes, such as proliferation rate and drug resistance [85]. Here, we observed that nutrient restriction had different effects on both tumor spheroids size and number depending on the cell type analyzed. Indeed, the FBSlow culture condition caused an increase of H460 tumor spheroids number compared to SM (3230 ± 221 (FBSlow)) vs (2450 ± 158 (SM)) (p-value < 0.05) without significantly affecting their diameter (163.9 ± 30.8 (SM) vs 158.51 ± 25.55 (FBSlow), ns). The number of tumor spheroids deriving from MCF7 cells was instead apparently unaffected by the different culture conditions (1050 ± 24 (FBSlow) vs 1223 ± 320 (SM), ns), but they appeared increased in size when grown in the FBSlow culture medium (156.99 ± 26.59 (SM) vs 180.02 ± 22.43 (FBSlow), p-value < 10–7) (Fig. 5A-B). Cell viability assay highlighted that while H460 cells suffered from nutrient-restricted culture medium (FBSlow) MCF7 cells, grown in the same culture condition, showed an enhanced cell viability (Fig. 5C). This difference can be attributed to the previously mentioned higher inherited metabolic plasticity of MCF7 cells, which therefore result more adaptable to nutrient restrictions and, overall, less dependent on glucose to produce ATP.Fig. 5Analysis of morphology and growth rate of H460- and MCF7-derived tumor spheroids. A Representative images and relative histograms of H460 3D_SM, H460 3D_ FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow tumor spheroids morphology, count and B diameter. C Cell viability of H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow assessed by Cell titer-Glo 3D assay and expressed as relative light unit (RLU). D Representative images of H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow tumor spheroids obtained by SEM. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: * < 0.05, < 0.01. * < 0.001. ns: not significant Finally, Scanning Electron Microscopy (SEM) analysis revealed that the plasma membrane ultrastructural features of 3D spheroids appeared morphologically distinguishable depending on culture conditions. Notably, both H460- and MCF7-derived spheroids cultured in SM showed intense plasma membrane blebbing, indicating high membrane dynamics with respect to FBSlow cultured counterpart. Since this activity can be related to microvesicles formation this aspect deserves further investigations. Moreover, H460-derived tumor spheroids grown in FBSlow appeared more compact, provided with a marked roundness, suggesting a different junctional behaviour of SM and FBSlow cultured samples (Fig. 5D). Collectively, these results suggest that both H460 and MCF7 cells survive to harsh nutrient culture conditions and generate tumor spheroids that appear more compact; besides, MCF7 appear favoured in terms of spheroids size and growth rate possibly because of their inherited metabolic plasticity. ## Suppression of ALDOC and ENO2 restrains 3D tumor spheroids growth of H460 and MCF7 cells To confirm the role of ALDOC and ENO2 in H460 and MCF7 tumor spheroids growth we performed the transient knock down of both enzymes. Data reported in Fig. 6A show that single knock down of ALDOC and ENO2 led to the evident reduction of each gene, which was more prominent when both genes were silenced together. Furthermore, we observed that ENO2 silencing did not affect ALDOC gene expression levels; conversely, ALDOC knock down significantly reduced ENO2 only in H460 cells regardless of the culture media conditions. Fig. 6ALDOC and ENO2 knock down reduces the sphere-forming ability of H460 and MCF7 cells. A qRT-PCR analyses of ALDOC and ENO2 in H460 3D_SM, H460 3D_ FBSlow, MCF7 3D_SM, and MCF7 3D_ FBSlow upon ALDOC and ENO2 silencing alone or in combination. B Cell viability of H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination assessed by Cell titer-Glo 3D assay and expressed as relative light unit (R.L.U). C Representative images and relative histograms of tumor spheroids morphology and diameter of H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: * < 0.05, < 0.01, * < 0.001. ns: not significant Next, we found that ALDOC and ENO2 knock down, either as a single entity or in combination, attenuated the spheroids forming ability in both cell lines regardless of the culture media conditions as shown by the markedly reduced cell viability (Fig. 6B) and tumor spheroids size (see images and relative histograms in Fig. 6C) (p-value < 0.05, p-value < 0.01, *p-value < 0.001). Based on these results, we wondered whether H460 and MCF7 growth in non-adherent conditions was dependent on ALDOC- and ENO2-mediated glucose metabolism. To this, we assessed the effects of transient knock down of ALDOC and ENO2 on intracellular glucose and L-lactic acid amounts as well as on L-lactic acid release within culture media. As shown in Fig. 7A-B, knock down of ALDOC and ENO2 alone caused, although without reaching the statistical significance, a reduction of both intracellular and extracellular L-lactic acid as well as a slight increase in glucose amounts in both cell lines and culture media. The only exception was represented by the intracellular glucose amounts which appeared significantly increased upon ALDOC or ENO2 silencing alone in H460 cells regardless of the cell culture conditions (p-value < 0.05, *p-value < 0.01). Silencing of both enzymes together, instead, led to the marked reduction of L-lactic acid production and release (p-value < 0.05) in both the cell lines (Fig. 7C). Collectively, the biological effects observed upon ALDOC and/or ENO2 transient knock down are reported in Fig. S3 (additional file 9).Fig. 7ALDOC and ENO2 knock down impairs glucose and L-lactic acid amounts in H460 and MCF7 3D tumor spheroids. A-B Intracellular glucose and L-lactic acid amounts measured by luminometric assays and reported as relative light units (R.L.U.) in H460 3D_SM, H460 3D_ FBSlow, MCF7 3D_SM and MCF7 3D_ FBSlow upon ALDOC and ENO2 silencing alone. C Quantification of L-lactic acid within the culture media (extracellular) performed by emogas analysis and expressed as mmol/l in H460 3D_SM, H460 3D_ FBSlow, MCF7 3D_SM and MCF7 3D_ FBSlow upon ALDOC and ENO2 silencing alone or in combination. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: * < 0.05, < 0.01, * < 0.001. ns: not significant It is important to note that data shown in Fig. 6 and 7 are the results of ALDOC and ENO2 silencing performed by using a specific siRNA for each gene. Two additional siRNAs (siALDOC #$\frac{2}{3}$; siENO2 #$\frac{2}{3}$) were also used to knock down each gene to exclude any off-targets effects as shown in Fig. S4 and S5 in additional files 10–11. Overall, these results indicate that the loss of either ALDOC or ENO2 significantly impairs the ability of both H460 and MCF7 to in terms of spheroids viability and size, regardless of the culture media conditions. This effect appears exacerbated when tumor spheroids are, in parallel, deprived of both enzymes. Notably, the transient knock down of each gene leads to the perturbation of the glycolytic flux, although without reaching the statistical significance in most of the 3D conditions. Among the two cell types, H460-derived tumor spheroids seem more affected by the single knock down of ALDOC, which alone causes the significant down regulation of ENO2 and the significant accumulation of intracellular glucose amount. We hypothesize that this could be explained, once again, by the already discussed primarily glycolytic phenotype of H460 cells [83]. The glycolytic flux results significantly perturbed in both cell types upon then loss of both enzymes, as demonstrated by the significant reduction of L-lactic acid production and release. These results were further confirmed in additional LUAD (HCC827) and breast cancer (T47D) cell lines. Indeed, as shown in Fig. 8, the combined knock down of the two glycolytic enzymes significantly impaired lactate production and hampered the growth of HCC827 and T47D cells as 3D tumor spheroid in non-adherent conditions. Fig. 8ALDOC and ENO2 knock down reduces the sphere-forming ability of HCC827 and T47D cells. A qRT-PCR analyses of ALDOC and ENO2 in HCC827 3D_SM, HCC827 3D_FBSlow, T47D 3D_SM, and T47D 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination. B L-lactic acid production assessed in HCC827 3D_SM, HCC827 3D_FBSlow, T47D 3D_SM, and T47D 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination. C Cell viability of HCC827 3D_SM, HCC827 3D_FBSlow, T47D 3D_SM, and T47D 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination assessed by Cell titer-Glo 3D assay and expressed as relative light unit (RLU). D Representative images and relative histograms of tumor spheroids morphology and diameter of HCC827 3D_SM, HCC827 3D_FBSlow, T47D 3D_SM, and T47D 3D_FBSlow upon ALDOC and ENO2 silencing alone or in combination. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: * < 0.05, < 0.01, * < 0.001. ns: not significant ## Discussion Cell adaptation, selection, and evolution are key processes along all the steps of tumor initiation and progression, including the propensity of cancer cells to leave the primary site, migrate and establish metastases [86]. To leave the primary tumor, cancer cells adopt drastic transcriptional and metabolic changes that jointly initiate the invasion-metastatic cascade and, thus, allow tumor cells to detach from the ECM, adopt an EMT phenotype and disseminate from primary lesions into the blood or the lymphatic vessels [87]. Then, circulating tumor cells adopt anti-anoikis, or anchorage-independent survival mechanisms, to further adapt to the severe environmental stress imposed by separation from the ECM [88–90]. According to the literature, oxygen, energy metabolism and redox homeostasis are three inextricably linked factors among which cancer cells need to strike a balance to survive under detached conditions [91]. Only a small subpopulation of persisting cancer cells leaving primary tumors are able to maintain an optimal balance between the competing interests arising from three factors and, thus, to successfully reach metastatic secondary sites. In addition to an “imprinted” predisposition and/or the acquisition of random mutational hits, persisting cancer cells show a remarkable plasticity against the metabolic requirements imposed by the different microenvironments during the different steps of the metastatic cascade. Such metabolic reprogramming can be controlled both through the transcriptional and post-transcriptionally regulation of specific enzymes or through metabolite availability [92]. In this regard, it has been demonstrated that lactate and pyruvate metabolism promote the switch from a proliferative to a migrating metastatic cell phenotype through the modulation of different signalling pathways and global gene expression programmes. In breast cancer cells, the reduction of the oxidative metabolism in favor of glycolytic energy production leads to the accumulation of acetyl-CoA and the consequent acetylation of the transcription factor Smad2, which is a well-known inducer of the mesenchymal genes patterns [93]. In agreement, both metastatic lung cancer cell lines and metastases isolated from lung cancer mouse models show downregulated gene expression of proteins belonging to the ETC [94]. In breast cancer cells, lactate dehydrogenase (LDH) has been found to be phosphorylated and thus activated by HER2 and SRC, and that the inhibition of such phosphorylation is associated with decreased invasiveness [95]. Once in blood circulation, lactate and pyruvate also contribute to the resistance to the hypoxia-mediated ROS accumulation within cell clusters through the stabilization of HIF1α protein [96]. Alternatively, glutamine metabolism is also involved in tumor invasion. To make an example, the overexpression of glutaminase 1 (GLS1), which catabolizes the conversion from glutamine to glutamate, is required for colorectal cancer cells migration and lymphnode metastasis [97]. In agreement, glutamate dehydrogenase (GDH), which converts glutamate to α-ketoglutarate, has been identified as a prognostic marker of colorectal cancer metastasis [98]. Alterations in lipid metabolism is also intimately linked to tumor progression. For instance, the increase in monounsaturated fatty acids, generated by the activity of SCDs, the rate-limiting enzymes in the formation of monounsaturated fatty acids, is associated with the acquisition of cancer stem cells (CSCs)-like features in ovarian and lung cancer cells lines [99–101]. In agreement, the increased activity of SCD1 has been found to promote YAP/TAZ signalling pathway thus enhancing melanoma CSCs aggressiveness [102]. Similarly, the activation of the mevalonate pathway, responsible for cholesterol synthesis, confers stem cell traits to breast cancer cells. In agreement, the inhibition of HMG-CoA reductase, the rate-limiting enzyme of the mevalonate cascade, resulted effective against breast cancer stem cells [103]. In this study, by using a well-integrated multi-omics approach, we demonstrate that the ability of H460 LUAD and MCF7 breast cancer cells to grow in non-adherent condition and to generate 3D tumor spheroids both in glucose-rich (3D_SM) and glucose-deprived (3D_FBSlow) culture media is associated with a consistent modulation of genes and proteins mainly involved in metabolic reprogramming towards an enhanced glycolytic phenotype most likely induced by a hypoxic condition. Indeed, we found that all the transitions from 2 to 3D cultures, regardless of the cancer cell type and cell culture conditions, are accompanied by the significant up-regulation of genes encoding for 6 out of 10 glycolytic enzymes: HK2 and ALDOC belonging to the energy-requiring phase and GAPDH, PGK1, ENO2, and PKM belonging to the energy-releasing phase [104]. In agreement with transcriptomic data, our metabolomic analyses highlighted a significant consumption of glucose and a corresponding increase in lactate production in all 3D tumor spheroids compared to their relative 2D parental cells, even within glucose-restricted culture conditions. If on the one hand, these results leant towards a mandatory role of the glycolytic cascade for the maintenance of cancer cell survival under detached-culture conditions, on the other hand raised the question of how 3D tumor spheroids enhanced their glycolytic phenotype in glucose-restricted conditions. Glucose metabolism is one of the major metabolic pathways essential for tumor growth [105]. According to the “Warburg effect” concept, tumor cells enhance glycolytic cascade and the LDH- mediated lactate production both under hypoxic and normoxic conditions [106]. Warburg effect allows tumor cells to gain survival advantages in two ways: one is to increase carbon sources, which are used to synthesize proteins, lipids, and nucleic acids to meet the needs of tumor growth; the other one is to turn off the aerobic respiration to suppress ROS generation, thereby preventing cell death [107]. In particular under hypoxia conditions, cancer cells tend to enhance lactate production by enhancing the expression of glycolytic enzymes and lactate dehydrogenase (LDH) [108]. In this regard, our results show that in association with the increase in lactate production all the 2D to 3D transitions are accompanied by the overexpression of LDH, although without reaching the statistical significance (Table S1). The overproduction of lactate represents a well-documented benefit for cancer cells through the acidification of the tumor microenvironment (TME), VEGF-mediated angiogenesis, increase of cancer cell motility and self-renewal of cancer stem cells (CSCs) [108]. As such, lactate is positively associated with tumor metastasis and recurrence [109, 110]. During hypoxic conditions, cancer cells also use other mechanisms to foster the conversion of pyruvate to lactate. Among these, one is the inhibition of pyruvate entry into the TCA cycle through the PGK1-mediated phosphorylation of pyruvate dehydrogenase kinase 1 (PDK1), which in turn inhibits the pyruvate dehydrogenase complex (PDC) [111]. Interestingly, PGK1 is one of the glycolytic genes up regulated in all 3D vs 2D conditions. Gene expression analysis of 3D glioblastoma spheroids has shown increased expression of pyruvate dehydrogenase kinase 4 (PDK4) involved in the suppression of mitochondrial activity. In agreement, MYC, involved in mitochondrial energy production, was found down-regulated and the evaluation of TCA cycle metabolic products showed decreases in the levels of succinate, fumarate, and malate [112]. Interestingly, the same changes have been reported in metabolomic analysis of ovarian cancer cell spheroids [113]. The induction of mitochondrial autophagy (mitophagy), in concert with inhibition of mitochondrial biogenesis, represents critical adaptive mechanism to maintain oxygen homeostasis and prevent mitochondrial ROS accumulation under hypoxic conditions [114, 115]. Importantly, our transcriptomic and proteomic data show a significant alteration of genes and proteins involved both in mitochondria biogenesis and clearance. All the transitions from 2 to 3D conditions, in fact, were characterized by i) the significant down regulation of MRPL41 and MRPL24 components of mitoribosomes involved in the synthesis of mitochondrial electrons transport chain (ETC) components [75], ii) the significant up-regulation of BINP3 and BNIP3L that are targets of HIF1 and are necessary for mitophagy [63]. BINP3/BINP3L are involved in mitochondrial quality control: in response to mitochondrial damage, they participate to the degradation of damaged proteins inside mitochondria and in the opening of the pores within the mitochondrial double membrane in order to mediate the translocation of the lysosomal proteins from the cytoplasm to the mitochondrial matrix. As last adaptive mechanism, HIF-1 reprograms tumor metabolism by increasing glycogen reserves under hypoxia [116]. According to the literature, a decrease in pO2 acts as an “alarm” that prepares cancer cells to face subsequent nutrient depletion through the induction of glycogen storage. In this regard, our findings demonstrate that the mRNA levels of the first enzyme of the glycogenesis PGM1 were increased in all 3D vs 2D conditions, regardless of the culture media. In agreement, metabolomic analysis shows a significant decrease in the intracellular level of uridine diphosphate glucose (UDP) which is the first substrate for glycogen synthesis. In addition to glucose, recent studies suggest that fructose can be preferentially metabolized by cancer cells under low oxygen conditions through an alternative catabolic pathway known as fructolysis [117]. During fructolysis fructose is first converted to fructose 1-phosphate by fructokinase and then converted to DHAP and G3P specifically by the aldolase isoforms ALDOB and ALDOC. In this regard, our results show that all 2D to 3D transitions were associated with the significant up-regulation of ALDOC isoenzyme at both gene and protein level concomitant with the significant reduction of the intracellular levels of fructose-monophosphate. Stemming from these observations, we could also hypothesize that, in non-adherent conditions, certain cancer cells, and above all those deprived of glucose, might become fructose-dependent. According to the literature, fructolysis show several advantages for cancer cells compared to glycolysis. First, fructose can be quickly catalyzed because fewer enzymes are involved in this process than in glycolysis. Fructolysis fuels glycolysis thus leading to a further increase in lactate production [118]. Indeed, since fructokinase activation sequesters a phosphate from ATP, the consequent ATP and phosphate depletion enhances glycolysis by activating the glycolytic enzymes PFK and PK [119]. Furthermore, G3P generated by ALDOB and ALDOC during fructolysis enters to the glycolytic pathway distal to PFK [53]. The rapid reduction of phosphate caused by the activation of fructokinase has been shown to activate the AMP deaminase (AMPD), which cleaves AMP to IMP [117]. The latter is used to generate acid uric, which in turn causes mitochondrial ROS accumulation [120]. In line with these data, our results show that 3D tumor spheroids were characterized by the up-regulation of AMPD3 isoform and a significant reduction of IMP intracellular levels. AMPD3 is involved in the activation of AMPK, which is largely recognized as an early energy sensor activated by glucose deprivation and responsible of the activation of alternative catabolic pathways to generate ATP [121]. Together with ALDOC, also ENO2 was found up-regulated Interestingly, both ALDOC and ENO2 are neuro-specific isoforms of the relative enzymes mainly expressed in normal neuronal tissues [53, 54]. As such, their overexpression in 3D tumor spheroids derived from LUAD and breast cancer cell lines was somehow unexpected; however, it could be suggestive of a broad neuronal-specific gene expression reprogramming of cancer cells during detachment from the ECM and 3D tumor growth. Although still poorly defined, the literature suggests that both isoenzymes exert non-canonical “moonlighting” functions in carcinogenesis [122, 123]. Under hypoxia, HIF1a binds to the hypoxia- responsive element (HRE) on the promoter region of ALDOC, thus causing metabolic reprogramming or aberration of glycolysis to promote glioblastoma and ovarian cancer [124]. In 2022 Maruyama R et al. demonstrated that ALDOC is overexpressed in 3D tumor spheroids derived from colorectal cancer (CRC) cell lines and that its overexpression in CRC patients correlated with metastasis and poor prognosis [125]. ENO2 can function as on oncogene, either in neuronal malignancies or in other cancer types, such as lung, breast, and prostate cancer [126–128]. Recent evidence was provided that the C-term domain of ENO2, which is not necessary for metabolic activity, activates the MAPK/ERK signaling pathway and thus promotes proliferation and migration of BRAV V600E-mutated CRC cells [129]. In this regard, in our study, we demonstrate that the combined knockdown of ALDOC and ENO2 significantly reduced lactate production and consequently attenuated the sphere-forming ability of both LUAD and breast cancer cell lines both in nutrient-rich and nutrient-restricted conditions. Finally, the integration of transcriptomic and proteomic data highlighted that NOL3 was up regulated in all 3D vs 2D conditions both at gene and protein levels. NOL3 functions as a suppressor of both intrinsic and extrinsic apoptosis through several mechanisms, including the blockage of death-inducing signaling complex (DISC) assembly, the limitation of caspase-8 for DISC-mediated activation, and the inactivation of pro-apoptotic BAX [130]. ## Conclusions Overall, the present work shows that the integration of transcriptomic, proteomic, and metabolomic analyses is a powerful approach to unveiling in-depth global adaptive cellular responses and the interconnection of regulatory circuits involved in the ability of cancer cells to survive in non-adherent conditions. Indeed, our findings reveal that an extensive metabolic rewiring towards an increased glycolytic “metabotype” and an enhanced lactate production is mandatory to achieve a new homeostasis state that favors cancer cell survival in 3D culture conditions. This phenomenon is accompanied by multiple adaptive events of both transcriptional and translational machineries that merge to a hypoxic-mediated upregulation of anaerobic glycolytic cascade, maintenance of intracellular redox homeostasis, activation of autophagic and antiapoptotic pathways. Noteworthy, in all the transitions from 2 to 3D cultures, ALDOC and ENO2 glycolytic enzymes are upregulated both at transcriptional and translational levels and interfering with their activity is sufficient to repress lactate production and to reduce sphere-forming ability of both LUAD and breast cancer cell lines. This result suggests that ALDOC and ENO2 may represent new powerful targets to restrain 3D tumor spheroids generation of both lung and breast cancer cell lines cultured in different environmental nutrient availability. ## Supplementary Information Additional file 1: Table S1. RNAseq data. Additional file 2: Table S2. GO_Biological_Process. Additional file 3: Table S3. GO_Molecular Function. Additional file 4: Table S4. GO_Cellular_Component. Additional file 5: Table S5. Proteomic data. Additional file 6: Figure S1. Correlations between mRNA expression and protein abundance. Sample-wise mRNA-protein correlation computed as Spearman’s Rho (y-axis). Samples are ordered along the x-axis based on increasing correlation. Additional file 7.Additional file 8: Figure S2. ALDOC and ENO2 levels increase in all H460 and MCF7 3D conditions compared to their 2D relative counterparts. A-B qRT-PCR and Western Blot analyses of ALDOC and ENO2 in H460 2D, H460 3D_SM, H460 3D_FBSlow, MCF7 2D, MCF7 3D_SM, and MCF7 3D_FBSlow. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: <0.05.Additional file 9: Figure S3. Schematic illustration of upstream and downstream events of ALDOC and/or ENO2 perturbation. Additional file 10: Fig. S4. ALDOC and ENO2 knock down with additional siRNAs confirms the reduction of sphere-forming ability of H460 and MCF7 cells. A qRT-PCR analyses of ALDOC and ENO2 in H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow upon ALDOC and ENO2 silencing with additional siRNAs. B Cell viability of H460 3D_SM, H460 3D_FBSlow, T47D 3D_SM, and T47D 3D_FBSlow upon ALDOC and ENO2 silencing with additional siRNAs assessed by Cell titer-Glo 3D assay and expressed as relative light unit (R.L.U.). D Representative images and relative histograms of tumor spheroids morphology and diameter of H460 3D_SM, H460 3D_FBSlow, MCF7 3D_SM, and MCF7 3D_FBSlow upon ALDOC and ENO2 silencing with additional siRNAs. All the experiments were carried out in triplicate and results are presented as mean ± SD. p-value: <0.05, <0.01, <0.001. ns: not significant. Additional file 11: Fig. S5. ALDOC and ENO2 knock down with additional siRNAs also impairs glucose and L-lactic acid amounts in H460 and MCF7 3D tumor spheroids. A-B Intracellular glucose and L-lactic acid amounts measured by luminometric assays and reported as relative light units (R.L.U.) in H460 3D_ SM, H460 3D_FBSlow, MCF7 3D_SM and MCF7 3D_FBSlow upon ALDOC and ENO2 silencing with additional siRNAs. C Quantification of L-lactic acid within the culture media (extracellular) performed by emogas analysis and expressed as mmol/1 in H460 3D_SM, H460 3D_ FBSlow, MCF7 3D_SM and MCF7 3D_ FBSlow upon ALDOC and ENO2 silencing with additional siRNAs. All the experiments were carried out in triplicate and results are presented as mean = SD. ns: not significant. ## References 1. Raimo D, di Raimo T, de Santis E, Coppola L, Rosario D’andrea M, Angelini F. Circulating tumor cells and the metastatic process: the complexity of malignancy. J Cancer Metastasis Treat* (2018.0) 4 54. DOI: 10.20517/2394-4722.2018.50 2. Guan X. 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--- title: Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke authors: - Lei Xu - Qin Xiong - Yang Du - Lu-wen Huang - Ming Yu journal: BMC Neurology year: 2023 pmcid: PMC10031995 doi: 10.1186/s12883-023-03158-x license: CC BY 4.0 --- # Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke ## Abstract ### Background Stroke is the second most common cause of morbidity and mortality. Even mild stroke survivors have an increased risk of cognitive impairment. Studies have been conducted on the relationship between glycated hemoglobin (HbA1c) and cognitive decline, but the findings have been inconsistent. Therefore, this study examined the link between HbA1c levels and cognitive impairment following acute mild ischemic stroke. ### Methods Data from 311 patients with acute mild ischemic stroke admitted to Suining Central Hospital, Sichuan Province, China, from January 1, 2015, to December 31, 2018, were evaluated. Fasting venous blood was taken to assess HbA1c levels on the day after admission. Cognitive function was assessed using the Chinese version of the Montreal Cognitive Assessment Scale (MoCA) 3–6 months after stroke onset. We used a generalized additive model and smooth curve fitting (penalty spline method) to assess the nonlinear relationship between HbA1c and poststroke cognitive impairment (PSCI). ### Results This study included 311 patients aged 23 to 96 years old (mean age: 67.37 ± 11.92 years), of whom 198 ($63.67\%$) were men. Among the 311 stroke patients, 120 ($38.59\%$) had PSCI. After adjusting for potential confounders, there was a nonlinear relationship between HbA1c and PSCI, with an inflection point of 8.2. To the left of the inflection point, the effect size, $95\%$ confidence interval, and P value were 0.87, 0.58 to 1.31, and 0.5095, respectively; however, to the right of the inflection point, these numbers were 1.96, 1.08 to 3.58, and 0.0280. ### Conclusion We found a nonlinear relationship between HbA1c and PSCI. When HbA1c was greater than $8.2\%$, HbA1c was positively correlated with PSCI. ## Background Globally, stroke is the second most common cause of morbidity and mortality [1]. Cognitive impairment is a common complication after stroke that has a poor prognosis and places a heavy burden on families and society [2]. Mild stroke with mild clinical symptoms has a good prognosis and no obvious neurological signs. However, research shows an increased risk of developing cognitive impairment even in mild stroke survivors [3]. Therefore, early identification of important risk factors for cognitive impairment in mild stroke will enable clinicians to intervene earlier in high-risk patients. Glycated hemoglobin (HbA1c) is used as a marker of blood glucose control since it indicates the average level of blood glucose over the previous three months [4]. It is essential for glucose control in patients with diabetes [5] and provides higher test-to-test consistency than individual fasting or postload blood glucose readings [6]. HbA1c and cognitive impairment have been previously studied, but the results have been inconsistent. Several studies have found HbA1c to be a risk factor for cognitive impairment in people with diabetes [7–9]. However, in patients with acute ischemic stroke, the relationship between HbA1c and cognitive impairment has not been studied in depth, and no correlation between the two has been found [10–13]. Therefore, this study examined the link between HbA1c levels and cognitive impairment following acute mild ischemic stroke and identified prospective biomarkers for poststroke cognitive impairment (PSCI) identification and prevention. ## Subjects In this retrospective cohort study, data from 736 patients with acute ischemic stroke admitted to Suining Central Hospital, Sichuan Province, China, from January 1, 2015, to December 31, 2018, were recruited. The following were the criteria for inclusion: 1) patients were 18 years of age and older; 2) patients were hospitalized within 7 days of stroke start; 3) patients had a National Institutes of Health Stroke Scale (NIHSS) score of less than or equal to 3; and 4) patients had acute cerebral infarction confirmed by magnetic resonance imaging (MRI) during hospitalization. The following were the criteria for exclusion: 1) previous history of stroke; 2) aphasia that made it impossible to assess cognitive function; 3) history of mental problems, neurological diseases, thyroid diseases, autoimmune diseases, or tumors; and 4) prestroke dementia or cognitive impairment. A total of 311 patients with acute mild ischemic stroke were eventually included in the final analysis (as shown in Fig. 1). The ethics committees at Suining Central Hospital approved this study in accordance with the Helsinki Declaration. Fig. 1An overview of the selection process ## Covariates On admission, age, sex, body mass index (BMI), education, smoking status, alcohol use, hypertension, diabetes, atrial fibrillation, and other information were collected. Fasting venous blood was taken on the day after admission to assess fasting plasma glucose (FPG), HbA1c, blood lipid, and uric acid (UA) levels. Within 72 h after hospitalization, MRI was performed. Within 24 h of admission, the NIHSS was used to determine the severity of the stroke, with a score of less than or equal to 3 indicating mild ischemic stroke. At discharge, the modified Rankin scale (mRS) was used to assess functional outcomes. ## Cognitive assessments Cognitive function was assessed using the Chinese version of the Montreal Cognitive Assessment Scale (MoCA) 3–6 months after stroke onset [14]. The highest possible MoCA score was 30, and less than 26 was defined as cognitive impairment. A score of less than 25 was classified as cognitive impairment if the number of years of education was less than or equal to 12 years [15, 16]. ## Statistical analysis Data are reported as the mean ± standard deviation (SD) (Gaussian distribution) or median (range) (skewed distribution) for continuous variables and as numbers and percentages for categorical variables. χ2 (categorical variables), one-way ANOVA (normal distribution), or Kruskal–Wallis H test (skewed distribution) were used to detect differences in variables among different HbA1c groups (based on tertiles). To examine the effect of HbA1c on PSCI, we constructed three different models, namely, Model 1 (no covariates were adjusted for), Model 2 (only sociodemographic variables were adjusted for) and Model 3 (covariates are presented in Table 3). A $95\%$ confidence interval was calculated for the effect sizes. We used smooth curve fitting (penalized spline method) to account for nonlinearity between HbA1c and PSCI as well as the generalized additive model (GAM). In addition, a two-piecewise binary logistic regression model was used to further explain the nonlinearity. Next, we performed a subgroup analysis and used the likelihood ratio test to examine subgroup interactions. Modeling was performed with the statistical software packages R (http://www.R-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). P values less than 0.05 (two-sided) were considered statistically significant. ## Baseline characteristics of the participants This study comprised 311 patients aged 23 to 96 years old (mean age: 67.37 ± 11.92 years), including 198 ($63.67\%$) men. The HbA1c ranges for tertiles 1–3 (T1-3) were 4.1–5.4, 5.5–6.3, and 6.4–$14.7\%$, respectively. Significant differences in FPG, homocysteine (HCY), high-sensitivity C-reactive protein (hs-CRP), alcohol consumption, and diabetes mellitus were observed among the groups with different HbA1c levels (Table 1).Table 1Baseline characteristics of participantsHbA1c tertile, %Low (4.10–5.40)Middle (5.50–6.30)High (6.40–14.70)P-valueNo. of subjects88119104Age, mean (SD), year66.12 (13.66)67.04 (11.67)68.80 (10.50)0.281BMI, mean (SD), kg/m222.75 (3.15)23.15 (2.55)22.99 (2.81)0.595HDL, mean (SD), mmol/L1.45 (0.44)1.42 (0.35)1.43 (0.44)0.837LDL, mean (SD), mmol/L2.53 (0.83)2.61 (0.83)2.55 (1.24)0.810TG, median (min–max), mmol/L1.55 (0.03–6.22)1.58 (0.04–7.61)1.94 (0.03–6.99)0.349TC, mean (SD), µumol/L4.56 (1.54)4.27 (1.35)4.45 (1.35)0.302FPG, mean (SD), mmol/L5.32 (1.29)5.27 (0.99)8.45 (3.75)< 0.001HCY, mean (SD), µumol/L13.53 (6.19)16.11 (10.10)13.25 (4.09)0.007Cr, mean (SD), µumol/L77.50 (22.90)77.71 (22.02)79.33 (41.22)0.894BUN, mean (SD), mmol/L6.08 (2.22)6.26 (2.35)6.67 (2.81)0.240UA, mean (SD), µumol/L331.51 (118.72)334.51 (113.32)319.11 (99.52)0.558hs-CRP, median (min–max), mg/L3.33 (0.21–704.00)1.20 (0.15–84.24)5.80 (0.04–85.69) < 0.001MOCA, mean (SD)24.41 (2.17)24.50 (2.21)24.21 (2.35)0.618Sex, n (%)0.741 Male56 ($63.64\%$)73 ($61.34\%$)69 ($66.35\%$) Female32 ($36.36\%$)46 ($38.66\%$)35 ($33.65\%$)Cognitive impairment, n (%)0.780 No53 ($60.23\%$)76 ($63.87\%$)62 ($59.62\%$) Yes35 ($39.77\%$)43 ($36.13\%$)42 ($40.38\%$)Education, n (%)0.843 Undergraduate, college or above3 ($3.41\%$)2 ($1.68\%$)5 ($4.81\%$) High school (including technical secondary school)4 ($4.55\%$)9 ($7.56\%$)6 ($5.77\%$) Junior high school17 ($19.32\%$)24 ($20.17\%$)23 ($22.12\%$) Primary school36 ($40.91\%$)51 ($42.86\%$)46 ($44.23\%$) Illiteracy28 ($31.82\%$)33 ($27.73\%$)24 ($23.08\%$)Smoking status, n (%)0.339 Never-smoker55 ($62.50\%$)86 ($72.27\%$)77 ($74.04\%$) Past smoker who has quit14 ($15.91\%$)16 ($13.45\%$)15 ($14.42\%$) Current smoker19 ($21.59\%$)17 ($14.29\%$)12 ($11.54\%$)Alcohol consumption, n (%)0.014 Yes30 ($34.09\%$)20 ($16.81\%$)23 ($22.12\%$) No58 ($65.91\%$)99 ($83.19\%$)81 ($77.88\%$)Hypertension, n (%)0.388 Yes55 ($62.50\%$)65 ($54.62\%$)65 ($62.50\%$) No33 ($37.50\%$)54 ($45.38\%$)39 ($37.50\%$)Diabetes mellitus, n (%) < 0.001 Yes2 ($2.27\%$)7 ($5.88\%$)49 ($47.12\%$) No86 ($97.73\%$)112 ($94.12\%$)55 ($52.88\%$)Hyperlipidemia, n (%)0.633 Yes2 ($2.27\%$)4 ($3.36\%$)5 ($4.81\%$) No86 ($97.73\%$)115 ($96.64\%$)99 ($95.19\%$)Atrial fibrillation, n (%)0.674 Yes2 ($2.27\%$)3 ($2.52\%$)1 ($0.96\%$) No86 ($97.73\%$)116 ($97.48\%$)103 ($99.04\%$)mRS score, n (%)0.080 17 ($7.95\%$)22 ($18.49\%$)9 ($8.65\%$) 269 ($78.41\%$)81 ($68.07\%$)82 ($78.85\%$) 310 ($11.36\%$)15 ($12.61\%$)9 ($8.65\%$) 40 ($0.00\%$)1 ($0.84\%$)0 ($0.00\%$) 52 ($2.27\%$)0 ($0.00\%$)4 ($3.85\%$)NIHSS score, n (%)0.514 013 ($14.77\%$)19 ($15.97\%$)8 ($7.69\%$) 121 ($23.86\%$)33 ($27.73\%$)33 ($31.73\%$) 229 ($32.95\%$)37 ($31.09\%$)38 ($36.54\%$) 325 ($28.41\%$)30 ($25.21\%$)25 ($24.04\%$)Abbreviations: BMI body mass index, HDL high-density lipoprotein, LDL low-density lipoprotein, TG triglycerides, TC total cholesterol, FPG fasting plasma glucose, HbA1c glycosylated hemoglobin, HCY homocysteine, Cr creatinine, BUN blood urea nitrogen, UA uric acid, hs-CRP high-sensitivity C-reactive protein, MOCA Montreal Cognitive Assessment, mRS modified Rankin Scale, NIHSS National Institutes of Health Stroke Scale ## Characteristics of the PSCI and non-PSCI groups Of the 311 recruited stroke patients, 120 ($38.59\%$) of them had PSCI, and 191 had normal cognition. There were significant differences in creatinine (Cr), blood urea nitrogen (BUN), UA, hypertension and atrial fibrillation between the two groups (P ≤ 0.05) (Table 2).Table 2Characteristics of the PSCI and non-PSCI groupsCharacteristicNon-PSCIPSCIP-valueNo. of subjects191120Age, mean (SD), year67.13 (11.45)67.75 (12.66)0.656BMI, mean (SD), kg/m223.08 (2.76)22.84 (2.89) 22.200.455HDL, mean (SD), mmol/L1.45 (0.43)1.41 (0.37)0.497LDL, mean (SD), mmol/L2.58 (1.02)2.55 (0.92)0.772TG, median (min–max), mmol/L1.80 (0.03–7.61)1.50 (0.03–6.63)0.239TC, mean (SD), µmol/L4.43 (1.41)4.39 (1.41)0.818FPG, mean (SD), mmol/L6.19 (2.73)6.59 (2.86)0.221HBAC6.23 (1.51)6.56 (2.00)0.107HCY, mean (SD), µmol/L14.47 (5.89)14.35 (9.64)0.888Cr, mean (SD), µmol/L71.00 (20.41)82.71 (33.90) < 0.001BUN, mean (SD), mmol/L5.93 (2.21)6.61 (2.61)0.019UA, mean (SD), µmol/L253.82 (68.97)375.44 (105.63) < 0.001hs-CRP, median (min–max), mg/L4.00 (0.04–704.00)3.10 (0.15–84.24)0.872MOCA, mean (SD)23.05 (1.81)26.49 (0.72) < 0.001The time from stroke onset to MoCA assessment, mean (SD), month4.44 (0.99)4.55 (1.03)0.346Sex, n (%)0.923 Male122 ($63.87\%$)76 ($63.33\%$) Female69 ($36.13\%$)44 ($36.67\%$)Education, n (%)0.424 Undergraduate, college or above7 ($3.66\%$)3 ($2.50\%$) High school (including technical secondary school)9 ($4.71\%$)10 ($8.33\%$) Junior high school35 ($18.32\%$)29 ($24.17\%$) Primary school86 ($45.03\%$)47 ($39.17\%$) Illiteracy54 ($28.27\%$)31 ($25.83\%$)Smoking status, n (%)0.525 Never-smoker131 ($68.59\%$)87 ($72.50\%$) Past smoker who has quit27 ($14.14\%$)18 ($15.00\%$) Current smoker33 ($17.28\%$)15 ($12.50\%$)Alcohol consumption, n (%)0.551 Yes47 ($24.61\%$)26 ($21.67\%$) No144 ($75.39\%$)94 ($78.33\%$)Hypertension, n (%)0.047 Yes122 ($63.87\%$)63 ($52.50\%$) No69 ($36.13\%$)57 ($47.50\%$)Diabetes mellitus, n (%)0.628 Yes34 ($17.80\%$)24 ($20.00\%$) No157 ($82.20\%$)96 ($80.00\%$)Hyperlipidemia, n (%)0.433 Yes8 ($4.19\%$)3 ($2.50\%$) No183 ($95.81\%$)117 ($97.50\%$)Atrial fibrillation, n (%)0.023 Yes1 ($0.52\%$)5 ($4.17\%$) No190 ($99.48\%$)115 ($95.83\%$)mRS score, n (%)0.815 123 ($12.04\%$)15 ($12.50\%$) 2141 ($73.82\%$)91 ($75.83\%$) 323 ($12.04\%$)11 ($9.17\%$) 41 ($0.52\%$)0 ($0.00\%$) 53 ($1.57\%$)3 ($2.50\%$)NIHSS score, n (%)0.218 025 ($13.09\%$)15 ($12.50\%$) 153 ($27.75\%$)34 ($28.33\%$) 257 ($29.84\%$)47 ($39.17\%$) 356 ($29.32\%$)24 ($20.00\%$) ## Relationships between HbA1c and PSCI To examine the links between HbA1c and PSCI, we utilized a binary logistic regression analysis. Table 3 shows the nonadjusted and adjusted models. In Model 1, HbA1c showed no correlation with PSCI (OR = 0.96, $95\%$ confidence interval (CI): 0.78 to 1.17, $$P \leq 0.6568$$). In Model 2 (adjusted for age and sex), the result were not different (OR = 0.94, $95\%$ CI: 0.77 to 1.16, $$P \leq 0.5811$$). We also found no connection in Model 3, a fully adjusted model after correcting for other factors (OR = 1.0, $95\%$ CI: 1.00 to Inf, $$P \leq 1.0000$$). We also used HbA1c as a categorical variable (tertiles) for sensitivity analysis and found the same pattern ($$P \leq 1.0000$$).Table 3Relationships between HbA1c and PSCIExposureModel 1OR ($95\%$CI) P-valueModel 2OR ($95\%$CI) P-valueModel 3OR ($95\%$CI) P-valueHbA1c0.96 (0.78, 1.17) 0.65680.94 (0.77, 1.16) 0.58111.00 (0.00, Inf) 1.0000HbA1c tertile LowRefRefRef Middle0.98 (0.44, 2.20) 0.96830.97 (0.43, 2.18) 0.94541.00 (0.00, Inf) 1.0000 High0.90 (0.39, 2.10) 0.81570.87 (0.37, 2.02) 0.74021.00 (0.00, Inf) 1.0000P for trend0.95 (0.63, 1.44) 0.81260.93 (0.61, 1.42) 0.73591.00 (0.00, Inf) 1.0000Model 1: Non-adjusted modelModel 2: Adjusted for Age and SexModel 3: Adjusted for Age, Sex, BMI, Education level, Smoking Status, Drinking Status, Hypertension, Diabetes mellitus, Atrial fibrillation, HDL, LDL, TG, TC, FPG, UA, Hyperlipidemia, HCY, Cr, BUN, hs-CRP, mRS, and NIHSS ## Nonlinear connection studies We investigated the nonlinear relationship between HbA1c and PSCI in this study since HbA1c is a continuous variable (Fig. 2). The relationship between HbA1c and PSCI was discovered to be nonlinear after adjusting for potential confounders. By using a two-piecewise linear regression model, we found the inflection point at 8.2. To the left of the inflection point, the effect size, $95\%$ CI, and P value were 0.87, 0.58 to 1.31, and 0.5095, respectively. However, to the right side of the inflection point, these numbers were 1.96, 1.08 to 3.58, and 0.0280), and we found a positive connection between HbA1c and PSCI (Table 4).Fig. 2The nonlinear connection studies. The same adjustments were made as in model 3Table 4Threshold effect analysis of HbA1c on PSCIOutcomeOR, $95\%$CI, P-valueModel 1 One line slope1.17 (0.88, 1.55) 0.2722Model 2 Infection point8.2 ≤ 8.20.87 (0.58, 1.31) 0.5095 > 8.21.96 (1.08, 3.58) 0.0280P for log likelihood ratio test0.044Adjustments are the same as those in model 3 ## The results of the subgroup analysis By subgroup analysis, we further explored additional risks between HbA1c and PSCI to assess other factors that might influence the results. Age, gender, BMI, education, smoking status, alcohol consumption, hypertension, diabetes mellitus, hyperlipidemia, mRS score and NIHSS score were selected as stratification factors (Table 5). According to the results, the relationship between HbA1c and PSCI was not modified by any of the above influencing factors. Table 5Relationships between HbA1c and PSCI in various subgroupsSubgroupNo. of subjectsOdds ratio ($95\%$ CI)P-valueP for interactionAge, year0.9746 23—631031.19 (0.80, 1.78)0.3945 64—731001.14 (0.75, 1.72)0.5362 74—961081.13 (0.77, 1.66)0.5264Gender0.3913 Male1981.25 (0.88, 1.78)0.2117 Female1131.05 (0.74, 1.49)0.7701BMI, kg/m20.6970 15.63—21.611031.04 (0.71, 1.53)0.8396 21.64—23.831001.27 (0.85, 1.90)0.2487 23.88—33.331081.21 (0.78, 1.89)0.3905Education0.2517 Undergraduate, college or above100.05 (0.00, 4.26)0.1910 High school (including technical secondary school)191.10 (0.30, 4.10)0.8861 Junior high school641.03 (0.68, 1.56)0.8915 Primary school1331.10 (0.88, 1.38)0.7321 Illiteracy851.64 (0.99, 2.71)0.0555Smoking status,0.1512 Never-smoker2181.08 (0.93, 1.24)0.5473 Past smoker who has quit451.86 (0.95, 3.66)0.0710 Current smoker480.78 (0.36, 1.67)0.5222Alcohol consumption0.6461 Yes731.29 (0.72, 2.33)0.3974 No2381.13 (0.84, 1.52)0.4316Hypertension0.4415 Yes1851.24 (0.88, 1.75)0.2250 No1261.06 (0.75, 1.50)0.7476Diabetes mellitus0.2544 Yes581.34 (0.87, 2.08)0.1846 No2531.00 (0.71, 1.42)0.9931Hyperlipidemia0.6520 Yes111.34 (0.60, 2.97)0.4770 No3001.11 (0.83, 1.48)0.4737mRS score0.1269 0–22701.17 (0.88, 1.58)0.2839 3–4410.65 (0.32, 1.35)0.2504NIHSS score0.7445 0400.76 (0.28, 2.05)0.5844 1871.24 (0.79, 1.96)0.3477 21041.20 (0.82, 1.75)0.3424 3801.09 (0.74, 1.61)0.6744Above model adjusted for Age, Sex, BMI, Education level, Smoking Status, Drinking Status, Hypertension, Diabetes mellitus, Atrial fibrillation, HDL, LDL, TG, TC, FPG, UA, Hyperlipidemia, HCY, Cr, BUN, hs-CRP, mRS, and NIHSS. In each case, the model is not adjusted for the stratification variable ## Discussion The connection between HbA1c and PSCI among participants was investigated using generalized linear model (GLM) and GAM models in this study. HbA1c was not linked with PSCI in the fully corrected model, as demonstrated. The same pattern was observed when HbA1c was treated as a categorical variable. However, a nonlinear relationship between HbA1c and PSIC was found, with different correlations on the left and right sides of the inflection point (HbA1c = $8.2\%$). On the left-hand side of the inflection point, HbA1c showed no significant relationship, but on the right-hand side of the inflection point, HbA1c was positively related to PSIC. Gong et al. found that 122 ($53.5\%$) of 228 patients with mild stroke who were assessed for cognitive impairment by MoCA 6–12 months after onset (MoCA < 22 was defined as cognitive impairment) developed cognitive impairment [17]. In a Korean study of 301 patients with acute ischemic stroke, 65 patients ($21.6\%$) developed PSCI when cognitive impairment was assessed by the K-VCIHS-NP 3 months after onset [11]. In our study, $38.59\%$ of patients with acute mild ischemic stroke were diagnosed with cognitive impairment 3–6 months after onset, which is different from previous studies. The reason is mainly related to the different evaluation criteria regarding cognitive function. HbA1c and PSCI have been correlated in previous studies, but the results have been inconsistent. Two previous studies on cognitive impairment in acute ischemic stroke patients did not find a correlation between HbA1c and PSCI [12, 13]. Two other studies of cognitive impairment 3 months after stroke also found no association between HbA1c and cognitive impairment after stroke [10, 11]. However, a cohort study found that HbA1c was an independent risk factor for cognitive impairment 6–12 months after acute mild ischemic stroke by multivariate logistic regression analysis [17]. The present study is the first to identify a curvilinear relationship between HbA1c and cognitive impairment at 3–6 months after mild ischemic stroke. Previous studies that did not find differences in HbA1c between the PSCI and non-PSCI groups did not conduct analyses of nonlinear relationships [10–13]. We employed the GAM to elucidate the nonlinear interactions between HbA1c and PSCI, as well as the generalized linear model to analyze their linear relationship. We found that for every $1\%$ increase in HbA1c greater than $8.2\%$, there was a 0.96-fold increase in the risk of PSCI. The clinical significance of this discovery is that the link between HbA1c and PSCI can only be seen when HbA1c reaches a particular level. The Mexican Health and Aging Study found that HbA1c ≥ $8\%$ was associated with poorer cognitive performance in older adults with diabetes [18]. Another study found that diabetes was associated with cognitive impairment only when it was poorly controlled (e.g., HbA1c ≥ $7.5\%$), suggesting that it was the degree of hyperglycemia, rather than diabetes itself, that had a negative impact on cognitive health [19]. The above two studies involved diabetic patients. After adjusting for various confounding factors, our study found that HbA1c > $8.2\%$ was an independent risk factor for PSCI. Our study found that the cut-off point was 8.2, which seems to be much higher than what we expect through basic knowledge. The reason may be related to the small sample size of people with elevated HbA1c. HbA1c is a commonly assessed parameter that reflects the average blood glucose concentration over the past 8–12 weeks and is a good indicator for evaluating long-term blood glucose control [20]. Elevated HbA1c is caused by increased glycosylation of proteins due to hyperglycemia [21]. Hyperglycemia and dementia have been linked in many studies, both animal and clinical studies, which show that short-term hyperglycemia can lead to learning and memory loss in experimental animals [22–24]. In addition, previous epidemiological studies have reported that hyperglycemia and diabetes status are independently associated with the incidence of dementia [25]. Hyperglycemia is associated with poorer cognitive performance and is caused by dysregulation of insulin and the expression of insulin-degrading enzymes [26, 27]. Intracerebral insulin originates from pancreatic beta cells and relies on efficient IRec-mediated insulin transport across the blood–brain barrier (BBB) to play an important role in cognition, including promoting learning and memory in older adults [28, 29]. This was the first study to identify a curvilinear relationship between HbA1c and cognitive impairment at 3–6 months after mild ischemic stroke. its effect on cognition from our study suggests the effect of average blood glucose may as a vascular risk factor on PSCI, rather than the effect of blood glucose in acute phase on PSCI. Thus, the implication of our study is the influence of pre-stroke blood glucose control on PCSI. There were certain limitations to our research. First, as this study is a retrospective study, selection bias and lack of data are inevitable. Second, the HbA1c-PSCI relationship cannot be generalized to all ischemic stroke populations due to the exclusion of those with moderate to severe stroke severity. Third, the inclusion of people in western China creates regional and ethnic boundaries. 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--- title: Occupational life-style programme over 12 months and changes of metabolic risk profile, vascular function, and physical fitness in blue-collar workers authors: - Nina Schaller - Katharina Blume - Markus Hornig - Ludger Senker - Bernd Wolfarth - Tibor Schuster - Martin Halle - Katrin Esefeld journal: Journal of Occupational Medicine and Toxicology (London, England) year: 2023 pmcid: PMC10031996 doi: 10.1186/s12995-023-00370-w license: CC BY 4.0 --- # Occupational life-style programme over 12 months and changes of metabolic risk profile, vascular function, and physical fitness in blue-collar workers ## Abstract ### Purpose Occupational health programmes have been successfully implemented to improve body composition, physical fitness and cardiovascular risk. However, most programmes have been small and have not included long-term evaluation. Therefore, we evaluated a twelve-month life-style change programme in a German refinery. ### Methods We offered a supervised six-week endurance exercise programme (2 × 90 min/week), starting after a two-day life-style seminar. After the active intervention and a half-day refresher seminar, employees were encouraged to continue exercising over one year on their own, with monthly supervised sessions to maintain adherence. Anthropometry, bicycle ergometry, cardio-metabolic risk profile, inflammatory parameters, and vascular function e.g. endothelial function was studied at baseline, after three and after twelve months. ### Results Of 550 employees, $$n = 327$$ (age 40.8 ± 9.7 years, $88\%$ males) participated in the study. Twelve-month intervention was associated with a reduced waist circumference (92.6 ± 12.2 to 90.8 ± 11.7 cm, $95\%$ confidence interval for the mean change (CI): -2.5 to -1.1 cm) and a gain in maximal exercise capacity (202 ± 39.6 to 210 ± 38.9 Watt; $95\%$ CI: + 5.1 to + 10.9 Watt). Metabolic and inflammatory parameters likewise HbA1c and C-reactive protein improved in central tendency at a local $95\%$ level of confidence. Vascular function e.g. Reactive-Hyperaemia-Index revealed a slight reduction, whereas no statistically robust changes in mean Cardio-Ankle-Vascular-Index and mean Ankle-Brachial-Index were observed. ### Conclusion Health education added by a six-week supervised exercise programme was associated with minor long-term twelve-month improvements of body composition as well as physical fitness and a concomitant improvement of inflammatory state. These changes were, however, not clinically relevant and not accompanied by statistically robust improvements of vascular function. ### Trial registration ClinTrialsGov: NCT01919632; date of registration: August 9, 2013; retrospectively registered. ## Introduction Demographic changes of an increasingly older age of employees at workplace in combination with a deteriorating life-style behaviour including low physical activity and increasing prevalence of obesity and metabolic syndrome has raised serious concerns among companies regarding the maintenance of a healthy work force [1]. Therefore, increased attention has been given to effective occupational preventive health programmes [2–6]. The primary goal of these programmes is the improvement of overall health status of employees particularly across large companies, improvement of the social attitude and lowering costly rates of leave days, thereby maintaining established work flows and increasing productivity [7]. Moreover, the occupational setting is ideal in the sense that interventions can be offered by the company during or adjacent to working time [7]. Additionally, reimbursement may also be financially covered by the company itself or by health insurances, which have special overarching contracts with these companies. On the one hand, this preventive concept of workplace health promotion (e.g. behaviour counselling, improving exercise/diet/non-smoking) has shown to be effective and data from larger studies have revealed beneficial effects on obesity/body weight, physical activity behaviour, physical fitness and cardiovascular risk factors [8, 9]. Likewise, Korshøj et al. [ 2016] revealed that lipid levels and inflammatory markers of blue-collar workers improved by a worksite aerobic exercise intervention (2 × 30 min /week) over 4 months. Reviews have shown strong evidence on body fat [10] and weight-related outcomes, especially for interventions with physical activity and/or nutrition [2], as well as in people with elevated risk for cardiovascular disease [10]. Furthermore, worksite interventions can also be effective in preventing mental and musculoskeletal disorders [2, 10, 11]. However, on the other hand, scientific evidence on long-term effects are limited or analyses have focused on standard body composition and cardiovascular parameters only [7]. Moreover, occupational intervention programmes addressing exercise only have been less applied in blue-collar workers, especially in those with a low socioeconomic status, which would benefit most from targeted interventions [7]. Therefore, we have conducted a one-year primary prevention exercise programme (“Moving”) for employees of an oil refining company mostly employing blue-collar workers. The objective was to examine the impact of an on-site programme on metabolic risk profile including inflammatory parameters as well as e.g. free fatty acid profile, exercise capacity assessed by maximal ergometry and on vascular function, i.e. endothelial function measured by the Reactive-Hyperaemia-Index (RHI) as primary endpoint. The hypothesis was that one year of participation in endurance exercise (twice a week for 90 min) may positively influence endothelial health. ## Study design and population The study has been designed as a prospective, one-arm, mono-centre and non-controlled intervention study. The group-based intervention consisted of an initial health-behaviour seminar followed by a supervised 6 weeks period and thereafter unsupervised exercise sessions over a total period of one year. During the whole programme, body weight, physical fitness as well as cardiovascular risk factors were monitored. The three medical appointments at baseline, at 3 and 12 months also included an assessment of endothelial function as a key parameter for vascular function [12]. The study was approved by the local ethics committee of the University Hospital “rechts der Isar”, Technical University of Munich, Germany ($\frac{2555}{09}$) in accordance with the Declaration of Helsinki. The study has been registered retrospectively and the trial registration number is NCT01919632. No changes have been made since commencement of the trial. From 2009 to 2012, potential study participants were recruited by an oil refining company (BP Europa SE), from its site in Lingen (Ems), Germany, with a total of approximately 550 employees, mostly of them blue-collar workers. After announcement and advertisement by the company´s internal media, occupational medical department as well as CEO and executive board of BP Europa SE, Lingen Refinery, employees voluntarily signed up documenting their interest in participating in the study and the life-style programme. Inclusion criteria were: healthy men and women of all age, eligibility documented by the occupational medical department to participate in physical activity, written informed consent of study participation. Exclusion criteria included acute or chronic disease of any kind, which does not allow participation in physical activity, language or cognitive barriers that do not allow a communication about the study design and concept. ## Worksite health programme and life-style intervention The initial stage of the worksite programme consists of a two-day health-behaviour seminar (©Moving - Gesundheitsmanagement GmbH, Berlin, Germany) in groups up to the size of 25 participants. In brief, on day one content included group-based life-style advice focusing on health prevention strategies, role of physical (in-)activity and cardiovascular risk. Day two covered topics like healthy nutrition, behaviour change management, self-motivation strategies, and resistance exercise training. After this prelude seminar, the groups started into a supervised outdoor exercise programme on the site of the refinery using the area of and around the factory grounds. Over a period of six weeks, experienced and instructed exercise instructors instructed and supervised all training sessions (twice a week for 90 min) of endurance exercise (i.e. jogging, and Nordic walking) in groups. Concepts included fundamental exercise behavioural strategies like 1.) “ start-low, go slow”, 2.) First increase of duration, thereafter intensity, 3.) Combination of endurance, resistance and flexibility and coordination exercises. After this time, a half-day refresher seminar was held and participants received recommendations to continue this exercise programme identically but unsupervised for up to one year. The employees were, however, encouraged to continue meeting in the same groups. Monthly supervised exercise training sessions with the same instructors (10 sessions over 46 weeks) were added to the unsupervised intervention phase in order to improve motivation and maintain adherence even beyond the supervised phase. General nutritional advice was given at the initial and refresher seminar, but not in an individual setting. Clinical investigations were performed locally by the occupational medical department of the refinery after being instructed by external staff of the department of prevention and sports medicine of the Technical University of Munich, Germany. These examinations were performed at baseline (V1) before the seminar, after three months (V2) and after 12 months (V3) (Fig. 1). As a primary parameter, endothelial function was measured by finger plethysmography (©EndoPAT, itamar, Italy) after induction by upper-arm occlusion and changes were expressed as Reactive-Hyperaemia-Index (RHI) [13]. Secondary parameters included vascular function parameters CAVI (Cardio-Ankle-Vascular-Index), ABI (Ankle-Brachial-Index) and AI (Augmentation Index). Moreover, maximal exercise capacity (Watt/kg) was assessed by bicycle ergometry at V1 and V3. Anthropometric parameters (body weight, body-fat and waist-circumference) and systolic as well as diastolic blood pressure was assessed at all three time-points. Clinical chemistry at all three time-points included inflammatory and metabolic parameters, e.g. blood glucose, HbA1c, total cholesterol, HDL, LDL, triglycerides, C-reactive protein and Omega-3-Index [14]. Also, lifestyle and health status were assessed by questionnaire at V1, V2, and V3. The PROCAM (Prospective Cardiovascular Munster) score [15] was calculated to predict and evaluate the 10-year cardiovascular risk. Data from the baseline and after twelve months are reported in this manuscript. Fig. 1Flow diagram of recruitment, examinations and intervention ## Statistics This is a prospective, mono-centre, non-controlled intervention study. Data was analysed in an exploratory manner. Descriptive statistics were performed reporting mean values, median, standard deviation, minimal and maximal values as well as number of patients per analysis. Since sample size was sufficiently large (n > 30) to obtain asymptotically robust results, Student’s t-test for paired samples was applied to assess mean changes in outcome measures between baseline and 12 months follow up. No correction of type I error probability was applied and hence, unadjusted p-values and $95\%$ confidence intervals were reported as exploratory distance and precision indices. For statistical analysis the programme SPSS Version 26.0 for Windows was used. ## Participants Between 2009 and 2012, 327 employees (age 40.8 ± 9.7 years, $88\%$ males) were assigned to 14 separate groups. Assuming a total workforce of approximately 550 employees, this corresponds to a participation rate of $60\%$. Because of the gender structure of an oil refinery, more males than females were recruited ($87.5\%$ vs $12.5\%$). About half of the participants reported to work partly in shift work ($48.2\%$), night work ($48.2\%$) and on weekends ($55.8\%$). $71.3\%$ of the participants worked mainly sedentary, whereas $27.7\%$ worked mainly physically demanding. The baseline characteristics of the participants are listed in Table 1.Table 1Baseline characteristics of the participantsCharacteristicsParticipantsMale/Female ($$n = 327$$) (%)$\frac{286}{41}$ ($\frac{87.5}{12.5}$)Age, years ($$n = 327$$)40.8 ± 9.7 (42.0 / 20–60)Smoking ($$n = 327$$) (%)27 (8.3)Mainly physically inactive ($$n = 301$$) (%)39 (13.0)Medication, Hypertension ($$n = 303$$) (%)28 (9.2)Medication, Hypercholesterolaemia ($$n = 303$$) (%)15 (5.0)Shift work ($$n = 301$$) (%)145 (48.2)Night work ($$n = 301$$) (%)145 (48.2)Weekend work ($$n = 301$$) (%)168 (55.8)Mainly computer work ($$n = 303$$) (%)216 (71.3)Mainly physical demanding work ($$n = 303$$) (%)84 (27.7)Data for categorical variables are presented as numbers (n) and percentages (%)Data for continuous variables are presented as mean ± standard deviation (median / minimum – maximum) Data from 317 participants could be followed at three months and 307 participants at 12 months (Fig. 1). Regarding adherence of the supervised training sessions, the trainers reported a training adherence of $68\%$ of the first six weeks supervised phase and $42\%$ in the following weeks with reduced supervision. ## Anthropometry Weight, body mass index (BMI) and body fat remained stable after 12 months. Waist circumference decreased (-1.8 cm, $95\%$ CI: -2.5 to -1.1) (Table 2).Table 2Comparison of anthropometry, cardiovascular risk factors, physical fitness and vascular function between baseline examination and final examination after 12 monthsnBaselineAfter 12 monthsp-value$95\%$ CImean changeAnthropometry Weight (kg)29188.5 ± 15.688.1 ± 15.00.060-0.9; + 0.02 Waist Circumference (cm)29192.6 ± 12.290.8 ± 11.7 < 0.001-2.5; -1.1 Body Mass Index (kg/m2)29127.0 ± 4.026.9 ± 3.80.085-0.3; + 0.02 Body Fat (%)29121.3 ± 4.921.5 ± 4.90.232-0.2; + 0.6Risk factors Haemoglobin A1c (%)2925.6 ± 0.45.5 ± 0.4 < 0.001-0.2; -0.09 Glucose (mg/dl)29178.3 ± 13.281.6 ± 15.50.001 + 1.4; + 5.2 C-reactive Protein (mg/dl)2920.39 ± 0.380.16 ± 0.28 < 0.001-0.28; -0.18 Uric Acid (mg/dl)2945.8 ± 1.25.8 ± 1.10.428-0.1; + 0.2 Ferritin (mg/dl)294137.3 ± 128.9128.4 ± 119.60.00415.1; -2.9 Triglycerides (mg/dl)294122.7 ± 67.6120.0 ± 84.60.485-10.4; + 4.9 Total Cholesterol (mg/dl)294205.1 ± 38.4204.6 ± 38.50.754-3.6; + 2.6 Low Density Lipoprotein (mg/dl)294131.1 ± 36.5130.0 ± 35.60.421-4.1; + 1.7 High Density Lipoprotein (mg/dl)29453.7 ± 13.554.3 ± 13.40.242-0.4; + 1.6 Omega-3-Index (%)2195.5 ± 1.35.5 ± 1.40.335-0.2; + 0.06 10-year cardiovascular risk (%) by PROCAM score)2863.7 ± 4.93.1 ± 3.60.006-1.0; -0.2 -Physical fitness Heart Rate, at rest (bpm)29067.1 ± 11.666.8 ± 12.90.652-1.7; + 1.1 Heart Rate, maximal (bpm)287165 ± 17.4167 ± 16.60.060-0.07; + 3.3 Maximal exercise capacity (Watt)287201.7 ± 39.6209.8 ± 38.9 < 0.001 + 5.1; + 10.9 Relative exercise capacity (Watt/kg)2872.3 ± 0.502.4 ± 0.5 < 0.001 + 0.1; + 0.13Vascular function Reactive-Hyperaemia- Index2862.00 ± 0.721.87 ± 0.490.007-0.2; -0.04 Cardio-Ankle-Vascular-Index2876.98 ± 1.327.07 ± 1.480.341-0.96; + 2.8 Ankle-Brachial-Index2901.17 ± 0.111.17 ± 0.110.892-1.6; + 1.4 Systolic blood pressure (mmHg)290140.6 ± 16.0140.7 ± 14.60.966-1.6; + 1.6 Diastolic blood pressure (mmHg)29087.6 ± 10.088.4 ± 9.90.115-0.2; + 1.9Data are shown as mean ± standard deviationCI Confidence intervalp-values and $95\%$ confidence intervals are based on two-sided Student t-test for paired samples (no correction for Type I error inflation due to multiple testing applied); PROCAM Prospective Cardiovascular Munster ## Risk factors The metabolic and inflammatory parameters HbA1c and C-reactive protein improved over the 12 months follow-up period, whereas changes in uric acid, blood lipids and Omega-3-Index did not change. Fasting glucose ($$p \leq 0.001$$) and ferritin ($$p \leq 0.004$$) slightly deteriorated. The 10-year cardiovascular risk by PROCAM improved by $0.6\%$ ($95\%$ CI: -1.0 to -$0.2\%$) (Table 2). ## Physical fitness Bicycle ergometry revealed that maximal exercise capacity improved by + 8.1 W ($95\%$ CI: + 5.1 to + 10.9 W). Resting and maximal heart rate remained largely unchanged (Table 2). ## Vascular function Endothelial function analysed as Reactive-Hyperaemia-Index revealed a slight decrease (-0.13, $95\%$ CI: -0.0.2; -0.04), whereas no systematic changes in blood pressure, Cardio-Ankle-Vascular-Index and Ankle-Brachial-Index could be established with sufficient statistical confidence (Table 2). ## Discussion Introducing a short-term life-style programme with exercise intervention as the core approach within a worksite prevention strategy initiative was associated with long-term minor improvements of physical fitness, abdominal obesity, systemic inflammation, glucose metabolism as well as 10-year-cardiovascular risk by PROCAM (Table 2). Of note, this intervention only consisted of a two-day seminar focusing on behavioural changes, a six weeks supervised exercise intervention twice weekly, a follow-up seminar of a half day and encouragement to continue exercise intervention, which was supported by monthly supervised exercise sessions. Data shown are the result of a long-term assessment after one year, far beyond the active intervention phase of six weeks. Moreover, the programme was introduced in a large proportion of employees involving $60\%$ of the whole work force at one company, which is, as a refinery, not per se a classical preventive strategies open employee cohort, characterized by a proportion of $50\%$ working night or weekend shifts (Table 1). However, the achieved changes were not clinically relevant and no systematic changes of vascular function parameters, lipids or fatty acid profile could be established in this healthy sample (Table 2). With regard to the relatively small effects on metabolic and vascular components after 12 months, it has to be taken into account that the study participants were a relatively healthy cohort (10-year cardiovascular risk at baseline: $3.7\%$; smoking rate at baseline: $8.3\%$). These findings are in line with previous studies and systematic reviews [7, 16]. Workplace interventions have shown to reduce body weight and waist circumference, but associations with biochemical markers as well as on blood pressure were inconclusive and small, respectively [17]. An ecological study, which examined trends of cardiovascular risk factors from 2008 – 2017 in oil refinery workers, found increasing rates only of hypertension and diabetes. Rates of low HDL, high LDL, high cholesterol, smoking and coronary artery risk decreased. The authors stated a medium cardiovascular risk in oil refinery workers and called for systematic health promotion at the workplace [18]. However, when starting with abnormal baseline levels, improvements might be far larger. In our study, with regard to endothelial function measured by RHI as primary endpoint, mean baseline values of RHI 2.00 ± 0.72 and RHI 1.87 ± 0.49 after 12 months were low, but still were above the cut-off value for endothelial dysfunction of RHI ≤ 1.67 [13, 19]. Previous studies revealed little effect of exercise training on endothelial function in healthy individuals, but endothelial function improved particularly in those with abnormal baseline endothelial function [20]. Nutritional advice or counselling beyond the introductory seminar was not given, which may explain that body weight was not considerably reduced over 12 months. Moreover, no changes of the Omega-3-Index, a parameter dependent on the consumption of unsaturated fatty acids, was observed (Table 2), although values were very low at baseline [14]. Therefore, individual nutritional counselling could have been a valid prevention strategy in addition to exercise intervention, as has shown by previous investigations [21–23]. We, however, did not include individual nutritional advice into our programme, as it was reported from previous experience by the corporate medical department and the employee representation that the employees were reluctant to adopt changes in nutrition to a large extend. It was felt that focussing on exercise intervention was more feasible and long-lasting. This approach was confirmed by our 12-months programme results that exercise training was only associated with improved exercise capacity also long-term. These findings are beyond most studies and even our experience that improvements observed during supervised intervention can mostly not be maintained during follow-up [24]. Obviously, the monthly training sessions offered onsite the factory area seem to be a practical and successful approach for maintaining adherence to exercise programmes. In a sub-analysis (data not shown) we classified the trainings adherence of all participants to low, moderate and high. In all three groups there was a slight reduction of $0.5\%$ to $0.7\%$ in the 10-year cardiovascular risk by PROCAM after 12 months. In general, physical activity have multiple health benefits, particularly on metabolic control, vascular function and cardiovascular morbidity and mortality and is strongly recommended in recent guidelines [25, 26]. However, there seems to be an exercise paradox when comparing exercise performed during leisure time or during the occupational setting. Data from the Copenhagen General Population Study with 104,046 adults revealed that higher leisure time physical activity is associated with a $15\%$ reduction of risk for major cardiovascular events (MACE) and a $40\%$ reduction of all-cause mortality risk [27]. At the same time, higher occupational physical activity was associated with a $35\%$ increase of MACE risk and a $27\%$ increase of risk for death [27]. These paradoxical results may be explained by the different characters of leisure and occupational physical activities and by occupational various stressors, e. g. shift work [28]. Regarding monetary benefits, occupational health programmes offer a lot of potential. However, cost effectiveness of corporate wellness programmes has been challenged in a recent review [16]. The economic impact of the programmes is hard to monetise. Given the small changes and unknown cost implications, the economic evidence of workplace-interventions remains uncertain. We did not apply any systematic cost-analysis, but as we have calculated approximately €300 for each individual for one year including seminars and exercise sessions, we are convinced that this programme is cost-effective. This amount, however, does not include risk assessment examinations. ## Limitations The strongest limitation is the lack of a control group. Consequently, no causal conclusions may be drawn from the results. However, the changes during intervention are certainly induced by the life-style changes, while a control group would perhaps even deteriorate. Nonetheless, a randomized trial would have been optimal. Regarding inclusion, maybe there is a selection bias, because health-conscious people are more likely to apply to health promotion programmes. Moreover, inclusion of more females would have been relevant to see changes in that group and compare these with male counterparts. However, females are not represented at an oil refinery because of the heavy labour of most jobs. Therefore, the number was overall low. Nonetheless, the strength of the current study is that $60\%$ of employees participated in the 12 months programme and adherence was high. In our experience this is mainly linked to the strong support of the company´s executive board in combination with the internal media and occupational health department. The health seminar added by medical examinations was clearly seen as an incentive for workers, which improved adherence to the programme. Previous studies have shown that unionization and management support were the strongest predictors of the adoption of health programmes [29]. ## Conclusion The results of the evaluation and personal experience performing the Moving programme imply that it is a feasible and successful practice example. The introduction of a behavioural seminar including education and advice over two days regarding medical background and practical applications of life-style measures followed by supervised exercise for six weeks was associated with minor long-term twelve-month improvements in body composition, physical fitness and inflammatory state. In this healthy sample, these changes were, however, not clinically relevant and not accompanied by statistically robust improvements of vascular function. Targeting especially those employees high at risk with a worksite exercise intervention would be more effective, and thus could be highly cost-effective for companies and health insurances in the long-term [7, 28]. ## References 1. Schaller N. Prevalence of the metabolic syndrome and its risk factors: results of a large work-site health assessment. Dtsch Med Wochenschr (2014) 139 2279-2284. PMID: 25350240 2. Proper KI, van Oostrom SH. 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--- title: 'Efficacy and safety of duloxetine in painful diabetic peripheral neuropathy: a systematic review and meta-analysis of randomized controlled trials' authors: - Chung-Sheng Wu - Yu-Jui Huang - Yuan-Chun Ko - Che-Hsiung Lee journal: Systematic Reviews year: 2023 pmcid: PMC10031998 doi: 10.1186/s13643-023-02185-6 license: CC BY 4.0 --- # Efficacy and safety of duloxetine in painful diabetic peripheral neuropathy: a systematic review and meta-analysis of randomized controlled trials ## Abstract ### Background Painful diabetic peripheral neuropathy (PDPN) is a key concern in clinical practice. In this systematic review and meta-analysis, we compared duloxetine and placebo treatments in terms of their efficacy and safety in patients with PDPN. ### Methods Following the PRISMA guidelines, we searched the Cochrane Library, PubMed, and Embase databases for relevant English articles published before January 11, 2021. Treatment efficacy and safety were assessed in terms of pain improvement, patient-reported health-related performance, and patients’ quality of life. ### Results We reviewed a total of 7 randomized controlled trials. Regarding pain improvement, duloxetine was more efficacious than placebo (mean difference [MD] − 0.89; $95\%$ confidence interval [CI] − 1.09 to − 0.69; $P \leq .00001$). Furthermore, duloxetine significantly improved the patients’ quality of life, which was assessed using the Clinical Global Impression severity subscale (MD − 0.48; $95\%$ CI − 0.61 to − 0.36; $P \leq .00001$), Patient Global Impression of Improvement scale (MD − 0.50; $95\%$ CI − 0.64 to − 0.37; $P \leq .00001$), and European Quality of Life Instrument 5D version (MD 0.04; $95\%$ CI 0.02 to 0.07; $$P \leq .0002$$). Severe adverse events were rare, whereas nausea, somnolence, dizziness, fatigue, constipation, and decreased appetite were common; approximately, $12.6\%$ of all patients dropped out because of the common symptoms. ### Conclusions Duloxetine is more efficacious than placebo treatments in patients with PDPN. The rarity of severe adverse events indicates that duloxetine is safe. When a 60-mg dose is insufficient, 120 mg of duloxetine may improve PDPN symptoms. Our findings may help devise optimal treatment strategies for PDPN. ### Systematic review registration PROSPERO CRD42021225451 ### Supplementary Information The online version contains supplementary material available at 10.1186/s13643-023-02185-6. ## Background Painful diabetic peripheral neuropathy (PDPN) is caused by chronic hyperglycemia and characterized by nerve damage and intolerable pain [1]. Approximately $50\%$ of all patients with diabetes develop peripheral neuropathy [2], 10 to $26\%$ of whom experience PDPN [3]. Clinical manifestations of PDPN include the neuropathy of distal lower extremities, which involves tingling, shooting pain, burning pain, allodynia, hyperesthesia, and other unusual sensations. Symptoms often deteriorate at night and affect sleep quality. Some patients may experience mood disorders, such as anxiety and depression [4–6]. The precise pathophysiology of PDPN remains debatable. A commonly accepted hypothesis is that distal nerve fiber damage would result in altered peripheral signaling and compensatory changes in the central nervous system, thereby disrupting the mechanisms underlying the inhibition of endogenous pain; this would promote the hyperexcitability and sensitization of pain-transmitting pathways and cause severe and persistent pain [4, 7]. The neurotransmitters serotonin (5-hydroxytryptamine) and norepinephrine help modulate nociceptive transmission by descending pain inhibitory pathways in the brain and spinal cord [8–10]. Duloxetine hydrochloride is a potent dual serotonin–norepinephrine reuptake inhibitor (SNRI); it modulates the mechanisms underlying PDPN [11]. SNRI antidepressants increase noradrenaline levels and target α2-adrenergic receptors in the dorsal horn of the spinal cord and the locus coeruleus. These pathways are highly efficacious against allodynia and hyperalgesia, which are associated with neuropathic pain. Serotonin may also enhance the inhibitory effects of noradrenaline in an auxiliary manner [12]. Currently, duloxetine and pregabalin are the only drugs approved by the US Food and Drug Administration (FDA) for treating PDPN. Pregabalin and duloxetine have been recommended as the first-line therapy in a total of 5 and 4 relevant guidelines, respectively. The 4 guidelines recommending duloxetine are the Neuropathic Pain Special Interest Group guideline (International Association for the Study of Pain), European Federation of Neurological Societies guideline, National Institute for Health and Care Excellence guideline (UK), and Canadian Pain Society guideline [13–17]. The additional guideline recommending pregabalin is the American Academy of Neurology guideline; the aforementioned difference between the 2 drugs in terms of recommendation stems from the fact that a limited number of duloxetine trials were graded as class I, and thus, data were regarded to be insufficient for level A recommendation for this drug [18, 19]. Nevertheless, duloxetine is not inferior to pregabalin in treating PDPN [20, 21]. To the best of our knowledge, no meta-analysis conducted after 2015 has evaluated the role of duloxetine in the treatment of PDPN [22]. Moreover, our knowledge regarding the clinical utility of duloxetine, different efficacies of its prescribed dose, and severity of associated adverse events in patients with PDPN remains limited. Therefore, in the present updated systematic review and meta-analysis of randomized-controlled trials (RCT; placebo-controlled), we evaluated the efficacy and safety of duloxetine in the treatment of PDPN. We extracted data from earlier studies in which relevant scales and questionnaires were used to investigate the optimal dose for treatment, the drug’s efficacy in improving pain and patients’ quality of life and the severity and occurrence of adverse events. ## Study design and inclusion criteria This systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [23]. The study protocol was developed and registered in the PROSPERO database (CRD42021225451; January 9, 2021). The selection criteria were defined before the literature search and included the PICO components—problem/population, intervention, comparison, and outcome. We included studies on the comparison between duloxetine and placebo treatments in adult patients with PDPN due to diabetic polyneuropathy who presented with daily pain for > 6 months, in whom the symptoms started from distal extremities bilaterally, and whose weekly average visual analog scale pain scores were > 4. All doses and treatment durations of duloxetine were reviewed. The primary outcome measure was a reduction in patients’ weekly mean pain scores, assessed using an 11-point Likert-type scale, for 24-h average pain. The secondary outcomes included 24-h worst pain and night pain; numbers of patients with $30\%$ and $50\%$ reductions in 24-h pain scores [24]; and scores on the Brief Pain Inventory (BPI) scale (severity and interference) [25], Short Form-36 (SF-36) questionnaire [26], Patient Global Impression of Improvement (PGI-I) scale [27], Clinical Global Impression scale (CGI; severity subscale) [28], European Quality of Life Instrument 5D version (EQ-5D) [29], and Short-Form McGill Pain Questionnaire (SF-MPQ; sensory component) [30]. Adverse events and compliance were assessed in terms of the number of patients discontinued because of adverse events, number of patients with at least one adverse event or severe adverse event, and incidence rate of common adverse events. Non-RCTs, case reports, reviews, animal studies, letters to editors, and conference abstracts were excluded from our review. No restrictions related to trial duration were applied. ## Search strategy We searched the Cochrane Library, PubMed, and EMBASE databases for relevant RCTs published in English before January 11, 2021. Keywords used for the search in each database were as follows: (((DM or “diabetes mellitus” or diabet) and (neuropathy or neuropath or neurolog* or neuralgia)) or ((PDPN or “painful diabetic peripheral neuropath” or DPNP or “diabetic peripheral neuropathic pain” or DPN or “diabetic peripheral neuropath” or PDN or “peripheral diabetic neuropath” or DSPN or “distal symmetric polyneuropath”) or (“diabetic polyneuropath” or “diabetic sensorimotor polyneuropath” or “distal symmetric sensorimotor polyneuropath” or “diabetic distal sensorimotor polyneuropath”) or (“diabetic focal neuropath” or “diabetic multifocal neuropath” or “diabetic amyotroph” or “diabetic autonomic neuropath” or “symmetric diabetic proximal motor neuropath” or “diabetic mononeuropath”))) and (duloxetin* or cymbalta or irenka or “drizalma sprinkle” or ariclaim or xeristar or yentreve). In addition, the reference lists of the included studies were manually searched for additional eligible studies. ## Study selection and quality assessment After removing duplicate studies, 2 reviewers (CS Wu and YR Huang) independently reviewed the titles and abstracts of the eligible studies. Non-RCTs and non-eligible studies were excluded (upon reviewer consensus) from further analyses. Subsequently, the full texts of the included articles were analyzed. Both reviewers independently assessed the risk of bias by using the Cochrane risk-of-bias tool for RCTs (RoB 2.0) [31]. Any disagreement between the 2 reviewers was mitigated by a third reviewer. ## Data collection and analysis Odds ratio (OR) and $95\%$ confidence interval (CI) values were calculated using the Mantel–Haenszel method for dichotomous outcome data, whereas the mean difference (MD) and $95\%$ CI values were calculated through inverse variance weighting for continuous data. Outcome data, such as sample size, mean, and standard deviation (SD), were extracted for each treatment. If the included articles reported only standard error values, the corresponding SD values were calculated using relevant software. Heterogeneity was investigated using the I2 statistic, and $25\%$, $50\%$, and $75\%$ values indicated low, moderate, and high degrees of heterogeneity, respectively [32]. The pooled estimates of the MD and OR values were calculated using random-effects models. In addition to reporting the overall effects of duloxetine, we also have done subgroup analyses of the effects of different doses of duloxetine on primary outcomes of reduction in patients’ weekly mean pain scores. Statistical significance was set at $P \leq 0.05.$ The meta-analysis was performed using RevMan (version 5.4) [33]. ## Included articles Our search returned a total of 1862 relevant articles; of them, 1475 were excluded after the removal of duplicates. Next, 794 studies were excluded after the titles and abstracts were screened. The full texts of the remaining 681 articles were reviewed, and 674 of them were eliminated on the basis of the exclusion criteria. Finally, a total of 7 eligible RCTs [34–40] were included in our meta-analysis. Figure 1 depicts the flowchart of study selection. Fig. 1Flowchart of study selection ## Study characteristics The 7 included studies comprised a total 2205 patients (men, 1246 [$56.5\%$]; mean age, 60.2 years). Patients received 20, 40, 60, and 120 mg of duloxetine per day. Six studies had a follow-up duration of 12 weeks, and one study had a follow-up duration of 8 weeks [39]. Reduction of 24-h weekly mean pain score (daily scores were recorded in a diary) was reported as the primary outcome in 6 studies and the reduction in 24-h average BPI pain score (scores recorded during weekly visits) in one study. Dropout rates were between $13.6\%$ and $25.7\%$, which were not sufficiently high to affect the statistical power. Table 1 presents a summary of the characteristics of the included studies. Table 1Characteristics of the included studiesCharacteristicsParticipantsInterventionComparisonOutcomeSourceStudy designCountryJadad scoreDiagnosisAge/Case noFemale No.(%)DLXPLCPrimary outcomeDLXPLCDrop-out rate, %Severe AE DLX/PLC, NoCase no./dose/frequency/durationCase no./dose/frequency/durationBaseline pain score, Mean ± SEGao et al., 2015 [40]Double-blind, RCTChina31. Daily pain > 6 mos2. Michigan score ≥ 33. BPI weekly pain ≥ $\frac{461.4}{405223}$ ($55.1\%$)$\frac{203}{60}$ mg/QD/12 wks202/PLC/QD/12 wksWeekly mean 24-h avg. pain score5.7 ± 1.75.6 ± $1.713.80\%$$\frac{3}{2}$Rowbotham et al., 2012 [39]Double-blind, rctUSA51. Daily pain > 6 mos2. Michigan score ≥ 33. Weekly 24-h pain score ≥ $\frac{459.9}{10848}$ ($44.4\%$)$\frac{57}{60}$ mg/QD/8 wks51/PLC/QD/8 wksWeekly mean 24-h avg. pain score6.61 ± 1.376.62 ± 1.23-$\frac{1}{1}$Gao et al., 2010 [38]Double-blind, rctChina41. Daily pain > 6 mos2. Michigan score ≥ 33. Weekly 24-h pain score ≥ 459.3,215114 ($53.0\%$)$\frac{109}{60}$ mg or 120 mg/QD/12 wks106/PLC/QD/12wksBPI avg. pain5.5 ± 1.35.5 ± $1.413.60\%$$\frac{2}{2}$Yasuda et al., 2011 [34]Double-blind, rctJapan41. Daily pain > 6 mos2. DSPN diagnostic criteria in Japan$\frac{60.8}{33882}$ ($24.3\%$)40 mg: $\frac{85}{40}$ mg/QD/12 wks167/PLC/QD/12 wksWeekly mean 24-h avg. pain score40 mg: 5.79 ± 1.235.78 ± $1.1716.70\%$$\frac{5}{660}$ mg: $\frac{86}{60}$ mg/QD/12 wks60 mg: 5.76 ± 1.17Wernicke et al., 2006 [35, 41]Double-blind, RCTUSA51. Daily pain > 6 mos2. Michigan score ≥ 33. Weekly 24-h pain score ≥ $\frac{460.7}{334130}$ ($38.9\%$)60 mg: $\frac{114}{60}$ mg/QD/12 wks108/PLC/QD/12 wksWeekly mean 24-h avg. pain score60 mg: 6.1 ± 1.65.9 ± $1.425.70\%$$\frac{7}{5120}$ mg: $\frac{112}{60}$ mg/BID/12 wks120 mg: 6.2 ± 1.5Raskin et al., 2005 [36]Double-blind, RCTCanada51. Daily pain > 6 mos2. Michigan score ≥ 33. Weekly 24-h pain score ≥ $\frac{458.8}{348186}$ ($53.4\%$)60 mg: $\frac{116}{60}$ mg/QD/12 wks116/PLC/QD/12 wksWeekly mean 24-h avg. pain score60 mg: 5.5 ± 1.15.5 ± $1.315\%$$\frac{6}{4120}$ mg: $\frac{116}{60}$ mg/BID/12 wks120 mg: 5.7 ± 1.3Goldstein et al., 2005 [37]Double-blind, RCTUSA51. Daily pain > 6 mos2. Michigan score ≥ 33. Weekly 24-h pain score ≥ $\frac{460.1}{457176}$ ($38.5\%$)20 mg: $\frac{115}{20}$ mg/QD/12 wks115/PLC/QD/12 wksWeekly mean 24-h avg. pain score20 mg: 5.9 ± 1.65.8 ± $1.524.70\%$Total 1960 mg: $\frac{114}{60}$ mg/BID/12 wks60 mg: 6.0 ± 1.7120 mg: $\frac{113}{60}$ mg/BID/12 wks120 mg: 5.9 ± 1.4Abbreviations: RCT randomized control trial, DLX duloxetine, PLC placebo, No number, BPI Brief Pain Inventory, Wks weeks, Mos months, Avg average, QD quaque die, SE standard error, AE adverse event, DSPN distal symmetric polyneuropathy ## Pain improvement A total of six studies reported improvements in weekly mean pain scores. Duloxetine was more efficacious than placebo, with low to moderate heterogeneity noted across studies (MD = − 0.95, $95\%$ CI = − 1.18 to − 0.72, $Z = 7.79$, $P \leq 0.00001$, and I2 = $36\%$; Fig. 2A). Subgroup analysis of different doses of duloxetine indicated significant improvements with doses of 40, 60, and 120 mg but not with that of 20 mg (MD = − 0.45, $95\%$ CI = − 1.05 to 0.15, and $$P \leq 0.14$$). Head-to-head comparisons between the effective doses revealed no significant differences. Fig. 2Pain. A Mean improvement in the weekly average of patients’ 24-h pain scores on an 11-point Likert-type scale at ≤ 12 weeks. B Mean improvement in pain severity assessed using the Brief Pain Inventory scale: average pain scores at ≤ 12 weeks. C Number of patients with ≥ $50\%$ improvement in the weekly average of 24-h pain scores on the 11-point Likert-type scale at ≤ 12 weeks. D Number of patients with ≥ $30\%$ improvement in the weekly average of 24-h pain scores on the 11-point Likert-type scale at ≤ 12 weeks. E Mean improvement in patients’ night pain scores on the 11-point Likert-type scale at ≤ 12 weeks. F Mean improvement in patients’ worst pain scores on the 11-point Likert-type scale at ≤ 12 weeks Six studies reported improvements in BPI average pain scores, and overall, duloxetine was significantly more efficacious than placebo, with low heterogeneity observed across studies (MD = − 0.88, $95\%$ CI = − 1.08 to − 0.68, $Z = 8.54$, $P \leq 0.00001$, and I2 = $14\%$; Fig. 2B). In total, 6 studies reported the numbers of patients with a $50\%$ pain reduction. Overall, the efficacy of duloxetine was higher than that of a placebo, with low heterogeneity noted across studies (OR = 2.06, $95\%$ CI = 1.67 to 2.54, $Z = 6.72$, $P \leq 0.00001$, and I2 = $25\%$; Fig. 2C). A total of 6 studies reported a number of patients with a $30\%$ pain reduction. Duloxetine was significantly more efficacious than placebo, with low heterogeneity observed across studies (OR = 2.25, $95\%$ CI = 1.86 to 2.72, $Z = 8.42$, $P \leq 0.00001$, and I2 = $0\%$; Fig. 2D). Improvements in night pain scores were reported in a total of 5 studies. The efficacy of duloxetine was higher than that of a placebo, with low heterogeneity noted across studies (MD = − 0.89, $95\%$ CI = − 1.09 to − 0.69, $Z = 8.70$, $P \leq 0.00001$, and I2 = $0\%$; Fig. 2E). In total, 5 studies reported improvements in worst pain scores. Duloxetine was significantly more efficacious than placebo, with low to moderate heterogeneity noted across studies (MD = − 1.05, $95\%$ CI = − 1.31 to − 0.80, $Z = 8.12$, $P \leq 0.00001$, and I2 = $31\%$; Fig. 2F). ## Patient-reported health performance and quality of life Improvements in the physical functioning, mental health, and bodily pain domains of the SF-36 questionnaire were reported by a total of 3, 3, and 2 studies, respectively. For all domains, duloxetine exhibited higher levels of efficacy than did placebo, with low heterogeneity across studies for all domains (physical functioning: MD = 2.75, $95\%$ CI = 1.77 to 3.72, $Z = 5.53$, $P \leq 0.00001$, and I2 = $0\%$ (Fig. 3A); mental health: MD = 1.60, $95\%$ CI = 0.56 to 2.63, $Z = 3.03$, $$P \leq 0.002$$, and I2 = $0\%$ (Fig. 3B); and bodily pain: MD = 6.88, $95\%$ CI = 4.15 to 9.60, $Z = 4.95$, $P \leq 0.00001$, and I2 = $0\%$ (Fig. 3C)).Fig. 3Quality of life. A Mean improvement in patients’ scores on the Short Form-36 (SF-36) physical functioning domain at ≤ 12 weeks. B Mean improvement in patients’ scores on the SF-36 mental health domain at ≤ 12 weeks. C Mean improvement in patients’ scores on SF-36 bodily pain domain at ≤ 12 weeks. D Patients’ scores on the Patient Global Impression of Improvement scale at ≤ 12 weeks. E Mean improvement in patients’ scores on the Brief Pain Inventory (interference) scale: average of the scores on 7 items at ≤ 12 weeks. F Mean improvement in patients’ scores on the Clinical Global Impression severity subscale at ≤ 12 weeks. G Mean improvement in patients’ scores on the European Quality of Life Instrument 5D version at ≤ 12 weeks. H Mean improvement in patients’ scores on the Short-Form McGill Pain Questionnaire sensory component at ≤ 12 weeks Improvements in patients’ scores on the PGI-I, BPI (interference subscale), CGI (severity subscale), EQ-5D, and SF-MPQ (sensory component) tools were reported by 7, 7, 5, 4, and 4 studies, respectively. Overall, the efficacy of duloxetine in improving the aforementioned scores was significantly higher than that of the placebo, with low to moderate heterogeneity observed among studies (PGI-I: MD = − 0.50, $95\%$ CI = − 0.64 to − 0.37, $Z = 7.31$, $P \leq 0.00001$, and I2 = $44\%$ (Fig. 3D); BPI: MD = − 0.69, $95\%$ CI = − 0.85 to − 0.53, $Z = 8.39$, $P \leq 0.00001$, and I2 = $0\%$ (Fig. 3E); CGI: MD = − 0.48, $95\%$ CI = − 0.61 to − 0.36, $Z = 7.64$, $P \leq 0.00001$, and I2 = $21\%$ (Fig. 3F); EQ-4D: MD = 0.04, $95\%$ CI = 0.02 to 0.07, $Z = 3.75$, $$P \leq 0.0002$$, and I2 = $0\%$ (Fig. 3G); and SF-MPQ: MD = − 2.97, $95\%$ CI = − 3.68 to − 2.27, $Z = 8.22$, $P \leq 0.00001$, and I2 = $0\%$ (Fig. 3H)). ## Safety and compliance A total of 7 studies reported a number of patients who dropped out because of adverse events. The risk associated with duloxetine was significantly higher than that associated with placebo, with low heterogeneity noted across studies (OR = 3.00, $95\%$ CI = 2.18 to 4.13, $Z = 6.72$, $P \leq 0.00001$, and I2 = $0\%$; Fig. 4A).Fig. 4Adverse events. A Numbers of participants who dropped out from the studies because of adverse events. B Numbers of participants with at least a single adverse event In total, 6 studies reported a number of patients with at least 1 adverse event. Duloxetine was associated with significantly higher levels of risk than was placebo, with low heterogeneity observed across studies (OR = 1.80, $95\%$ CI = 1.47 to 2.21, $Z = 5.62$, $P \leq 0.00001$, and I2 = $0\%$; Fig. 4B). A total of 5 studies reported the numbers of all adverse events. We analyzed the 6 most common adverse events associated with duloxetine (Table 2). Nausea had the highest incidence rate ($20.21\%$; 10.4 to $30.2\%$), followed by somnolence ($12.73\%$; 8.4 to $21.6\%$), dizziness ($10.10\%$; 5.8 to $15.1\%$), fatigue/malaise ($8.14\%$; 5.0 to $12.4\%$), constipation ($8.01\%$; 5.0 to $12.8\%$), and decreased appetite ($2.89\%$; 5.4 to $10.4\%$). The numbers of serious adverse events, defined as events leading to prolonged hospitalization, life-threatening experience, severe disability, or death during the study, reported in the 7 included studies were also recorded (Table 1). No significant differences were observed between duloxetine and placebo; moreover, the studies reported no common severe adverse event, except hyperglycemia, which was reported by a total of 3 studies [36, 37, 40] and electrolyte imbalance, which was reported by a total of 2 studies [35, 39].Table 2Adverse events reported by the included studiesGao [2015] [40], ($$n = 202$$), nGao [2010] [38], ($$n = 106$$), nYasuda [2011] [34], ($$n = 171$$), nWernicke [2006] [35, 41], ($$n = 226$$), nRowbotham [2012] [39], ($$n = 57$$), nTotal ($$n = 762$$), n (%)Nausea213224689154 ($20.21\%$)Somnolence17173726-97 ($12.73\%$)Dizziness17161030477 ($10.10\%$)Fatigue/malaise108928762 ($8.14\%$)Constipation10111129-61 ($8.01\%$)Decreased appetite1111---22 ($2.89\%$) ## Study quality The quality of the included studies was assessed using RoB 2.0; the details are presented in Fig. 5 (ROB) and the supplementary information file. RoB 2.0 is an outcome-based instrument, but since each study’s outcome measures do not have different potential risks of bias (they all have the same assessment process and are all subjective scales), the evaluation is presented within a single study-based figure. A total of 4 studies were classified as low risk and 3 studies (conducted by Goldstein et al. [ 37], Yasuda et al. [ 34], and Gao et al. [ 40]) indicated some concerns. The study by Goldstein et al. had some concerns in RoB 2.0 domains 2 and 3, which was based on a high dropout rate of $24.7\%$ and significant differences between the treatment (duloxetine: 60 mg/day, $13.2\%$; 120 mg/day, $19.5\%$) and placebo ($5.2\%$) groups ($P \leq 0.001$). The study by Yasuda 2011 had some concerns in domains 1 and 2 because allocation concealment was not explained clearly. The study conducted by Gao et al. incompletely described the processes of randomization sequence generation and allocation concealment, leading to some concerns in domains 1 and 2.Fig. 5Risk of bias of the included studies ## Discussion The efficacy of duloxetine in treating PDPN has been demonstrated in a total of 4 RCTs conducted between 2005 and 2011 [34–37]. In total, 3 sequential 52-week-long phase studies have revealed that duloxetine is superior to routine care in the long-term management of PDPN [41–43]. By contrast, another 52-week-long study reported no significant difference between PDPN and routine care in terms of their efficacy in pain control; nevertheless, the findings confirmed the long-term safety of duloxetine [44]. Other medications for PDPN include antiepileptic agents, pregabalin, and gabapentin and certain antidepressants (including tricyclic antidepressants) [45]. However, most of these medications are only partially effective for PDPN; moreover, they are frequently discontinued because of their associated adverse events, which limit their clinical utility. As mentioned, duloxetine and pregabalin are the only 2 FDA-approved drugs for PDPN. Some studies have reported that the efficacy of duloxetine in PDPN pain relief is non-inferior [46, 47] to or even better than that of pregabalin [48, 49]. Only 3 recent systemic reviews and meta-analyses included RCTs on duloxetine for PDPN. Of them, 2 studies included a total of 5 studies [50, 51] and 1 study included a total of 6 studies [22]. Unfortunately, no relevant meta-analyses have been conducted since 2017. In addition to assessing pain severity and PGI scores, which were analyzed in an earlier meta-analysis [43], in the present study, we assessed patients’ health performance and quality of life in terms of night pain, worst pain, and SF-36 (physical functioning, mental health, and bodily pain domains), CGI, BPI (7-item interference), EQ-5D, and SF-MPQ (sensory component) scores. To the best of our knowledge, the present study is the first meta-analysis to explore the incidence rates of common adverse events. Compared with placebo, duloxetine significantly improved patients’ pain scores on every item; the total weighted mean (TWM) of the reduction in the weekly mean of 24-h pain scores was − 2.62 (MD − 0.89; $P \leq 0.00001$). The TWM values of the reductions in the weekly mean values of average pain scores on BPI, night pain scores, and 24-h worst pain scores were –2.64 (MD − 0.84 and $P \leq 0.00001$), − 2.80 (MD − 0.82 and $P \leq 0.00001$), and − 3.02 (MD − 1.00 and $P \leq 0.00001$), respectively. Furthermore, $45.6\%$ and $64.5\%$ of all patients experienced $50\%$ and $30\%$ pain reduction, respectively, which highlights the clinical efficacy of duloxetine in the treatment of PDPN. Duloxetine considerably improved the physical and mental health and the quality of life of patients with PDPN, as evident from their scores on the following tools: CGI (TWM = − 1.39), PGI-I (TWM = 2.47), EQ-5D (TWM = 0.13), SF-36 (physical functioning and mental health domains, respectively: TWM = 6.06 and 1.01), and SF-MPQ (TWM = − 7.80). ( All these analyses included the 20 mg and 40 mg subgroups data). A total of 4 doses of duloxetine were compared in the present study. The 20-mg dose improved only the weekly worst pain and CGI scores of patients with PDPN; this dose exhibited the lowest efficacy in the aforementioned 2 items. This finding implies that the clinical value of the 20-mg dose is low. By contrast, the 40-mg dose markedly improved the patients’ scores on all pain relief items and PGI-I; in terms of efficacy, this dose was non-inferior to the 60- and 120-mg doses. The 40-mg dose achieved $50\%$ pain reduction in most patients. Although only a single study included the 40-mg dose, the aforementioned finding suggests that the 40-mg dose of duloxetine can serve as an alternative to the 60- and 120-mg doses. The 60- and 120-mg doses exhibited significant efficacy in almost all items (except for the low efficacy of the 60-mg dose in improving patients’ SF-36 mental health and EQ-5D scores), and no significant differences were observed between them. However, the 120-mg dose was associated with numerically better outcomes than the 60-mg dose in terms of pain reduction (weekly mean of 24-h pain, BPI average pain, night pain, and worst pain), and health performance, and quality of life. Thus, the 120-mg dose of duloxetine may be useful when the 60-mg dose fails to ensure adequate relief in patients with PDPN. Duloxetine was found to be safe in the treatment of PDPN: the incidence of severe adverse events was low, not higher than that associated with placebo treatments. However, complications such as nausea, somnolence, dizziness, fatigue, constipation, and decreased appetite were common; because of these common adverse events, approximately $12.6\%$ of patients dropped out of the studies. Approximately, $71.3\%$ of all patients experienced at least 1 adverse event. Therefore, when administering duloxetine, patients must be informed about these adverse effects, and they must be closely monitored for the incidence and severity of common adverse events. Symptomatic interventions should be administered as required. The present study has some limitations. We found only 1 additional eligible study [40] compared with the last relevant meta-analysis [22]. Nevertheless, because the sample size of the additional RCT was high (second largest among the 7 included studies; $$n = 405$$) and comprehensive data on pain relief were reported by the RCT, we could provide considerable additional and updated data regarding the efficacy and safety of duloxetine in the treatment of PDPN. Of the studies included in the present study, the study by Gao et al. [ 2010] [29] reported variable dosage schedules for duloxetine depending on clinical responses (60 or 120 mg); this made it challenging for us to perform subgroup analyses. Hence, we performed the analyses for benefit considering that all patients received the higher dose (120 mg) and for harm considering that if all patients received the lower dose (60 mg). We further performed sensitivity analysis by excluding the data of the aforementioned study from each item, and the results remained significant. All 7 studies had similar cohorts and good uniformity in terms of the use of assessment tools. Although recent studies have demonstrated a high efficacy of duloxetine, further studies are warranted to evaluate the effects of various duloxetine doses, particularly the 40-mg dose. Furthermore, head-to-head RCTs on pregabalin, gabapentin, and SNRI drugs are necessary to identify the optimal treatment option for patients with PDPN. ## Conclusions The findings of our systematic review and meta-analysis suggest that duloxetine is more efficacious than placebo treatments in terms of pain relief and improvements in the quality of life of patients with PDPN. 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--- title: Prognostic implication of stress hyperglycemia in patients with acute coronary syndrome undergoing percutaneous coronary intervention authors: - Man Wang - Wen Su - Ning Cao - Hui Chen - Hongwei Li journal: Cardiovascular Diabetology year: 2023 pmcid: PMC10031999 doi: 10.1186/s12933-023-01790-y license: CC BY 4.0 --- # Prognostic implication of stress hyperglycemia in patients with acute coronary syndrome undergoing percutaneous coronary intervention ## Abstract ### Background It is now understood that stress hyperglycemia is associated with adverse outcomes in hospitalized patients. Herein, we aimed to investigate the association between stress hyperglycemia and mortality risk in acute coronary syndrome (ACS) patients who underwent percutaneous coronary intervention (PCI). ### Methods This cohort study comprised 5190 ACS patients who underwent PCI from the Cardiovascular Center Beijing Friendship Hospital Database Bank (CBDBANK) from January 2013 to January 2021. Stress hyperglycemia was defined by the glucose/glycated albumin (GA) ratio, calculated as admission fasting plasma glucose divided by GA. The patients were divided into four groups according to glucose/GA ratio quartiles (Q1-Q4). Cox proportional hazards regression and restricted cubic spline were used to evaluate the association between glucose/GA ratio and all-cause and cardiovascular mortality. ### Results During a median follow-up of 4.0 years, the number of all-cause deaths was 313 ($6.0\%$) and cardiovascular-associated deaths was 177 ($3.4\%$). After adjustment for potential confounders, the risk of all-cause mortality increased in the lowest (HR, 1.43; $95\%$ CI, 1.01–2.03) and highest (HR, 1.51; $95\%$ CI, 1.03–2.21) glucose/GA ratio quartiles compared to Q2. The restricted cubic splines showed that the association between glucose/GA ratio and all-cause mortality was U-shaped after full adjustment (P nonlinear = 0.008). Similar results were observed for cardiovascular mortality. In subgroup analyses according to diabetes status, the U-shaped relationship was only significant in patients with diabetes mellitus. ### Conclusion In ACS patients undergoing PCI, low and high glucose/GA ratio values were associated with an increased all-cause and cardiovascular mortality, especially in those with diabetes mellitus. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12933-023-01790-y. ## Introduction Stress hyperglycemia refers to the transient elevation of blood glucose levels in patients suffering from acute illnesses, such as acute myocardial infarction (AMI), congestive cardiac failure, and cerebrovascular accidents [1–3]. Previous studies have shown that acute stress hyperglycemia was associated with a poor prognosis in patients with acute coronary syndrome (ACS) [4–7]. However, some other studies indicated a significant association between low glycemic levels and adverse outcomes which may provoke confusion [8, 9]. Although patients with previously known diabetes mellitus reportedly have a worse clinical outcome [10–12], it remains controversial how stress hyperglycemia may affect the prognosis of ACS patients with different diabetic status [13, 14]. Different definitions of stress hyperglycemia have been used in the literature based on fasting or random glucose levels [7, 15], which failed to reflect the chronic glycemic levels. Recently, novel markers have been proposed to reflect true acute hyperglycemic status. Most of these markers estimate the average glucose level from glycosylated hemoglobin (HbA1c) [4, 6, 8, 16]. Nevertheless, one recent study used the ratio of fasting plasma glucose (FPG) to glycated albumin (GA) to assess stress hyperglycemia considering the background glucose level before the onset of the acute event [17]. GA is a measure of the mean plasma glucose level over approximately 2–3 weeks, which is shorter than HbA1c, and may reflect glycemic control under conditions with rapid changes in glycemia [18]. Besides, GA is not influenced by conditions such as chronic kidney disease (renal anemia), or hemorrhage which affect erythrocyte lifespan [19]. Thus, GA may provide more accurate information on the actual status of glycemic control compared with HbA1c. However, the association between stress hyperglycemia defined as the ratio of glucose/GA and mortality risk of patients with ACS who underwent percutaneous coronary intervention (PCI) remains unknown, warranting further research. Therefore, the present study aimed to investigate whether stress hyperglycemia, measured by glucose/GA ratio, could predict mortality in ACS patients with or without diabetes who underwent PCI. ## Study population The Cardiovascular Center Beijing Friendship Hospital Database Bank (CBDBANK) is a large prospective cohort study containing patients diagnosed with ACS from the Department of Cardiology of Beijing Friendship Hospital. Patients with ACS (ST-segment elevation myocardial infarction [STEMI], non-ST-segment elevation myocardial infarction [NSTEMI], and unstable angina [UA]) were diagnosed based on relevant guidelines [20, 21]. A total of 8022 patients were diagnosed with ACS and underwent PCI from January 2013 to January 2021. 2832 patients were excluded according to the following exclusion criteria: [1] lack of GA, FPG, or follow-up data; [2] severe liver dysfunction (alanine ≥ 5 times the upper reference limits), severe renal insufficiency (estimated glomerular filtration rate [eGFR] < 30 ml/min/1.73m2), or kidney replacement treatment; [3] severe acute infection, malignancy, or autoimmune disease; [4] previous coronary artery bypass grafting (CABG), cardiogenic shock (defined as systolic blood pressure [SBP] < 90 mmHg for ≥ 30 min or catecholamines to maintain SBP > 90 mmHg, clinical pulmonary congestion and impaired end-organ perfusion [altered mental status, cold/clammy skin and extremities, urine output < 30 ml/h, or lactate > 2.0 mmol/L], or class IV according to the Killip classification), or heart failure (left ventricular ejection fraction [LVEF] < $30\%$). Finally, 5190 patients were included in this study (Fig. 1). The study was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University, and was conducted in accordance with the Declaration of Helsinki. Fig. 1Flowchart for the enrollment of the study population ## Treatment and procedure Coronary angiography and PCI were implemented according to relevant guidelines [22]. All patients received a 300 mg loading dose of aspirin, a 300 to 600 mg loading dose of clopidogrel (or 180 mg of ticagrelor), and 70–100 IU/kg unfractionated heparin. PCI was performed using 6 or 7 Fr guiding catheters via the radial artery approach according to the standard techniques by experienced cardiologists. Patients were treated with predilatation and new-generation drug-eluting stents whenever possible. Standard medication after PCI was continued before discharge, including the maintenance dose of aspirin (100 mg/day), clopidogrel (75 mg/day) or ticagrelor (180 mg/day), statin, angiotensin-converting enzyme inhibitors (ACEI) or angiotensin II receptor blockers (ARB), and beta-blockers. ## Assessment of stress hyperglycemia Overnight fasting venous blood samples were drawn from patients within 24 h after admission and immediately transferred to the central laboratory (Beijing Friendship Hospital) for testing GA and FPG using standard laboratory techniques. Detailed workflow for blood sample collection is shown in Additional file 1: Figure S1. The GA level was presented as a percentage of total serum albumin. Stress hyperglycemia was defined by the glucose/GA ratio [17], which was calculated by using the following equation: glucose/GA ratio = admission FPG (mmol/L)/GA (%). The use of FPG as the numerator instead of admission random blood glucose was based on the fact that it had a greater prognostic value in patients with acute cardiovascular disease [3, 23, 24], was almost unaffected by food or other sugary infusions [3, 25] and exhibited little interindividual heterogeneity [24]. The patients were divided into quartiles of glucose/GA ratio (Q1 < 0.334, Q2 = 0.334–0.384, Q3 = 0.385–0.442, Q4 > 0.442) for further analysis. ## Follow-up and outcome Relevant information regarding cardiovascular events during hospitalization was confirmed based on their medical records. Clinical follow-up was performed at 1, 6, and 12 months and every year after discharge by telephone interview or outpatient follow-up. The primary endpoint was all-cause mortality during hospitalization and over the follow-up period. Cardiovascular mortality was a secondary outcome. Cardiovascular death was defined as death caused by stroke, AMI, heart failure, or documented sudden cardiac death. ## Covariates Baseline characteristics, including demographic information (age, sex), medical history, lifestyles (smoking and drinking status [none, ever, current], body mass index [BMI]), laboratory results, and in-hospital therapy were collected from hospital records. The medical history included the presence of comorbidities, including diabetes, hypertension, dyslipidemia, previous coronary heart disease, and chronic kidney diseases. The diagnostic criteria for diabetes include: [1] previously diagnosed diabetes under treatment of antidiabetic medication; [2] the typical symptoms of diabetes with an FPG ≥ 7.0 mmol/L and/or random blood glucose ≥ 11.1 mmol/L and/or 2-h blood glucose after oral glucose tolerance test ≥ 11.1 mmol/L. Hypertension was defined as SBP ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg three times on different days and/or under antihypertensive treatments. Overnight fasting blood samples were obtained and tested for total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride, high-density lipoprotein cholesterol (HDL-C), hemoglobin, albumin, high-sensitivity C-reactive protein (hs-CRP), cardiac troponin I (cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and creatinine in the central laboratory by standard methods. Dyslipidemia was defined as TC > 5.18 mmol/L (200 mg/dL), LDL-C > 3.37 mmol/L (130 mg/dL), triglyceride > 1.72 mmol/L (150 mg/dL), HDL-C < 1.0 mmol/L (40 mg/dL), and/or previous use of lipid-lowering agents. The eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) formula: eGFR (mL/min/1.73 m2) = 175 × (Scr)−1.154 × (Age)−0.203 × (0.742 if female) × (1.212 if African American) [26]. Echocardiograms were performed by cardiologists or certified sonographers, and the LVEF was assessed using the Simpsons method. Medications were obtained directly from the medical records, including aspirin, clopidogrel or ticagrelor, β-blocker, ACEI or ARB, and statins. ## Statistical analysis Continual variables were presented as means and standard deviation (SD) or median (interquartile ranges [IQR]) and were compared by one-way ANOVA or Kruskal–Wallis H test. Categorical variables were reported as frequency (percentage) and compared by chi-square or Fisher exact test. Person-years was calculated from baseline to the date of death, loss to follow-up, or the end of follow-up (31 March 2021), whichever came first. Cox proportional hazards regression models were used to calculate adjusted hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for glucose/GA ratio quartiles and mortality. We used three models progressively adjusted for confounders known to influence the prognosis of ACS. Model 1 was adjusted for age and sex. Model 2 included Model 1 variables plus BMI, smoking status, diabetes, hypertension, dyslipidemia, previous myocardial infarction, previous PCI, previous stroke, and AMI. Model 3 included Model 2 variables plus left main coronary artery or three‑vessel disease, eGFR, SBP, heart rate, LVEF < $50\%$, hs-CRP, albumin, hemoglobin, ACEI/ARB at discharge, and β-blocker at discharge. Adjusted survival curves were performed based on the multivariable Cox regression (Model 3) for describing all-cause and cardiovascular mortality according to the glucose/GA ratio categories [27]. We additionally utilized restricted cubic splines based on Cox models to depict detailed descriptions of the dose–response curves between glucose/GA ratio, all-cause mortality, and cardiovascular mortality [28]. The restricted cubic splines were fitted with 3 knots placed at the 10th, 50th, and 90th percentiles across the range of glucose/GA ratios. Wald tests were used to evaluate the statistical significance (at the 0.05 level) of the overall association and for the nonlinearity of the risk curves. To explore the joint effects of diabetes and glucose/GA ratio in predicting event rates, we determined the incidence rate within each subgroup defined by glucose/GA ratio categories and diabetes status (with or without). Subgroup analyses were conducted to evaluate the association between glucose/GA ratio and mortality according to diabetes status (with or without), age group (< 65 years or ≥ 65 years), sex (male or female), BMI group (< 25 kg/m2 or ≥ 25 kg/m2), and ACS status (UA or AMI). The P values for interactions between categories of glucose/GA ratio and diabetes status, age, sex, BMI, or ACS status for the association of outcomes were also estimated using the Wald χ2 test by adding an interaction term (i.e., glucose/GA ratio × diabetes status) in the multivariable models. Finally, we did a sensitivity analysis using E-values to assess how strongly associated unmeasured confounders would need to be with exposure and outcome to potentially fully explain observed non-null association [29]. The data analysis was performed using Stata software, version 17.0 (StataCorp LP, College Station, TX, USA), and R software, version 4.1.2 (R Foundation for Statistical Computing). A two-sided P-value < 0.05 was statistically significant. ## Results A total of 5190 patients were analyzed in the present study with a mean age of 63.4 years, with male predominance ($71.6\%$), and $45.0\%$ presented with AMI. The median glucose/GA ratio was 0.384 (IQR, 0.333–0.442). Table 1 shows patient characteristics stratified by the glucose/GA quartiles. Patients with higher glucose/GA ratio tended to be younger, male and current smokers. Besides, a higher proportion of dyslipidemia and STEMI and higher levels of FPG, hemoglobin, eGFR, hs-CRP, peak cTnI, triglyceride, TC, and LDL-C were observed in this group. Table 1Baseline characteristics by quartiles of glucose/GA ratioOveralln = 5190Q1$$n = 1311$$Q2n = 1285Q3n = 1305Q4n = 1289F/χ2 valueP valueClinical characteristics Age, year63.4 ± 10.866.5 ± 10.363.9 ± 10.362.5 ± 10.660.6 ± 10.972.84 < 0.001 Male, n (%)3714 (71.6)871 (66.4)928 (72.2)949 (72.7)966 (74.9)25.28 < 0.001 BMI, kg/m225.9 ± 3.424.9 ± 3.525.6 ± 3.226.4 ± 3.326.5 ± 3.562.73 < 0.001 Heart rate, bpm71.9 ± 12.770.0 ± 11.770.5 ± 11.572.6 ± 12.874.7 ± 14.138.08 < 0.001 SBP, mmHg130.5 ± 19.4131.6 ± 19.4129.0 ± 18.6131.2 ± 18.7129.9 ± 20.84.920.002 DBP, mmHg75.5 ± 11.974.7 ± 11.375.3 ± 11.876.4 ± 11.875.5 ± 12.64.920.002 Diabetes, n (%)2394 (46.1)612 (46.7)455 (35.4)536 (41.1)791 (61.4)193.43 < 0.001 Hypertension, n (%)3497 (67.4)906 (69.1)883 (68.7)883 (67.7)825 (64.0)9.560.023 Dyslipidemia, n (%)4424 (85.2)1064 (81.2)1090 (84.8)1129 (86.5)1141 (88.5)30.22 < 0.001 Previous MI, n (%)468 (9.0)142 (10.8)108 (8.4)108 (8.3)110 (8.5)7.090.069 Previous stroke, n (%)802 (15.5)251 (19.1)187 (14.6)197 (15.1)167 (13.0)20.76 < 0.001 Previous PCI, n (%)687 (13.2)198 (15.1)170 (13.2)157 (12.0)162 (12.6)6.130.11 Current smoker, n (%)2185 (42.1)410 (31.3)548 (42.6)577 (44.2)650 (50.4)103.91 < 0.001 LVEF, %63.0 ± 8.663.8 ± 8.764.1 ± 7.663.3 ± 8.460.7 ± 9.141.85 < 0.001ACS status, n (%)346.94 < 0.001 UA2855 (55.0)874 (66.7)752 (58.5)760 (58.2)469 (36.4) NSTEMI1015 (19.6)247 (18.8)258 (20.1)242 (18.5)268 (20.8) STEMI1320 (25.4)190 (14.5)275 (21.4)303 (23.2)552 (42.8)Laboratory examinations FPG, mmol/L5.6 (4.9, 7.0)4.9 (4.5, 5.8)5.3 (4.8, 6.2)5.7 (5.1, 6.8)7.2 (5.9, 9.8)1303.44 < 0.001 GA, %15.0 (13.1, 18.4)16.9 (15.0, 20.6)14.7 (13.4, 17.1)13.8 (12.4, 16.5)13.9 (11.9, 18.3)593.95 < 0.001 Hemoglobin, g/L136.4 ± 16.7131.8 ± 16.1135.8 ± 16.5137.8 ± 16.1140.1 ± 16.959.47 < 0.001 eGFR, mL/min/1.73m2113.5 ± 29.4109.5 ± 28.9112.7 ± 28.6115.6 ± 28.1116.3 ± 31.214.04 < 0.001 hs-CRP, mg/L2.3 (0.9, 7.6)1.7 (0.7, 5.8)2.0 (0.8, 7.1)2.4 (0.9, 7.4)3.4 (1.3, 10.8)126.72 < 0.001 Peak cTnI, ng/mL2.3 (0.1, 12.4)0.5 (0.0, 3.6)1.2 (0.0, 8.4)2.8 (0.1, 10.8)6.9 (0.8, 27.0)215.03 < 0.001 Peak NT-proBNP, pg/mL387.5 (112.0, 1454.0)339.0 (106.0, 1348.0)323.0 (100.0, 1125.0)284.0 (95.7, 1179.0)719.0 (153.0, 2119.0)87.79 < 0.001 Triglyceride, mmol/L1.4 (1.1, 2.0)1.2 (0.9, 1.7)1.4 (1.1, 1.9)1.5 (1.1, 2.1)1.7 (1.2, 2.5)321.10 < 0.001 Total cholesterol, mmol/L4.4 ± 1.14.2 ± 1.04.3 ± 1.04.5 ± 1.14.7 ± 1.247.05 < 0.001 LDL-C, mmol/L2.6 ± 0.82.4 ± 0.72.5 ± 0.72.6 ± 0.82.7 ± 0.843.84 < 0.001 HDL-C, mmol/L1.0 ± 0.31.1 ± 0.21.0 ± 0.31.0 ± 0.21.0 ± 0.30.820.48In-hospital treatment, n (%) Aspirin5055 (97.4)1271 (96.9)1255 (97.7)1278 (97.9)1251 (97.1)3.480.32 Clopidogrel/Ticagrelor4840 (93.3)1211 (92.4)1202 (93.5)1223 (93.7)1204 (93.4)2.280.52 β-Blocker3693 (71.2)900 (68.6)874 (68.0)939 (72.0)980 (76.0)25.5 < 0.001 ACEI/ARB3013 (58.1)745 (56.8)725 (56.4)753 (57.7)790 (61.3)7.820.050 Statins4761 (91.7)1216 (92.8)1182 (92.0)1205 (92.3)1158 (89.8)8.650.034Angiographic data, n (%) LM lesion545 (10.5)164 (12.5)136 (10.6)131 (10.0)114 (8.8)9.700.021 Multi-vessel lesion4204 (81.0)1040 (79.3)1034 (80.5)1062 (81.4)1068 (82.9)5.620.13 Chronic total occlusion lesion1791 (34.5)354 (27.0)405 (31.5)424 (32.5)608 (47.2)131.53 < 0.001Target vessel territory, n (%) LM172 (3.3)54 (4.1)48 (3.7)44 (3.4)26 (2.0)10.140.017 LAD2569 (49.5)653 (49.8)626 (48.7)646 (49.5)644 (50.0)0.480.92 LCX999 (19.2)256 (19.5)251 (19.5)260 (19.9)232 (18.0)1.810.61 RCA1576 (30.4)386 (29.4)396 (30.8)391 (30.0)403 (31.3)1.240.74Hypoglycemic agents, n (%) Metformin705 (13.6)173 (13.2)133 (10.4)181 (13.9)218 (16.9)23.87 < 0.001 Alpha-glucosidase inhibitor1202 (23.2)277 (21.1)230 (17.9)286 (21.9)409 (31.7)77.36 < 0.001 Sulfonylurea420 (8.1)99 (7.6)81 (6.3)96 (7.4)144 (11.2)23.43 < 0.001 DPP-4i33 (0.6)9 (0.7)8 (0.6)6 (0.5)10 (0.8)1.100.78 Insulin510 (9.8)163 (12.4)88 (6.8)93 (7.1)166 (12.9)47.20 < 0.001Values are mean ± SD, n (%), or median (interquartile range)ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; cTnI, cardiac troponin I; DBP, diastolic blood pressure; DPP-4i, dipeptidyl peptidase-4 inhibitors; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; GA, glycated albumin; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high sensitivity C-reactive protein; LAD, left anterior descending artery; LCX, left circumflex artery; LDL-C, low-density lipoprotein cholesterol; LM, left main coronary artery; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NSTEMI, non-ST-segment elevation myocardial infarction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; PCI, percutaneous coronary intervention; RCA, right coronary artery; SBP, systolic blood pressure; STEMI, ST-segment elevation myocardial infarction; UA, unstable anginaQuartiles of glucose/GA ratio, Q1 < 0.334, Q2 = 0.334–0.384, Q3 = 0.385–0.442, Q4 > 0.442 During a median follow-up of 4.0 years (IQR 1.1–5.1 years), the number of all-cause deaths and cardiovascular-associated deaths was 313 ($6.0\%$) and 177 ($3.4\%$), respectively. Associations between glucose/GA ratio quartiles and all-cause and cardiovascular mortality are shown in Table 2 and Fig. 2. After adjustment for potential confounders, the risk of all-cause mortality increased in the lowest and highest glucose/GA ratio quartiles, exhibiting a U-shaped relationship between glucose/GA ratio and all-cause mortality. For patients with glucose/GA ratio level < 0.334 (Q1), the fully adjusted HR for all-cause mortality was 1.43 ($95\%$ CI, 1.01–2.03) compared to Q2. For patients with glucose/GA ratio level > 0.442 (Q4), the adjusted HR for all-cause mortality was 1.51 ($95\%$ CI, 1.03–2.21). Similarly, the incidence rate for cardiovascular mortality increased in patients with the lowest and highest glucose/GA ratio quartiles. After adjusting for covariates in model 3, only the highest glucose/GA ratio level was associated with increased cardiovascular mortality (adjusted HR, 1.69; $95\%$ CI, 1.02–2.79) (Table 2 and Fig. 2). Covariates-adjusted survival curves of time until all-cause and cardiovascular death are shown in Fig. 3. Patients in the lowest and highest glucose/GA ratio groups were associated with higher mortality during follow-up. Table 2Association between glucose/GA ratio and all-cause and cardiovascular mortalityOutcomeCases, NoIncidence Rate, per 1000 Person-YearsHR ($95\%$ CI)Model 1 aModel 2 bModel 3 cAll-cause mortalityGlucose/GA ratio, quartilesd Q110724.11.63 (1.17–2.25)1.54 (1.10–2.14)1.43 (1.01–2.03) Q25512.21 [Reference]1 [Reference]1 [Reference] Q37115.21.40 (0.98–1.98)1.44 (1.01–2.06)1.33 (0.91–1.95) Q48018.01.86 (1.32–2.63)1.60 (1.11–2.29)1.51 (1.03–2.21)Cardiovascular mortalityGlucose/GA ratio, quartiles Q16013.51.77 (1.13–2.78)1.69 (1.07–2.69)1.51 (0.94–2.43) Q2286.21 [Reference]1 [Reference]1 [Reference] Q3388.11.48 (0.91–2.41)1.52 (0.92–2.50)1.41 (0.84–2.35) Q45111.52.34 (1.47–3.72)1.97 (1.21–3.20)1.69 (1.02–2.79)CI, confidence interval; GA, glycated albumin; and HR, hazard ratioa Model 1 was adjusted for age and sexb Model 2 was adjusted as model 1 plus BMI, smoking status, diabetes, hypertension, dyslipidemia, previous myocardial infarction, previous PCI, previous stroke, and AMIc Model 3 was adjusted as model 2 plus left main coronary artery or three‑vessel disease, eGFR, SBP, heart rate, LVEF < $50\%$, hs-CRP, albumin, hemoglobin, ACEI/ARB at discharge, and β-blocker at discharged Quartiles of glucose/GA ratio, Q1 < 0.334, Q2 = 0.334–0.384, Q3 = 0.385–0.442, Q4 > 0.442Fig. 2Adjusted hazard ratios for all-cause mortality and cardiovascular mortality according to glucose/GA ratio categories. Model 1 included age and sex. Model 2 included Model 1 variables plus BMI, smoking status, diabetes, hypertension, dyslipidemia, previous myocardial infarction, previous PCI, previous stroke, and AMI. Model 3 included Model 2 variables plus left main coronary artery or three‑vessel disease, eGFR, SBP, heart rate, LVEF < $50\%$, hs-CRP, albumin, hemoglobin, ACEI/ARB at discharge, and β-blocker at dischargeFig. 3Adjusted Kaplan–Meier curves for all-cause mortality (A) and cardiovascular mortality (B) according to the glucose/GA ratio categories The dose–response relationships between glucose/GA ratio level and all-cause and cardiovascular mortality using restricted cubic splines are shown in Fig. 4. The association between glucose/GA ratio and all-cause mortality was U-shaped after adjusting for variables in Model 3 (Pnonlinear = 0.008). Similar results were observed for the association between glucose/GA ratio and cardiovascular mortality (Pnonlinear = 0.028).Fig. 4Restricted cubic spline analysis for association between the glucose/GA ratio and all-cause mortality or cardiovascular mortality with the adjustment of covariates in Model 1 (A and C) or Model 3 (B and D). Model 1 included age and sex. Model 3 included Model 1 variables plus BMI, smoking status, diabetes, hypertension, dyslipidemia, previous myocardial infarction, previous PCI, previous stroke, AMI, left main coronary artery or three‑vessel disease, eGFR, SBP, heart rate, LVEF < $50\%$, hs-CRP, albumin, hemoglobin, ACEI/ARB at discharge, and β-blocker at discharge We subsequently explored the effect modification of diabetes status on the association between glucose/GA ratio and mortality in subgroup analyses. The joint association of diabetes status and glucose/GA ratio quartiles with all-cause and cardiovascular mortality is depicted in Fig. 5. In patients with diabetes, the all-cause mortality rate was highest in the Q1 group (25.6; $95\%$ CI, 19.4–$\frac{33.7}{1000}$ person-years), and followed by the Q4 group (21.5; $95\%$ CI, 16.7–$\frac{27.8}{1000}$ person-years). In patients without diabetes, the all-cause mortality rate was higher in Q1 (23.0; $95\%$ CI, 17.7–$\frac{29.8}{1000}$ person-years) and Q2 (15.4; $95\%$ CI, 11.5–$\frac{20.6}{1000}$ person-years) groups. Similar trends were observed when considering cardiovascular mortality (Fig. 5). In the multivariate Cox analysis, higher mortality risk was observed in the lowest and highest glucose/GA ratio groups for patients with diabetes (Table 3). Compared with patients in the Q2 group, the multivariable-adjusted HR for all-cause mortality was 3.19 ($95\%$ CI, 1.55–6.56), 3.04 ($95\%$ CI, 1.44–6.42) and 3.36 ($95\%$ CI, 1.64–6.91) for patients in the Q1, Q3, and Q4 groups. Similarly, the corresponding multivariable-adjusted HR ($95\%$ CI) for cardiovascular mortality was 4.18 (1.61–10.88), 3.46 (1.28–9.36), and 3.91 (1.50–10.17) for patients in the Q1, Q3, and Q4 group, respectively. In contrast, this association was non-significant for patients without diabetes (Table 3). Moreover, there was a significant interaction between glucose/GA ratio and diabetes status for all-cause mortality (P for interaction = 0.038), but not for cardiovascular mortality (P for interaction = 0.061).Fig. 5Incidence rates per 1000 person-years of all-cause mortality and cardiovascular mortality according to the combination of glucose/GA ratio quartiles and diabetes statusTable 3Association between glucose/GA ratio and all-cause and cardiovascular mortality according to diabetes statusOutcomeDiabetes ($$n = 2394$$)No diabetes ($$n = 2796$$)Cases, NoIncidence RateHR ($95\%$ CI)Cases, NoIncidence RateHR ($95\%$ CI)Model 1 aModel 2 bModel 3 cModel 1 aModel 2 bModel 3 cAll-cause mortalityGlucose/GA ratio, quartilesd Q15025.63.49 (1.77–6.89)3.30 (1.67–6.52)3.19 (1.55–6.56)5723.01.18 (0.79–1.74)1.11 (0.74–1.68)1.10 (0.71–1.70) Q2106.41 [Reference]1 [Reference]1 [Reference]4515.41 [Reference]1 [Reference]1 [Reference] Q33519.63.22 (1.59–6.50)3.14 (1.55–6.38)3.04 (1.44–6.42)3612.40.96 (0.62–1.49)1.04 (0.67–1.62)0.98 (0.61–1.58) Q45921.53.82 (1.95–7.47)3.37 (1.70–6.65)3.36 (1.64–6.91)2112.31.24 (0.73–2.09)1.14 (0.67–1.94)1.11 (0.64–1.94)Cardiovascular mortalityGlucose/GA ratio, quartiles Q13316.94.58 (1.79–11.75)4.40 (1.71–11.30)4.18 (1.61–10.88)2710.91.08 (0.61–1.88)1.02 (0.57–1.83)0.87 (0.46–1.61) Q253.21 [Reference]1 [Reference]1 [Reference]237.91 [Reference]1 [Reference]1 [Reference] Q32011.23.68 (1.38–9.80)3.51 (1.31–9.43)3.46 (1.28–9.36)186.20.95 (0.51–1.76)1.09 (0.58–2.04)1.01 (0.52–1.97) Q43813.94.96 (1.95–12.62)4.30 (1.67–11.04)3.91 (1.50–10.17)137.61.52 (0.77–3.03)1.51 (0.74–3.07)1.32 (0.63–2.75)CI, confidence interval; GA, glycated albumin; and HR, hazard ratioper 1000 person-yearsaModel 1 was adjusted for age and sexbModel 2 was adjusted as model 1 plus BMI, smoking status, hypertension, dyslipidemia, previous myocardial infarction, previous PCI, previous stroke, and AMIcModel 3 was adjusted as model 2 plus left main coronary artery or three‑vessel disease, eGFR, SBP, heart rate, LVEF < $50\%$, hs-CRP, albumin, hemoglobin, ACEI/ARB at discharge, and β-blocker at dischargedQuartiles of glucose/GA ratio, Q1 < 0.334, Q2 = 0.334–0.384, Q3 = 0.385–0.442, Q4 > 0.442P for interaction between glucose/GA ratio and diabetes group for all-cause mortality was 0.023 in Model 1, 0.033 in Model 2, and 0.038 in Model 3P for interaction between glucose/GA ratio and diabetes group for cardiovascular mortality was 0.075 in Model 1, 0.088 in Model 2, and 0.061 in Model 3 Subgroup analyses were performed for all-cause mortality by the following variables: age, sex, BMI, and ACS status (Additional file 1: Figure S2–S5). The U-shaped relationship between glucose/GA ratio and all-cause mortality was more pronounced among patients aged < 65 years, females, and patients with BMI < 25 kg/m2. However, there was no significant interaction between the glucose/GA ratio and each subgroup for all-cause mortality (all P for interaction > 0.05). In sensitivity analysis, the estimated E-value for the association between glucose/GA ratio and mortality based on the fully adjusted model is shown in Additional file 1: Figures S6 and S7. For all-cause mortality, the E-values were 2.21 and 2.39 for glucose/GA ratio level < 0.334 (Q1) and > 0.442 (Q4) compared to Q2 (Additional file 1: Figure S6). Similarly, the E-values for cardiovascular mortality were 2.39 and 2.77 accordingly (Additional file 1: Figure S7). ## Discussion In this large, prospective cohort study of patients with ACS undergoing PCI, we provided preliminary evidence that low and high glucose/GA ratio levels were associated with a higher mortality risk, exhibiting a U-shaped relationship. Interestingly, this relationship varied by diabetes status, with a significant association between glucose/GA ratio and mortality in patients with diabetes compared with those without diabetes. ## Stress hyperglycemia in patients with ACS Stress hyperglycemia has been documented as a strong predictor of adverse outcomes in patients with ACS. It has been reported that stress hyperglycemia is significantly associated with major adverse cardiovascular and cerebrovascular events, irrespective of the diabetes status in STEMI patients undergoing PCI [5]. In addition, stress hyperglycemia ratio (SHR), representing relative hyperglycemia using the ratio of the admission blood glucose to estimated chronic blood glucose, is significantly related to in-hospital mortality in patients with coronary artery disease, especially for those with prediabetes and diabetes [30]. Moreover, it has been reported that, unlike the admission blood glucose, the SHR is an independent predictor of in-hospital mortality after AMI and improves the predictability of prognostic models containing the Global Registry of Acute Coronary Events (GRACE) score [6]. Similarly, Luo et al. reported that adding SHR to the GRACE score significantly improved its post-MI risk stratification performance among patients with diabetes [31]. These studies suggested a strong prognostic value of stress hyperglycemia, which may help identify ACS patients with a higher risk of subsequent adverse outcomes. The present study used a novel index of relative hyperglycemia, the glucose/GA ratio, and evaluated the association between glucose/GA ratio and mortality in ACS patients that underwent PCI. ## Dose–response relationship between hyperglycemia and outcomes Overwhelming evidence substantiates that a higher level of stress hyperglycemia is significantly associated with adverse outcomes [4–7]. However, most of these studies were conducted without exploring the potential differences among the lower groups of relative hyperglycemia index and ignored the nonlinear relationship. The present study assessed a novel index of relative hyperglycemia, indicating a U-shaped relationship with low and high glucose/GA ratio levels associated with a higher mortality risk, especially in diabetes patients. Another study of 5562 patients with ACS who underwent PCI reported the U-shaped or J-shaped association between SHR and early and late poor prognosis using restricted cubic splines analyses [8]. Notably, the U-shaped or J-shaped association was not significant in the subgroup of patients without diabetes, consistent with our findings [8]. Moreover, Zhou et al. found a U-shaped association between SHR and in-hospital cardiac, kidney, and infectious adverse events in non-surgical hospitalized patients with type 2 diabetes and heart failure [32]. In addition, one study consisting of 3750 AMI patients admitted to 35 hospitals in Japan found that severe hyperglycemia (glucose ≥ 11 mmol/L) and euglycemia (glucose < 7 mmol/L) was associated with higher mortality compared to moderate hyperglycemia (glucose 9 to 11 mmol/L) in patients with a history of diabetes. On the contrary, this relationship was linear in non-diabetic patients, with glucose levels < 6 mmol/L associated with the lowest mortality [9]. It has long been thought that stress hyperglycemia, as an index of disease severity, enables quantification of the degree of acute illness and has a prognostic value. A positive relationship between hyperglycemia and infarct size, reduced LVEF, the severity of microvascular obstruction, and the use of an intra-aortic balloon pump were reported in the AMI population [33–35]. In addition, Goyal et al. documented that a more substantial drop in glucose in the first 24 h after AMI was associated with decreased mortality, which has potential implications for a cause-and-effect relationship between hyperglycemia and increased mortality [36]. However, the U-shaped phenomenon may provoke confusion and challenge the blood glucose management strategy. ## Mechanisms and implications The mechanisms underlying the U-shaped association between stress hyperglycemia and outcomes in ACS patients remain unknown. It has been reported that mild-to-moderate stress hyperglycemia might play a protective role during the acute phase, especially for ischemia. Following reduced blood flow during ischemia, moderate hyperglycemia (blood glucose of 140 to 220 mg/dL) results in a new glucose balance with a higher blood ‘glucose diffusion gradient’ conducive to maximum cellular glucose uptake [37, 38]. In addition, hyperglycemia may reduce infarct size and improve systolic function by increasing cell survival factors (hypoxia-inducible factor-1α, vascular endothelial growth factor) and decreasing apoptosis [39]. Besides, the diabetic status might modulate this relationship in clinically important ways. Consistent with previous studies [8, 9], the U-shaped relationship between the hyperglycemia index (as glucose/GA ratio in the present study) and mortality was more significant in patients with diabetes. A study involving 44,964 patients admitted to intensive care units suggested that patients with diabetes may benefit from higher glucose target ranges than those without [40]. However, it should be borne in mind that severe stress hyperglycemia may still be harmful. In our study, we found that the inflection points of the glucose/GA ratio for the whole cohort was approximately 0.35, and higher values indicated an elevated risk of mortality. Since the blood glucose level is usually higher in patients with diabetes before ACS, the threshold glucose level associated with deleterious effects might be raised. Indeed, more prospective cohort studies are needed to determine the threshold of stress hyperglycemia, and a more stratified glycemic target should be applied according to the glucose/GA ratio value. ## Strengths and limitations The strengths of this study included a large sample size, a long follow-up period, and the glucose/GA ratio measures to assess relative acute rises in plasma glucose level compared with premorbid glucose status. To our knowledge, this is the first study to confirm the prognostic value of the glucose/GA ratio in ACS patients who underwent PCI. However, several limitations need to be addressed. First, this was a single-center cohort study in the Chinese population, which limits the generalization of our findings. Besides, given the observational nature of the cohort studies, only the association between the glucose/GA ratio and the outcome was determined rather than a causal relationship. The presence of residual or unmeasured confounding factors, such as the symptom onset to balloon time, could not be entirely ruled out. In addition, FPG and GA levels were only measured once after admission, which could cause potential bias. Additional prospective and mechanistic cohort studies are required to further validate our findings. ## Conclusion Both low and high glucose/GA ratio values were associated with an increased all-cause and cardiovascular mortality, exhibiting a U-shaped relationship. 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--- title: CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients authors: - Ziting Lan - Xiaoying Ding - Yarong Yu - Lihua Yu - Wenli Yang - Xu Dai - Runjianya Ling - Yufan Wang - Wenyi Yang - Jiayin Zhang journal: Cardiovascular Diabetology year: 2023 pmcid: PMC10032006 doi: 10.1186/s12933-023-01801-y license: CC BY 4.0 --- # CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients ## Abstract ### Objectives To investigate the prognostic value of computed tomography fractional flow reserve (CT-FFR) in patients with diabetes and to establish a risk stratification model for major adverse cardiac event (MACE). ### Methods Diabetic patients with intermediate pre-test probability of coronary artery disease were prospectively enrolled. All patients were referred for coronary computed tomography angiography and followed up for at least 2 years. In the training cohort comprising of 957 patients, two models were developed: model1 with the inclusion of clinical and conventional imaging parameters, model2 incorporating the above parameters + CT-FFR. An internal validation cohort comprising 411 patients and an independent external test cohort of 429 patients were used to validate the proposed models. ### Results 1797 patients (mean age: 61.0 ± 7.0 years, 1031 males) were finally included in the present study. MACE occurred in $7.18\%$ ($\frac{129}{1797}$) of the current cohort during follow- up. Multivariate Cox regression analysis revealed that CT-FFR ≤ 0.80 (hazard ratio [HR] = 4.534, $p \leq 0.001$), HbA1c (HR = 1.142, $$p \leq 0.015$$) and low attenuation plaque (LAP) (HR = 3.973, $$p \leq 0.041$$) were the independent predictors for MACE. In the training cohort, the Log-likelihood test showed statistical significance between model1 and model2 ($p \leq 0.001$). The C-index of model2 was significantly larger than that of model1 (C-index = 0.82 [0.77–0.87] vs. 0.80 [0.75–0.85], $$p \leq 0.021$$). Similar findings were found in internal validation and external test cohorts. ### Conclusion CT-FFR was a strong independent predictor for MACE in diabetic cohort. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12933-023-01801-y. ## Introduction According to World Health Organization, the prevalence of diabetes have increased exponentially worldwide over the past few decades, from 108 million ($4.7\%$) in 1980 to 425 million ($8.5\%$) in 2017 [1]. Cardiovascular disease is one of the common complications of diabetes, including coronary artery disease (CAD), heart failure, arrhythmia and sudden cardiac death [1]. Compared with non-diabetic patients, diabetes shows a higher incidence of coronary atherosclerosis and greater probability of obstructive CAD [2, 3]. It is of clinical significance for precise risk stratification in diabetic patients to guide proper treatment strategy and therefore improve prognosis. For invasive test, clinical evidence regarding the risk stratification in CAD patients with diabetes has been cumulated based on the plaque imaging by optical coherence tomography (OCT) or coronary microvascular function evaluated by pressure guidewire. In COMBINE OCT-FFR study, the thin-cap fibroatheroma detected by OCT was a strong predictor of major adverse clinical events (MACE) [4]. Meanwhile, microvascular dysfunction confirmed by index of microcirculatory resistance was also an independent predictor of MACE among diabetic patients with suspected CAD [5]. However, the invasiveness and high medical cost of the above tests significantly limit their clinical application in the general diabetic population. For non-invasive imaging, coronary computed tomography angiography (CCTA) is recommended as the first-line test for CAD diagnosis [6, 7], which has high negative predictive value to safely rule out obstructive CAD. In addition to stenosis evaluation, CCTA is able to characterize high-risk plaque (HRP) features, such as low attenuation plaque (LAP), positive remodeling (PR), spotty calcification (SC) and napkin-ring sign (NRS) [8–10]. According to previous studies, higher stenotic grade as assessed by Coronary Artery Disease Reporting and Data System (CAD-RADS) [11] as well as the presence of HRP features are associated with poor prognosis [12, 13]. However, conventional CCTA data lacks functional evaluation regarding hemodynamic significance of coronary stenosis, which has become increasingly important in the diagnosis and treatment of CAD patients with diabetes over the years [14, 15]. Computed tomography angiography-derived fractional flow reserve (CT-FFR) is a non-invasive physiological test that enables functional assessment of flow-limiting stenosis based on CCTA data [16, 17]. This novel method is able to guide optimal treatment strategy with reduced unnecessary invasive procedures [18]. In multiple studies enrolling patients with suspected CAD, subjects with lesion-specific CT-FFR value > 0.8 have better prognosis than those with lesion-specific CT-FFR value ≦ 0.8 [19, 20]. However, there is a lack of evidence on the prognostic value of this promising approach in diabetic patients, which has high incidence of hemodynamic significant CAD as revealed by CT-FFR. In light of the above findings, we hypothesized that CT-FFR might be a strong independent predictor for MACE in diabetic patients and has incremental value over other clinical and imaging parameters for risk stratification of diabetes. Thus, the aims of this study were to investigate the prognostic value of CT-FFR in patients with diabetes and to establish a risk stratification model for MACE by combining clinical risk factors, CT-FFR and HRP features. ## Study population The hospital ethic committee approved this post-hoc analysis of a prospective cohort and all patients gave informed consents. We consecutively enrolled diabetic patients with intermediate pre-test probability of CAD (defined as pre-test probability between 15 and $85\%$ according to updated Diamond–Forrester score [21]) from two hospitals from January, 2016 to December, 2019. All patients were referred for CCTA and followed up for at least 2 years. The exclusion criteria were: [1] severe renal dysfunction or allergic to CT contrast medium; [2] severe aortic stenosis or pulmonary hypertension; [3] any conditions that causing hemodynamic instability; [4] patients with history of coronary revascularization or myocardial infarction; [5] patients with non-ischemic cardiomyopathy disease or valvular disease; [6] impaired image quality of CCTA (insufficient to perform CT-FFR or plaque analysis); [7] patients underwent early revascularization (within 3 months after baseline CCTA) for lesions revealed by index CCTA; [8] lost clinical follow-up. Participants in this study were divided into three separate cohorts: a training cohort, an internal validation cohort and an external test cohort. Specifically, patients from one hospital were randomly assigned to either the training cohort or the internal validation cohort at a 7:3 ratio. The external test cohort consisted of patients from another hospital. ## CCTA acquisition A third-generation dual source CT (SOMATOM Force, Siemens Healthineers, Germany) or a 256-slice wide detector CT scanner (Revolution HD, GE Healthcare, USA) was used for scanning. Coronary Agatston calcium score (CACS) was firstly performed to assess the overall calcification burden of coronary vasculature. For CCTA acquisition, prospective ECG triggered technique was employed in all patients, covering $35\%$–$75\%$ of the R–R interval. Automated tube voltage and current modulation (CAREKv, CAREDose 4D, Siemens Healthineers, Germany; or KV Assist, Smart mA, GE Healthcare, USA) was applied to reduce radiation exposure. More details regarding acquisition parameters are given in the online appendix. ## CCTA-based plaque analysis All CCTA data were transferred to an offline workstation (syngo.via, version VB20A, Siemens Healthineers, Germany) and the images with best quality were selected for manual diameter stenosis (DS) quantification. DS was defined as (reference diameter – minimal lumen diameter) / reference diameter. Patient-based stenosis severity was assessed according to the Coronary Artery Disease-Reporting and Data System (CAD-RADS) [11] while patients with CAD-RADS grade 3 or higher were considered having obstructive CAD. Further plaque analysis was performed using a research software (Coronary Plaque Analysis, version 2.0, Siemens Healthineers, Germany), which allows semi-automatic plaque quantification [22, 23]. For all atherosclerotic lesions, four HRP features were characterized according to the following definitions: [1] PR, defined as any lesion with a remodeling index ≥ 1.1; [2] LAP, defined as any voxel < 30 HU within a coronary plaque; [3] SC, defined by an intra-plaque calcium < 3 mm in length that comprises < 90 degrees of the lesion circumference; [4] NRS, defined as a plaque core with low CT attenuation surrounded by a rim-like area of hyper-density [8–10, 24]. Any lesion with 2 or more HRP features were considered vulnerable plaque [25]. Two cardiovascular radiologists (with 3-years and 12-years experience of cardiac imaging), who were blinded to the clinical history and outcomes, independently analyzed the lesions. Any disagreement between two observers were resolved by consensus. ## CT-FFR simulation CT-FFR simulation was performed on a research software package (Cta-Plus; version 2.0, Pulse Medical Imaging Technology, China) based on quantitative flow ratio (CT-QFR) technology. The diagnostic performance of this novel algorithm has been validated in previous studies using invasive FFR as the reference standard [26, 27]. The details regarding the computation and how onsite processing was performed are provided in the online appendix. Lesion-specific CT-FFR value was measured 1–2 cm distal to the lesion for all coronary stenosis on major epicardial vessels with diameter ≥ 2 mm [28]. Vessel-specific CT-FFR was defined as the CT-FFR value for the most distal lesion. For vessels without significant stenosis, the CT-FFR value was recorded at the most distal site where vessel diameter was ≥ 2 mm. The lowest vessel-specific CT-FFR value of major epicardial arteries was used for patient-based analysis and the presence of any vessel-specific CT-FFR ≤ 0.80 was considered hemodynamically significant. Two cardiovascular radiologists (with 3-years and 12-years experience of cardiac imaging), who were blinded to the clinical history and outcomes, independently analyzed the lesions. The mean CT-FFR value of measurement by two observers was recorded for further analysis. ## Clinical follow-up and study endpoints All enrolled patients were followed up for at least 2 years, or until the occurrence of MACE. Patients were followed-up every 6 months via outpatient visits. MACE was defined as all-cause mortality, cardiac death, non-fatal myocardial infarction, late revascularization (occurred three months after index CCTA), and rehospitalization due to heart failure or aggravated angina. The primary endpoint of this study was to determine the predictive value of CT-FFR for MACE in patients with diabetes. The secondary endpoint was to establish a risk stratification model for MACE by combining clinical risk factors, CT-FFR and HRP features. ## Statistical analysis Continuous data were presented as mean ± standard deviation (SD) or median and interquartile range (IQR) depending on whether it conformed to a normal distribution, which was tested with Kolmogorov–Smirnov test. Categorical data were presented as absolute frequencies and proportions. Student's t-test or Mann–Whitney U-test (for two groups or ANOVA (for three groups) was used for continuous variables. Chi-square test or Fisher's exact test was used to compare the frequency distribution of categorical and binary data between subgroups, according to the size of data cells. Inter-observer and intra-observer agreement of CTA-derived parameters was assessed by intra-class correlation coefficient (ICC). Cumulative incidence rates of MACE were estimated using Kaplan–Meier method and compared with the log-rank test, and using the following cut-off values: presence of HRP, CAD-RADS ≧ 3, and CT-FFR ≦ 0.8, whereas CACS groups were reclassified as CACS 0, CACS of 0 to less than 100, CACS of 100–400, and CACS greater than 400. In the training cohort, univariate and multivariate Cox proportional hazards regression models were used to analyze the prognostic value of various clinical and imaging parameters for MACE and to identify the independent predictors accordingly. We built the base prediction model (model 1) with the inclusion of selected parameters (obstructive CAD, CACS, HRP, LAP, PR, SC and NRS, which were considered the known risk factors for MACE [8–11]. To demonstrate the incremental predictive value of CT-FFR, we used the nest model to build a new model (model 2) by overlaying CT-FFR on top of the base model. All models were adjusted for predefined sociodemo-graphic variables (age, sex, BMI) and cardiovascular risk factors (hypertension, diabetes, dyslipidemia, current-smoking, fast glucose, HbA1c). Incremental prognostic values of models were compared using Harrell’s C-statistics (C-index), log-likelihood test and time-dependent receiver operator characteristic (ROC) curve analysis. The goodness of fit of models was assessed by the calibration curve with Brier and score the Akaike information criterion (AIC). In addition, decision curve analysis was used to assess the clinical usefulness of the model by quantifying the net benefit at different threshold probabilities. In the training set, a nomogram was developed based on model 2 (incorporating all significant independent predictors as revealed by multivariate Cox regression analysis) and obtained MACE probability estimates. This nomogram was further validated by C-index analysis in three cohorts. The score of each patient according to the nomogram was calculated and the median of the scores in the training cohort was selected as cutoff. It was further validated for the risk stratification performance in two validation cohorts. Kaplan–Meier curves with log-rank test were applied to compare patient survival between different groups. Two-sided $p \leq 0.05$ was considered statistically significant. Statistical analyses were performed using SPSS statistical package (version 26.0, IBM, Armonk, New York, USA) and the R statistical package (version 4.2.1). ## Patient characteristics A total of 2346 diabetic patients with intermediate pre-test probability of CAD were referred for CCTA from January, 2016 to December, 2019 and initially enrolled. Among them, 549 patients were excluded due to pre-specified criteria (Fig. 1). Eventually, there were 1797 subjects (mean age: 61.0 ± 7.0 years, 1031 males) included in the present study. As previously mentioned, patients were subsequently divided into training cohort ($$n = 957$$), internal validation cohort ($$n = 411$$), and external test cohort ($$n = 429$$). More details of demographic characteristics are given in Table 1 and Additional file 1: Table E1.Fig. 1Flow chart of inclusion and exclusion criteria. CAD = coronary artery disease; CCTA = coronary computed tomography angiography;Table 1Demographic dataTraining setInternal validation setp#External test setp($$n = 957$$)($$n = 411$$)($$n = 429$$)Age (years)62.0 [55.0–69.0]61.0 [54.0–69.0]0.72361.0 [54.0–68.0]0.162Males, n (%)536 (56.0)243 (59.1)0.314252 (58.7)0.456BMI, kg/m224.4 [22.5–26.8]24.2 [22.3–26.4]0.12625.0 [22.6–27.3]0.010Course of diabetes (years)10.0 [4.00–16.0]10.0 [3.00–15.0]0.14710.0 [3.00–16.0]0.310Hypertension, n (%)533 (55.7)218 (53.0)0.398242 (56.4)0.571Dyslipidemia, n (%)543 (56.7)249 (60.6)0.207312 (72.7) < 0.001Current smoking, n (%)300 (31.3)122 (29.7)0.584128 (29.8)0.766Fast glucose (mmol/L)7.00 [5.50–9.55]6.80 [5.50–9.49]0.4847.67 [6.00–10.8] < 0.001HbA1c (%)8.40 [7.20–10.0]8.40 [7.10–10.1]0.4638.50 [7.20–10.3]0.525Radiation dose, mSv1.92 [1.24–2.60]1.86 [1.21–2.52]0.3752.49 [1.70–4.85] < 0.001CT-FFR ≤ 0.80, n (%)73 (7.63)41 (9.98)0.18226 (6.06)0.103Obstructive CAD, n (%)286 (29.9)127 (30.9)0.75690 (21.0)0.001CAD-RADS, n (%):0.1910.001 0238 (24.9)96 (23.4)139 (32.4) 1219 (22.9)75 (18.2)101 (23.5) 2214 (22.4)113 (27.5)99 (23.1) 3172 (18.0)75 (18.2)59 (13.8) 4A103 (10.8)43 (10.5)27 (6.29) 4B9 (0.94)8 (1.95)2 (0.47) 52 (0.21)1 (0.24)2 (0.47)CACS0.4150.800 0427 (44.6)167 (40.6)191 (44.5) 0–100343 (35.8)164 (39.9)157 (36.6) 100–400120 (12.5)55 (13.4)54 (12.6) > 40067 (7.00)25 (6.08)27 (6.29)HRP, n (%)272 (28.4)118 (28.7)0.966101 (23.5)0.131LAP, n (%)277 (28.9)120 (29.2)0.97797 (22.6)0.034PR, n (%)505 (52.8)226 (55.0)0.487225 (52.4)0.706SC, n (%)58 (6.06)27 (6.57)0.81423 (5.36)0.759NRS, n (%)96 (10.0)46 (11.2)0.58328 (6.53)0.047Microvascular complications, n (%)694 (72.5)288 (70.1)0.392330 (76.9)0.072MACE, n (%)65 (6.79)35 (8.52)0.31329 (6.76)0.489BMI, body mass index; CACS, Coronary Artery Calcium Scoring; CAD, Coronary artery disease; CAD-RADS, Coronary Artery Disease—Reporting and Data System; CT-FFR, computed tomography fractional flow reserve; HbA1c, hemoglobin A1c; HRP, high-risk plaque; LAP, low-attenuation plaque; MACE, major adverse cardiac events. NRS, napkin-ring sign; PR, positive remodeling; SC, spotty calcificationP#: the p between Training set and Internal validation set, P: the p among the three cohortsValues are mean ± SD, n (%), or median (IQR). SD, standard deviation The dose-length products (DLP) for CCTA were 141.7 mGy × cm (93.6–215.4) mGy × cm and the mean effective dose of radiation for CCTA was 1.98 mSv (1.31–3.02) mSv when using 0.014 as the conversion coefficient. The median amount of contrast agent used for CCTA was 50 mL (45 mL, 55 mL). There were good Intra-observer and Inter-observer agreements in the measurement of all parameters (ICC > 0.75, $p \leq 0.001$ for all) (details shown in Additional file 1: Tables E3, E4). ## Clinical outcomes All patients were followed-up for a median time of 3.14 years (2.58 years, 4.03 years). MACE occurred in $7.18\%$ ($\frac{129}{1797}$) patients. Of these 129 patients who developed MACE, 97 patients were re-hospitalized due to heart failure ($$n = 3$$) or aggravated angina ($$n = 94$$), 18 patients experienced myocardial infarction, 11patients underwent late percutaneous coronary intervention, 3 patients died of cardiac death ($$n = 1$$) or noncardiac death ($$n = 2$$). ## Comparison of clinical and imaging parameters between patients with and without MACE In both internal and external sets, patients with MACE had significantly higher incidence of having CT-FFR ≤ 0.8 or the presence of HRP compared to patients without MACE. Similar findings were also observed for obstructive CAD and higher CACS (Table 2; Fig. 2). Other clinical factors, such as age, gender, diabetes course, fast glucose level and HbA1c level, demonstrated discrepant results between different patient sets (Table 2). In addition, the medication did not show significant difference between patients with and without MACE in the training cohort, whereas more frequent use of some types of antihypertensive agents were noted in the internal and external validation cohorts (Additional file 1: Table E2).Table 2Clinical and imaging characteristics of patients with and without MACETraining setInternal validation setExternal test setMACE (−)MACE (+)p valueMACE (−)MACE (+)p valueMACE (−)MACE (+)p valueN = 892N = 65N = 376N = 35N = 400N = 29Age (years)62.0 [55.0–68.2]65.0 [59.0–71.0]0.01261.0 [54.0–69.0]67.0 [60.5–75.5]0.00261.0 [53.0–68.0]64.0 [59.0–68.0]0.103Males, n (%)493 (55.3)43 (66.2)0.115224 (59.6)19 (54.3)0.668235 (58.8)17 (58.6)1.000BMI, kg/m224.4 [22.5–26.8]24.9 [22.7–26.7]0.76424.2 [22.3–26.5]24.5 [22.2–25.8]0.83325.0 [22.7–27.5]24.0 [22.5–26.8]0.521Course of diabetes (years)10.0 [4.00–16.0]10.0 [6.00–17.0]0.12410.0 [3.00–15.0]13.0 [5.00–17.5]0.10610.0 [3.00–16.0]10.0 [5.00–15.0]0.536Hypertension, n (%)489 (54.8)44 (67.7)0.059193 (51.3)25 (71.4)0.036220 (55.0)22 (75.9)0.046Dyslipidemia, n (%)508 (57.0)35 (53.8)0.720229 (60.9)20 (57.1)0.799291 (72.8)21 (72.4)1.000Current smoking, n (%)278 (31.2)22 (33.8)0.756114 (30.3)8 (22.9)0.465119 (29.8)9 (31.0)1.000Fast glucose (mmol/L)7.00 [5.46–9.50]7.50 [5.96–9.86]0.3386.80 [5.52–9.50]6.05 [5.29–9.34]0.2627.78 [6.02–10.9]6.61 [5.40–9.02]0.102HbA1c (%)8.30 [7.10–9.90]9.40 [7.60–10.8]0.0298.40 [7.10–10.1]8.50 [7.20–10.2]0.7378.50 [7.20–10.3]8.90 [7.00–10.3]0.940CT-FFR ≤ 0.8, n (%)46 (5.16)27 (41.5) < 0.00127 (7.18)14 (40.0) < 0.00117 (4.25)9 (31.0) < 0.001Obstructive CAD, n (%)240 (26.9)46 (70.8) < 0.001102 (27.1)25 (71.4) < 0.00172 (18.0)18 (62.1) < 0.001CACS2.56 [0.00–45.7]87.5 [13.1–275] < 0.0015.69 [0.00–59.9]47.9 [11.1–243] < 0.0012.25 [0.00–48.2]59.9 [11.1–262] < 0.001HRP, n (%)231 (25.9)41 (63.1) < 0.00197 (25.8)21 (60.0) < 0.00186 (21.5)15 (51.7)0.001LAP, n (%)234 (26.2)43 (66.2) < 0.00198 (26.1)22 (62.9) < 0.00184 (21.0)13 (44.8)0.006PR, n (%)450 (50.4)55 (84.6) < 0.001195 (51.9)31 (88.6) < 0.001200 (50.0)25 (86.2) < 0.001SC, n (%)49 (5.49)9 (13.8)0.01322 (5.85)5 (14.3)0.06820 (5.00)3 (10.3)0.197NRS, n (%)81 (9.08)15 (23.1)0.00137 (9.84)9 (25.7)0.00923 (5.75)5 (17.2)0.032Microvascular complications, n (%)645 (72.3)49 (75.4)0.695261 (69.4)27 (77.1)0.446305 (76.2)25 (86.2)0.317BMI, body mass index; CACS, Coronary Artery Calcium Scoring; CAD, Coronary artery disease; CT-FFR, computed tomography fractional flow reserve; HbA1c, hemoglobin A1c; HRP, high-risk plaque; LAP, low-attenuation plaque; MACE, major adverse cardiovascular event; NRS, napkin-ring sign; PR, positive remodeling; SC, spotty calcificationValues are mean ± SD, n (%), or median (IQR). SD, standard deviationFig. 2Representative cases of diabetic patients with and without MACE. ( A) CCTA of a 63-year-old male with stable angina showed multiple obstructive stenosis at proximal and middle LAD. Plaque characterization revealed the presence of LAP for the proximal lesion (blue content) and the distal CT-FFR value was 0.63. The total points of the proposed nomogram were 185. This patient underwent late revascularization of proximal LAD lesion 2.29 years later due to aggravated angina symptom. ( B) CCTA of a 79-year-old male with stable angina showed mild stenosis at proximal LAD. Plaque characterization revealed the absence of any HRP feature and the distal CT-FFR value was 0.97. The total points of the proposed nomogram were 55. The patient did not develop MACE at a follow-up of 2.72 years. CCTA = coronary computed tomography angiography; CT-FFR = computed tomography fractional flow reserve; HRP = high-risk plaque; LAP = low-attenuation plaque; LAD = left anterior descending; MACE = major adverse cardiac events According to Kaplan–Meier survival curves, patients with CT-FFR ≤ 0.8 or the presence of HRP had markedly higher MACE rate compared to patients without flow-limiting lesions or vulnerable plaques (Fig. 3). Similar findings were also noted for patients with obstructive CAD or CACS > 400.Fig. 3Kaplan–Meier curves for cumulative event rate of MACE according to (A) CT-FFR; (B) CAD-RADS classification; (C) HRP; (D) CACS. CACS = coronary artery calcium score; CAD-RADS = Coronary Artery Disease—Reporting and Data System; CT-FFR = computed tomography fractional flow reserve; HRP = high-risk plaque; MACE = major adverse cardiac event ## Development and validation of prediction models In the training set, all clinical and imaging parameters were screened as potential predictors for MACE. According to univariate analysis, age, CACS, HbA1c, CT-FFR ≤ 0.80, CAD-RADS ≥ 3, HRP features and Alpha-glucosidase inhibitor were all predictors for MACE ($p \leq 0.05$), which were subsequently included into further multivariate Cox regression analysis. Multivariate Cox regression analysis found that CT-FFR ≤ 0.80 (HR = 4.534, $p \leq 0.001$), HbA1c (HR = 1.142, $$p \leq 0.015$$) and LAP (HR = 3.973, $$p \leq 0.041$$) remained the independent predictors for MACE. These three factors in model 2 were used to construct a nomogram at 1-, 2-, and 3-year to predict the probability of MACE (Fig. 4). The details of univariate and multivariable Cox regression analyses are presented in Table 3.Fig. 4Nomogram for 1-, 2-, and 3-year probability of MACE. HbA1c = hemoglobin A1c; CT-FFR = computed tomography fractional flow reserve; LAP = low-attenuation plaqueTable 3Univariate and multivariable Cox regression analysis of clinical and imaging predictors for MACE in the training cohortUnivariate analysisMultivariate analysis 1*Multivariate analysis 2HR$95\%$CIp valueHR$95\%$CIp valueHR$95\%$CIp valueAge (per + 1 year)1.0341.010–1.0590.0061.0160.991–1.0420.2141.0200.994–1.0480.131Male1.5200.909–2.5410.110BMI, (per + 1 kg/m2)0.9940.926–1.0660.859Course of Diabetes (per + 1 year)1.0180.990–1.0470.202Hypertension1.6821.000–2.8290.0501.2020.700–2.0630.5041.0780.621–1.8690.790Dyslipidemia0.9910.607–1.6160.970Current smoking1.0420.623–1.7440.875fast glucose (per + 1 mmol/L)1.0370.983–1.0940.186HbA1c (per + $1\%$)1.1271.015–1.2520.0251.1331.017–1.2630.0241.1421.026–1.2720.015CACS (per + 1)1.0021.001–1.002 < 0.0011.0011.001–1.002 < 0.0011.0011.000–1.0010.089CT-FFR ≤ 0.810.8656.627–17.814 < 0.0014.5342.468–8.330 < 0.001CAD-RADS ≥ 36.1093.579–10.429 < 0.0012.1921.117–4.2990.0221.8560.932–3.6970.078Any HRP4.5302.737–7.499 < 0.0010.5670.146–2.2060.4130.3750.090–1.5640.178Any LAP5.0753.035–8.485 < 0.0013.6671.031–13.0480.0453.9731.060–14.8930.041Any PR5.1382.619–10.079 < 0.0011.5710.624–3.9540.3381.6610.652–4.2310.288Any SC2.6671.319–5.3950.0061.0460.492–2.2250.9080.9070.419–1.9610.804Any NRS3.1041.739–5.540 < 0.0011.3110.681–2.5240.4181.2090.624–2.3440.573Microvascular complications1.2500.710–2.2000.439Insulin secretagogues0.6380.353–1.1520.136TZDs1.6980.415–6.9410.461Insulin0.8870.540–1.4560.634Biguanides0.9250.568–1.5070.755α-Glucosidase inhibitors1.7181.053–2.8050.0301.5160.915–2.5120.1071.6260.975–2.7130.063DPP-4 inhibitors1.4980.895–2.5070.124SGLT-2 inhibitor1.6230.508–5.1920.414GLP-1 RAs1.4020.439–4.4740.568β-blockers1.1430.416–3.1450.795ACEI1.3440.580–3.1120.491ARB1.1060.648–1.8880.712Ca2 + channel blockers0.9860.578–1.6820.958Statins1.6780.896–3.1410.106ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor antagonist; BMI, body mass index; CACS, Coronary Artery Calcium Scoring; CAD-RADS, Coronary Artery Disease—Reporting and Data System; CT-FFR, computed tomography fractional flow reserve; DPP-4, dipeptidyl peptidase 4; GLP-1 RA, glucagon-like peptide 1 receptor agonist; HbA1c, hemoglobin A1c; HRP, high-risk plaque; LAP, low-attenuation plaque; MACE, major adverse cardiovascular event; NRS, napkin-ring sign; PR, positive remodeling; SC, spotty calcification; SGLT-2, sodium-glucose cotransporter 2; TZDs, Thiazolidinedione drugsMultivariate analysis 1 was used for constructing model 1 and multivariate analysis 2 was used for constructing model 2 We further tested the predictive performance of established models for MACE in diabetes in internal validation and external test sets. The Log-likelihood test showed statistical significance between model 1 and model 2 in all the three cohorts ($p \leq 0.001$, respectively). In the training cohort, the C-index of model 2 was significantly larger than that of model 1 (C-index = 0.82 ($95\%$CI = 0.77–0.87) vs. C-index = 0.80 ($95\%$CI = 0.75–0.85), $$p \leq 0.021$$). Similar findings were revealed in internal validation and external test cohorts (Table 4). Moreover, the AUCs of model 2 for predicting 1- and 3-year MACE were significantly larger than those of model 1 in the training cohort. For the validation of the nomogram, the C-index was 0.80 ($95\%$ CI 0.75–0.86), 0.78 ($95\%$ CI 0.70–0.87) and 0.82 ($95\%$ CI 0.75–0.89) in the internal training, internal validation and external test cohorts respectively. The Kaplan–Meier curves in all three cohorts demonstrated that there were significant differences regarding the survival of patients in each risk group (Additional file 1: Fig. E1). The results were further validated in the internal validation and external test cohorts (Table 4). However, it is also notable that the C-index of the proposed model dropped to 0.621 ($95\%$ CI 0.45–0.80) in diabetic patients with CT-FFR value between 0.75 and 0.80.Table 4Prediction accuracy and risk reclassification of each modelModelsP Value by the Log-likelihood testC-index ($95\%$CI)P ValueTime-dependent AUCAICBrier score1-year3-year1-year3-yearTraining setModel 1 < 0.0010.80 (0.75–0.85)0.0210.820.82801.080.0110.026Model 20.82 (0.77–0.87)0.850.84786.250.0110.024Internal validation setModel 1 < 0.0010.80 (0.73–0.87)0.0220.800.84384.640.0250.039Model 20.84 (0.77–0.90)0.830.87371.560.0210.035External test setModel 1 < 0.0010.82(0.74–0.90)0.0270.890.82328.270.0170.031Model 20.85(0.77–0.93)0.920.87313.060.0150.025AIC, Akaike information criterion; C-index, Harrell’s C-statisticsModel 1 included age, sex, BMI, hypertension, diabetes, dyslipidemia, current-smoking, fast glucose, HbA1c, Obstructive CAD, CACS, HRP, LAP, PR, SC and NRSModel 2 = Model 1 + CT-FFR Based on model 2, the calibration curves showed excellent consistency between prediction and observation in all three cohorts (Fig. 5). In addition, the model 2 outperformed model 1 with a lower Brier score for 1- and 3-year outcomes in the training, internal validation and external test cohorts (Table 4). The model 2 also had lower AIC results than did model 1 in all three cohorts (Table 4).Fig. 5Calibration curves of the nomogram for 1-, 2-, and 3-year of MACE. Nomograms (A) for training cohort; (B) for internal validation cohort; and (C) for external validation cohort. MACE = major adverse cardiac event In the present study, the clinical benefits of model 2 were compared with model 1 in three cohorts by using the decision curve for 1- and 3-year events (Additional file 1: Fig. E2). Accordingly, model 2 provided incremental net benefits within a reasonable threshold probability compared to Model 1 in all three cohorts. ## Discussion The major finding of the present study is to confirm CT-FFR as the strong independent predictor for MACE in patients with diabetes. Moreover, incorporating CT-FFR into risk stratification model provided incremental value over conventional clinical and anatomical parameters for prediction of MACE. Patients with diabetes are two times more likely to develop MACE compared with those without diabetes [29]. Accurate risk stratification is a fundamental step to guide proper therapeutic decision [30]. Previous prediction models for MACE in diabetic patients are mainly based on clinical variables [31, 32], without taking the coronary imaging findings into consideration. However, previous studies have confirmed that diabetes is associated with higher incidence of vulnerable plaques, coronary calcification and higher atherosclerotic burden [33]. These pathological features are strongly related to unfavorable outcomes and can be non-invasively evaluated by CCTA. Therefore, it is of clinical importance to develop a prediction model incorporating clinical factors as well as coronary imaging features. CCTA is a valuable non-invasive imaging modality to detect coronary atherosclerosis and follow-up plaque progression in patients with diabetes [34]. Previous studies using CCTA have demonstrated that obstructive stenosis and HRP features are two independent predictors for MACE in diabetic cohorts [35, 36] and help to enhance risk stratification. However, CT-FFR, as an advanced functional imaging approach, has not been investigated for its prognostic value in diabetes. In the present study, we successfully developed a novel prediction model, consisting of both clinical variable and imaging parameters. According to multivariate Cox regression analysis, it was CT-FFR rather than obstructive CAD (CAD-RADS ≥ 3) that served as the independent predictor for MACE. This result could be ascribed to the advantages of CT-FFR over conventional stenotic extent to identify true flow-limiting lesions [37]. In this regard, the ischemic stenosis, which can be diagnosed by CT-FFR, tends to result in unrelieved symptom or late revascularization, despite of its stenotic severity. On the other hand, LAP was an independent imaging predictor for MACE in the present cohort. This HRP feature correlates to the large necrotic core in vulnerable plaque and is strongly associated with acute coronary syndrome [9]. It represents the high-risk lesion that is prone to rupture if not intensively treated. HbA1c was another independent predictor for MACE in the present cohort. Higher baseline HbA1c levels were found in this study. A previous study found that HbA1c levels at baseline were significantly associated with baseline plaque burden [38]. There is substantial evidence that increased chronic mean high blood glucose levels (usually HbA1c) are associated with a variety of diabetic complications including microvascular and macrovascular events [39, 40], particularly when levels are substantially elevated [41, 42]. Moreover, to test its generalizability, the current prediction model was further validated via an independent external dataset, which also exhibited excellent performance for predicting MACE. Therefore, the proposed model provided a comprehensive approach for cardiovascular risk stratification in diabetic patients with intermediate pre-test probability of CAD, who are the candidates for CCTA imaging as recommended by the present guidelines [6, 7]. In light of the above findings, the clinical implication of the present study lies in the following aspect. The application of CT-FFR is more favored than CT-based stenosis assessment for risk stratification. Compared to using CT-based anatomical evaluation, adding CT-FFR further improves the model performance for identifying diabetic patients at high risk of MACE. It consequently helps to guide appropriate therapeutic strategy and reduce unfavorable outcomes. Despite the aforementioned promising results, the current study has several limitations. First, CT-FFR is a parameter that indicates the hemodynamic significance of epicardial coronary arteries. However, diabetes may also result in microvascular dysfunction (MVD), which can only be evaluated by other additional functional imaging modalities, such as cardiac magnetic resonance imaging [43]. Therefore, the risk of diabetic patients with MVD might be underestimated by the present model. In addition, the current cohort in majority consisted of diabetic patients with intermediate pre-test probability of CAD. Thus, the model cannot be directly applied to other diabetic cohorts, either with low or high pre-test probability. Finally, the nomogram score had significantly decreased predictive performance in diabetic patients with CT-FFR value between 0.75 and 0.80. It can be ascribed to the impaired diagnostic accuracy of CT-FFR for “grey-zone” lesions [44]. In conclusion, vessel-specific CT-FFR was a strong independent predictor for MACE in diabetic cohorts. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE. ## Supplementary Information Additional file 1. Figure E1. Kaplan–Meier curves for patients in the low- and high-risk groups in training cohort (A), internal validation cohort (B), and external validation cohort (C). Figure E2. Decision curves of prediction model for (A) 1-year MACE in the training cohort; (B) 3-year MACE in the training cohort internal; (C) 1-year MACE in the internal validation cohort; (D) 3-year MACE in the internal validation cohort; (E) 1-year MACE in the external validation cohort; (F) 3-year MACE in the external validation cohort. 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--- title: Improvement of vaginal probiotics Lactobacillus crispatus on intrauterine adhesion in mice model and in clinical practice authors: - Fei Wu - Yao Kong - Wenjie Chen - Dingfa Liang - Qin Xiao - Lijuan Hu - Xiao Tan - Jing Wei - Yujuan Liu - Xiaorong Deng - Zhaoxia Liu - Tingtao Chen journal: BMC Microbiology year: 2023 pmcid: PMC10032012 doi: 10.1186/s12866-023-02823-y license: CC BY 4.0 --- # Improvement of vaginal probiotics Lactobacillus crispatus on intrauterine adhesion in mice model and in clinical practice ## Abstract ### Background Intrauterine adhesion (IUA) is a frequent acquired endometrial condition, for which there is no effective preventive or treatment. Previous studies have found that vaginal microbiota dysregulation is closely related to endometrial fibrosis and IUA. Therefore, we wondered whether restoration of vaginal microbiota by vaginal administration of L. crispatus could prevent endometrial fibrosis and ameliorate IUA. ### Results First, we created a mechanically injured mouse model of IUA and restored the mice’s vaginal microbiota by the addition of L. crispatus convolvulus. The observations suggested that intrauterine injections of L. crispatus significantly decreased the degree of uterine fibrosis, the levels of IL-1β and TNF-α in blood, and downregulated the TGF-β1/SMADs signaling pathway in IUA mice. A therapy with L. crispatus considerably raised the abundance of the helpful bacteria Lactobacillus and Oscillospira and restored the balance of the vaginal microbiota in IUA mice, according to high-throughput sequencing. Then we conducted a randomized controlled trial to compare the therapeutic effect of L. crispatus with estrogen after transcervical resection of adhesion (TCRA). And the results showed that vaginal probiotics had a better potential to prevent intrauterine adhesion than estrogen. ### Conclusions This study confirmed that L. crispatus could restore vaginal microbiota after intrauterine surgery, inhibit endometrial fibrosis, and finally play a preventive and therapeutic role in IUA. At the same time, it is a new exploration for the treatment of gynecological diseases with vaginal probiotics. ### Clinical trial registration : http://www.chictr.org.cn/, identifier (ChiCTR1900022522), registration time: $\frac{15}{04}$/2019. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12866-023-02823-y. ## Background Intrauterine adhesion (IUA), a common acquired endometrial disease, typically develops as a result of endometrial damage brought on by trauma, curettage, infection, etc. [ 1], and its typical clinical signs are amenorrhea, infertility and pelvic pain [2]. Currently, the mainstream treatment for IUA is a combination of treatment based on transcervical resection of adhesion (TCRA) [3] supplemented by intrauterine device (IUD) placement [4], stem cells [5] or estrogen [6]. However, high treatment costs, displacement of the IUD device and re-adhesion after treatment bring heavy psychological and economic burdens to patients [7]. Hence, it is imperative to develop a noninvasive technology that is safe, effective and attractive in order to prevent adhesion from occurring again. IUA is a prevalent medical disorder that is essentially an inflammatory and fibrotic disease brought on by poor endometrial epithelial regeneration and repair, but the exact mechanism of its occurrence is not clear [8]. It has been demonstrated that NF-κB is a crucial regulator of inflammation-fibrosis [9], which activates and translocates to the nucleus in response to inflammatory factor stimulation, binds specifically to the promoter binding site of TGF-β1 activator, promotes TGF-β1 expression, and activates the TGF-β1/SMADs signaling pathway [10]. The SMADs protein is phosphorylated and binds to the corresponding binding site of NF-κB, which is also phosphorylated, in the nucleus, re-encoding translation and stimulating the production of more cellular inflammatory factors [interleukin-1β, (IL-1β); tumor necrosis factor-α, (TNF-α)], causing persistent damage to cells, promoting fibroblast differentiation, collagen fiber production, and subsequently fibrosis [11, 12]. Moreover, our previous study has confirmed that vaginal microbiota disorder is closely related to IUA [13]. Specifically speaking the quantity of Lactobacilli in the vagina of IUA patients decreased from $97\%$ to $45\%$ compared to healthy women of reproductive age, and in $30\%$ ($\frac{6}{20}$) of them, the presence of Lactobacilli in the vagina was barely detectable. Meanwhile, the pathogenic bacteria Gardnerella and Prevotella sp. were dramatically increased in the vagina of IUA patients. Therefore, we wondered whether restoration of vaginal microbiota by vaginal administration of L. crispatus could prevent endometrial fibrosis and ameliorate IUA. Lactobacillus crispatus (L.crispatus) is a Gram-positive bacterium that can maintain vaginal microecological health by producing acid, hydrogen peroxide and bacteriocins in the vagina [14], and has good inhibitory effect on common pathogens of vaginitis such as Candida albicans and Group B Streptococcal species [15]. In addition, it has good antioxidant properties and can adhere to Hela cells in large numbers and effectively prevent the colonization of Hela cells by opportunistic pathogens [16]. At the same time, it is safe, non-toxic, stable and can be preserved for a long time [17]. To date, little research has examined the connection between the vaginal microbiota and IUA, particularly the possible role of probiotics in the management of IUA. Therefore, we chose L. crispatus, which has several advantages, as the probiotics for this study. To explore whether the vaginal probiotic L. crispatus can improve IUA, we conducted this study using a combination of basic and clinical science. ## L. crispatus prevent and treat intrauterine adhesion in mice In this study, after adaptive feeding, the mice model of intrauterine adhesion was constructed by means of mechanical injury, and then treated with L. crispatus. In the end, there were six mice in C and M groups and only five mice in L group. In Fig. 1A, we can observe that the uterus of mice in group M lost elasticity and narrowed uterine cavity compared with control group C. Meanwhile, HE staining and Masson staining additionally demonstrated that the monolayer columnar cell layer on the endometrial surface of mice in group M was broken or even absent, and the fibrous scar tissue gradually replaced or covered the original endometrial group, and the gland structure was small and sparse, accompanied by inflammatory cell infiltration. However, in group L mice treated with L. crispatus, uterine morphology improved but did not return to normal levels. The endometrial surface of the mice in group L had a complete and continuous monolayer of columnar cells, endometrial glands also increased, and the infiltration of inflammatory cells and collagen fibers decreased. This was evident from the findings of HE staining and Masson staining, so L. crispatus can lessen the harm that mechanical manipulation does to the endometrium. Fig. 1Improvement of fibrosis and inflammation in mice by L. crispatus. ( A)The morphological changes of intrauterine adhesion were observed in the isolated uterine model; He staining was used to observe inflammatory exudation and Masson staining was used to assess collagen fiber deposition in the uterine tissue of intrauterine adhesion mice (magnification: × 100). At the protein level, the effect of L. crispatus on the expression of (B) TNF-α and (C) IL-1β in uterine tissues of mice with intrauterine adhesions. At the gene level, the effect of L. crispatus on (D) TNF-α and (E) IL-1β transcription in uterine tissues of mice with intrauterine adhesion. ( F) Inflammatory protein expression in uterine tissues of mice with intrauterine adhesion. Effects of L. crispatus on inflammation-related (G) TLR4 and (H) p-NF-κB/NF-κB proteins in uterine tissues of mice with intrauterine adhesion. C group, Control group; M group, Mechanical injury was used to construct a model of intrauterine adhesion; L group was treated with L. crispatus for intrauterine adhesion mice. * $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001.$ Western blotting images were cropped to improve the conciseness of the data, and the original images with visible membrane edges can be found in Supplementary Material ## L. crispatus effectively inhibit the production of proinflammatory factors As TNF-α and IL-1β are the main mediators of local inflammatory response [18], the contents of two proteins in each mouse group’s uterine tissue were detected by ELISA and Q-PCR at the protein and gene levels, respectively, to explore the relationship between IUA and inflammation. As shown in Fig. 1B-E, compared with group C, surgery increased the expression of TNF-α (37.82 to 149.18, $P \leq 0.01$) and IL-1β (405.76 to 1031.72, $P \leq 0.01$) in the serum in group M, while group L treated with L. crispatus decreased TNF-α (149.18 to 63.37, $P \leq 0.05$) and IL-1β (1031.72 to 665.03, $P \leq 0.05$) expression. Furthermore, the Q-PCR results further demonstrated that intrauterine surgery significantly increased the transcriptional levels of TNF-α (1.00 to 1.42, $P \leq 0.01$) and IL-1β (1.00 to 1.52, $P \leq 0.01$) in group M, while L. crispatus treatment significantly reduced the transcriptional levels of pro-inflammatory factors in group I. However, since activation of signaling pathways is required for the release of inflammatory factors, we used western blotting to further investigate the canonical inflammatory TLR4/NF-κB signaling pathways. In Fig. 1F-H, compared with group C, surgery increased the expression levels of TLR4 (0.45 to 1.16, $P \leq 0.001$) and p-NF-κB (0.55 to 1.13, $P \leq 0.01$). The opposite trend was expressed in L group mice treated with L. crispatus. ## L. crispatus reduces fibrosis in the models of intrauterine adhesion mice Previous studies demonstrated that activation of the TGF-β1/Smads signaling pathway was associated with the appearance of IUA [19, 20], so we used western blotting to assess its expression level at the adhesion site. As shown in Fig. 2A-D, compared with group C, the expression of TGF-β1 (0.72 to 1.23, $P \leq 0.01$), p-Smad2 (0.48 to 1.12, $P \leq 0.01$) and p-Smad3 (0.56 to 1.20, $P \leq 0.001$) increased significantly in group M after surgery. After L. crispatus treatment, TGF-β1 (1.23 to 0.85, $P \leq 0.05$), p-Smad2 (1.12 to 0.75, $P \leq 0.05$) and p-Smad3 (1.20 to 0.80, $P \leq 0.01$) expression levels were restored in group L. Therefore, we studied the expression of Matrix metalloproteinase-9 (MMP-9) and α-Smooth muscle actin (α-SMA) in each group, and found that intrauterine surgery did down-regulate MMP-9 (Fig. 2E), which was significantly improved after L. crispatus treatment, while α-SMA had the opposite result (Fig. 2F). Fig. 2The formation of intrauterine adhesions is closely related to the fibrosis TGF-β1/Smads pathway. ( A) Expression of fibrosis-related proteins in uterine tissues of intrauterine adhesion mice. Effects of Lactobacillus crimp on (B) TGF-β1, (C) p-smad2 /Smad2, (D) p-Smad3/Smad3, (E) MMP-9 and (F) α-SMA proteins related to fibrosis in uterine tissues of mice with intrauterine adhesion. C group, Control group; M group, Mechanical injury was used to construct a model of intrauterine adhesion; L group was treated with L. crispatus for intrauterine adhesion mice. $P \leq 0.05$; $P \leq 0.01$; $P \leq 0.001.$ Western blotting images were cropped to improve the conciseness of the data, and the original images with visible membrane edges can be found in Supplementary Material ## L. crispatus improved the vaginal microbiota of mice with IUA In the previous study, we found that there were significant differences in vaginal microbiota of patients with IUA compared with normal women. Therefore, we used high-throughput sequencing technology to explore whether the vaginal microbiota of mice in each group was different. According to the PCoA results (Fig. 3A), most of the points in group L are close to group C, while the points in group M are scattered away from group C. Subsequently, we compared the differences of the top 10 microbiota in the vaginal microbiota of each group at phylum level and genus level (Fig. 3B-C). In Fig. 3D-E, compared with C group, the richness of Firmicutes in M group (31.88 to $22.81\%$, $P \leq 0.05$) decreased to a certain extent, while after treatment with L. crispatus, this bacteria in L group (22.81 to $32.09\%$, $P \leq 0.05$) increased compared with those before treatment. However, Cyanobacteria spp. showed the opposite trend. Intrauterine operation could reduce its richness (0.43 to $0.21\%$, $P \leq 0.01$), but after treatment with L. crispatus, its richness increased (0.21 to $0.27\%$, $P \leq 0.05$). In Fig. 3F-G shows the results of vaginal microbiota at genus level. The abundance of Oscillospira in group M was higher than that in group C (5.78 to $4.15\%$, $P \leq 0.05$). This trend was reversed with the supplementation with L. crispatus, with a significant decrease in Oscillospira abundance in the L group (4.15 to $6.31\%$, $P \leq 0.05$) when compared to group M. However, the content of Lactobacillus (3.12 to $1.71\%$, $P \leq 0.05$) in group M was reduced by the operation, but the abundance of Lactobacillus (1.71 to $2.59\%$, $P \leq 0.05$) in group L increased obviously after L. crispatus treatment. Fig. 3Effect of L. crispatus involved on vaginal microbiota in mice with intrauterine adhesion. Evaluation of the effect of L. crispatus on vaginal microbiota of intrauterine adhesion mice using (A) the PCoA of the β diversity index, the relative abundance (B) at the phylum level and (C) at the genus level. At the phylum level, the effects of L. crispatus on (D) Firmicutes and (E) Cyanobacteria were evaluated. At the genus level, the effects of L. crispatus on (F) Oscillospira and (G) Lactobacillus were evaluated. C group, Control group; M group, Mechanical injury was used to construct a model of intrauterine adhesion; L group was treated with L. crispatus for intrauterine adhesion mice. ns, $P \leq 0.05$; $P \leq 0.05$; *$P \leq 0.01$ ## Clinical investigation of patients with IUA A total of 125 people were evaluated for eligibility from January 2020 to December 2021; of those, 104 were assigned at random and used in the study, 52 in both the E and L groups. During this process, 3 participants in group E (2 withdrawal of consent, 1 lost to follow-up) and 5 participants in group L (3 withdrawal of consent, 2 lost to follow-up) failed to complete the study (Fig. 4). In addition, the incidence of CD38 (+) and CD138 (+) in group I was $27.08\%$ (14 + $\frac{12}{49}$ + 47) after routine pathological examination of endometrium after TCRA. The participants in E group and L group were graded as mild, moderate and severe according to the Chinese intrauterine adhesion diagnostic grading criteria [21], and no discernible distinction could be made between the two groups. The thickness of the endometrium before and after treatment, and the cure rate and recurrence rate after TCRA were statistically compared between the two groups (Table 1). Among them, 17 patients ($34.69\%$) in group E had postoperative recurrence, while only 8 patients ($17.02\%$) in group L, showed $P \leq 0.05$ by X2 test. Fig. 4The flow chart of a clinical trial Table 1Comparison of the efficacy of E group and L group after operation for IUAVariablesE groupL groupP value Number 4947 Age 29.76 ± 0.556829.87 ± 0.59420.8857 Degree of adhesion Mild adhesion 6 ($12.24\%$)7 ($14.89\%$) Moderate adhesion 31 ($63.27\%$)29 ($61.70\%$) Severe adhesion 12 ($24.49\%$)11 ($23.41\%$) CD38(+) and CD138(+) 14 ($28.57\%$)12 ($25.53\%$) Endometrial thickness (mm) a Pre-operation 4.955 ± 0.21855.155 ± 0.24910.9437 Post operation 7.178 ± 0.29936.291 ± 0.23520.0667 Postoperative effect b Cure 32 ($65.31\%$)39 ($82.98\%$)0.0486 Recurrence 17 ($34.69\%$)8 ($17.02\%$)aMean ± SD, t-testbNumber (percentage), X2 test ## L. crispatus can improve IUA vaginal microbiota Finally, 10 vaginal secretion samples were selected in the group C, I, E and L, a total of 40 samples were used for high-throughput sequencing. In α-diversity, there are significant differences in Shannon ($P \leq 0.01$) and Simpson ($P \leq 0.005$) (Fig. 5A). Principal coordinate analysis (PCoA) of the β-diversity data revealed that the microbiological diversity of each group varied (Fig. 5B). An aggregated heat map was also produced for each group by association of the 20 vaginal microbes with the highest average abundance at the genus level (Fig. 5G). According to the findings, group C had the highest and group I had the lowest relative abundance of the beneficial bacterium Lactobacillus. Moreover, in Group I, the content of the following bacteria is relatively high, such as Prevotella, Anaerococcus, Megasphaera, etc. In group E, there was a greater relative abundance of Gardnerella and Finegoldia. But in the L group, the relative content of Pseudomonas, Methylobacterium, Enterococcus, Devosia and other bacteria was higher. The intrauterine adhesions before and after treatment were compared with vaginal microbiota in healthy women at phylum and genus levels. Significant changes were found in the composition of each classification and the taxonomic composition of vaginal microbiota in each group was compared (Fig. 5C-D). At the phylum level (Fig. 5E-F), compared with group C, the beneficial bacteria Firmicutes (93.52 to $76.88\%$, $P \leq 0.05$) in patients with IUA decreased, while Bacteroidetes (0.09 to $4.37\%$, $P \leq 0.05$) increased. After estrogen treatment, Firmicutes (76.88 to $85.99\%$, $P \leq 0.05$) increased, while Bacteroidetes (4.37 to $2.05\%$) decreased. This trend was even more pronounced after treatment with L. crispatus (76.88 to $89.35\%$, 4.37 to $0.31\%$). At the genus level (Fig. 5H-I), Lactobacillus was significantly reduced in group I (93.14 to $62.19\%$, $P \leq 0.01$), while Gardnerella (0.24 to $2.27\%$, $P \leq 0.05$) was increasing. After treatment, the content of Lactobacillus in Group E (62.19 to $83.18\%$, $P \leq 0.05$) and group L (62.19 to $88.11\%$, $P \leq 0.01$) increased. The relative levels of Gardnerella decreased in groups E (2.27 to $5.83\%$, $P \leq 0.05$) and L (2.27 to $0.19\%$, $P \leq 0.05$). Fig. 5The effect of L. crispatus and Estrogen on vaginal microbiota in patients after TCRA. Evaluation of the effect of L. crispatus and Estrogen on vaginal microbiota of TCRA using: (A) the Shannon index and the Simpson index; (B) the PCoA of the β diversity index; the relative abundance (C) at the phylum level and (D) at the genus level. At the phylum level, the effects of L. crispatus and Estrogen on (E) Firmicutes and (F) Bacteroidetes were evaluated. At the genus level, the effects of L. crispatus on (H) Lactobacillus and (I) Gardnerella were evaluated. ( G) the cluster heat map of IUA before and after treatment compared with healthy women. C group, the healthy female control group; I group, patients with untreated IUA; E group was treated with Estrogen after TCRA surgery; L group was was treated with L. crispatus after TCRA surgery. $P \leq 0.05$; *$P \leq 0.01$ We allow visitors to reproduce images with permission and/or credit ## Discussion A combination of TCRA supplemented with IUD [22], balloon [23], intrauterine anti-adhesive [24] and amniotic membrane [25] are the primary methods of treating IUA. Postoperative dysbiosis, overgrowth of pathogenic bacteria and displacement of IUD will lead to serious physiological disorders and disruption of microecological balance, which in turn will affect the patient’s postoperative recovery and prognosis [26]. Nowadays, the antagonistic and synergistic effects between vaginal microbiota are significant in keeping the female reproductive system in good condition [27]. One or a few Lactobacilli, such as Lactobacillus iners, L. crispatus, Lactobacillus jensenii, and Lactobacillus gasseri, which exhibit relatively low microbial diversity, predominate in the healthy reproductive women’ vaginal microbiota [28]. Studies have reported that surgical stimulation, use of prophylactic antibiotics, and irrigation of large amounts of normal saline may destroy reproductive tract microbes balance, leading to retrograde infection of pathogenic bacteria in the vagina and inflammation of the uterine cavity [16–29]. At present, the clinical application of vaginal probiotics is mainly for the treatment of different forms of vaginitis, and few studies have investigated the possible role of probiotics in the treatment of IUA [30]. So this study aims to investigate the therapeutic effect and mechanism of vaginal probiotics on IUA through animal and clinical experiments. It is well known that the constant stimulation of infection and inflammation impairs the repair of endometrial basal layer damage, leading to intrauterine inflammation and fibrosis, and ultimately to IUA [31]. In animal experiments, we evaluated the treatment effect from mouse uterine morphology, HE staining and Masson staining. And ELISA and Q-PCR were employed to identify the expression of mouse serum IL-1β and TNF-α pro-inflammatory factors, because inflammation has a substantial impact on fibrosis [32]. Then, western blot analysis was used to determine the protein expression of the inflammatory signaling pathways (TLR4, p-NF-κB and NF-κB) and the fibrosis signaling pathways (TGF-β1, p-Smad2, Smad2, p-Smad3, Smad3, MMP-9, α-SMA). The results showed that L. crispatus considerably reduced the uterine injury’s inflammatory and fibrotic reactions and postponed the occurrence of IUA. In the process of further research on the mechanism of action, we discovered that probiotics significantly decreased the pro-inflammatory factors IL-1β and TNF-α and downregulated TLR4/NF-κB Inflammatory Signaling pathways. In IUA tissue, studies have demonstrated that NF-κB is considerably up-regulated and activated, promoting the production of IL-1β and TNF-α, two chemicals that cause inflammation [11]. Tissue fibrosis occurs as a result of these pro-inflammatory chemicals’ actions on fibroblasts [12]. Inferring that probiotics prevent IUA by lowering the inflammatory response, L. crispatus therapy decreased the expression of essential proteins in the TLR4/NF-κB signaling pathway. The purpose of IUA treatment is to reduce endometrial fibrosis and boost endometrial regeneration. TGF-β1 and MMP-9 are two profibrotic and antifibrotic cytokines whose interactions control the damaged endometrial healing process [33]. Smad2 and Smad3 are two significant downstream regulators that encourage TGF-β1 mediated tissue fibrosis [34], and their phosphorylation functions play an important role in avoiding TGF-β1 mediated fibrosis [35]. The degree to which α-SMA, a marker of myofibroblasts, is expressed reflects the degree of fibrosis production [36]. MMP-9, a downstream target gene of TGF-β1, is considered as an anti-fibrosis factor due to its ability to degrade and reshape extracellular matrix (ECM) [37]. This study confirmed that surgery increased fibrosis in model mice, resulting in up-regulation of α-SMA and down-regulation of MMP-9. However, the use of L. crispatus significantly reduced the essential proteins linked to the TGF-β1/Smads pathway (TGF-β1, p-Smad2, p-Smad3 and α-SMA), while considerably boosted the level of MMP-9. In high-throughput sequencing results, the composition and diversity of vaginal microflora in mice with IUA were significantly changed, as shown by PCoA in Fig. 3A. So, we may infer that this population’s microbiota is unbalanced. But this imbalance in vaginal microbiota improved after L. crispatus intervention, consistent with previous studies [38]. Vaginal dysbiosis promotes colonization of the vagina by pathogens and leads to the formation of bacterial biofilms and an increased risk of vaginal infection, implying the occurrence and recurrence of chronic diseases [39]. The results of the microbiota at the genus level showed that there was a significant decrease in the abundance of I group Oscillospira and Lactobacillu, and the treatments with L. crispatus showed a large increase in the abundance of both species, and made the composition of vaginal microorganisms after treatment more similar to normal levels. Oscillospira, as one of the producers of butyrate [40], can be considered as a beneficial bacterium of the vagina to maintain the health of the female vagina, mainly because butyrate promotes mucosal repair and functional recovery, inhibits the formation of inflammatory cytokines, and has anti-inflammatory effects [41]. Moreover, Lactobacillus can ferment sugars to produce lactic acid, maintaining the acidic environment of the vagina, which is thought to be highly protective against infection or vaginal colonization by pathogens and non-native microorganisms. In addition, it can secrete bacteriocins and other antimicrobial factors to inhibit or kill pathogenic microorganisms [42]. So, Lactobacillus plays an important role in the maintenance of vaginal microbial homeostasis. There have been studies in the treatment of human papilloma virus (HPV) [43] and bacterial vaginosis (BV) [30] by vaginal administration of Lactobacillus to restore vaginal microbiota composition. Therefore, the homeostasis of women’s vaginal microbes plays a significant role in women’s health [44, 45]. This study suggests that the formation of IUA is related to the imbalance of vaginal microbiota. Vaginal administration of L. crispatus to improve the imbalance of vaginal flora has therapeutic effect on IUA mice. In the clinical investigation, $27.08\%$ of patients with IUA had endometritis, which were positive for CD38 and CD138, which was consistent with previous studies on the etiology of intrauterine adhesion [46]. Therefore, we speculated that the cervical mucus plug may be destroyed during intrauterine surgery, causing the spread of pathogenic bacteria to the uterine cavity and increasing the risk of intrauterine infection, leading to the occurrence of intrauterine adhesions and endometritis [47, 48]. After TCRA, by comparing with estrogen, we found that the effect of estrogen in promoting endometrial growth is better than that of L. crispatus, but the recurrence rate after TCRA can reach $34.69\%$. This confirmed that postoperative estrogen use promotes endometrial growth and inhibits endometrial fibrosis [33]. However, increased estrogen levels are conducive to the dominance of Lactobacillus in vaginal microbiota, but the incidence of vulvovaginal candidiasis (VVC) is also increased [49]. Under the influence of female high estrogen, the accumulation of glycogen in the vaginal epithelial cells increases, the lactic acid is increased by the decomposition of Lactobacillus, the pH value in the vagina decreases, and the microecological balance in the vagina is destroyed, which is conducive to the survival of anaerobic pathogens suitable for an acidic environment, also contribute to the adhesion of pathogens [50]. In addition, under the action of estrogen, the congestion, edema and permeability of the vaginal mucosa increase, which may make the vaginal mucosa more vulnerable to injury than before treatment, thus making it more prone to vaginal infection [51], and eventually leading to the occurrence of IUA [2]. Meanwhile, long-term estrogen therapy may affect women’s health, including fertility, diabetes, obesity and cancer [52]. Although L. crispatus was inferior to estrogen therapy in promoting endometrial growth, its cure rate was as high as $82.98\%$ ($P \leq 0.05$). High-throughput results showed significant changes in vaginal microbiota composition between patients with IUA and healthy women. Treatment with L. crispatus improved vaginal microbiota more than treatment with estrogen. Not only was α-diversity statistically significant, but PCoA also confirmed this result. At the genus level. Lactobacillus in patients with Intrauterine adhesion was significantly reduced, but after estrogen therapy, especially after L. crispatus treatment, Lactobacillus was close to the level of normal women. The opposite trend was observed in pathogenic Gardnerella. Therefore, we infer that vaginal microbiota dysbiosis exacerbates the development of IUA, this is consistent with our previous research results [13]. Furthermore, any imbalance in the vaginal microbiota can lead to vaginal infections, such as bacterial vaginitis (BV), aerobic vaginitis (AV), atrophic vaginitis, candida vaginitis, and trichomonad vaginitis [53]. Studies have shown that the reduction of vaginal L. crispatus may increase the risk of HIV [42] and HPV [43] infection, as well as the incidence of BV [30], polycystic ovary syndrome (PCOS) [52] and endometriosis (EMS) [54]. Therefore, vaginal administration of L. crispatus can not only reduce the recurrence rate after Intrauterine adhesion, but also effectively prevent and treat other gynecological diseases and improve fertility. This study confirmed that the supplementation of L. crispatus can promote early postoperative recovery of patients with intrauterine adhesions by restoring the vaginal microbial balance and inhibiting uterine inflammation and fibrosis. However, some limitations should be taken note. We know that the vaginal microbiota is a complex biological system, and we did not further purify the specific components of L. crispatus that play a role in the treatment of intrauterine adhesion. Additionally, the study included a small volunteer base, which reduced the validity of the statistical analysis. And we did not follow up on the pregnancy and reproductive status of the patients after treatment. ## Conclusions This study confirmed that L. crispatus could promote early postoperative recovery in patients with IUA by restoring vaginal microbial balance and inhibiting uterine inflammation and fibrosis. Although vaginal probiotics have been explored in the treatment of gynecological diseases, large sample and multi-center clinical data are still needed to confirm their efficacy in the treatment of luminal adhesion. ## Animal models and treatments A total of 32 female adult SPF BALB/C mice (HUNAN SJA Laboratory animal co., LTD), weighing 22–26 g and aged 6–8 weeks, were employed in this experiment. The mice were placed in a clean, cozy, air-conditioned space with unrestricted access to food and drink. The temperature, lighting, noise, ventilation and other conditions of the observation room are controlled within the specified range. After one week of adaptation, 32 mice with similar body weight and strong adaptability were selected and divided into 3 groups, including the control group (C, $$n = 8$$) and 2 experimental groups ($$n = 12$$ per group). These experimental groups were divided into M (model of IUA) and L (IUA + L. crispatus) groups. All animal procedures were completed at the Institute of Translational Medicine of Nanchang University after approval by the Ethical Committee of Nanchang Royo Biotech Co., Ltd (reference number RYE2019121702). After adaptation, the IUA model of mice in all experimental groups was established by using the same mechanical injury method as Yang Huan’s [55] according to the clinical surgical requirements. Briefly, the mice were anesthetized by intraperitoneal injection of $1\%$ sodium pentobarbital (100 mg/kg; Cat# B1202-005; Fluka), and the abdominal cavity was opened to expose the uterus after disinfection and sterile surgical towel covering. After preparing a 2 mm transverse incision in the upper part of the uterus, a curettage was used to create a 1.5-2.0 cm endometrial lesion without puncturing the uterine wall. After suturing the uterus and surgical incisions, each mouse was fed separately for 2 weeks to recover. Compared with the model group (M group), the L group was given 1 × 108CFU/mL/day L. crispatus (L. crispatus, Lcr-MH175, number CGMCC 15,938, HarbinMeihua Biotechnology Co., Ltd., Harbin, Heilongjiang, China) every night using absorbable gelatin sponge in their vaginas for consecutive 2 weeks after surgery. Vaginal secretions [56] from each group were collected consecutively for 5 days prior to euthanasia, and sufficient samples were obtained for high-throughput sequencing. At the end of the two weeks, the mice were euthanized and their venous blood, uterine tissue, and vaginal tissue were collected and appropriately kept for later investigations. ## Histological analysis Histological examination was carried out in accordance with earlier studies [37]. Prior to being cut into 6 μm transverse slices, mouse uterine tissue was first fixed with $10\%$ paraformaldehyde, dried in a graded ethanol solution, and embedded in paraffin. After HE and Masson stains were applied to all of the slices, under a microscope, the pathogenic alterations were seen. ## Cytokine assays Mouse serum was obtained by centrifugation at 1000x g for 20 min at 4 ° C, after which serum cytokine concentrations were measured using ELISA kits for IL-1β(Cat#SEA563Mu; mouse; Cloud-Clone Crop; sensitivity range: 15.6–1,000 pg/mL; concentration range used for generating calibration curves: 1,000, 500, 250, 125, 62.5, 31.2, 15.6 and 0 pg/mL) and TNF-a (Cat# SEA133Mu; mouse; Cloud-Clone Crop; sensitivity range: 15.6–1,000 pg/mL; concentrations used for generating calibration curves: 1,000, 500, 250, 125, 62.5, 31.2, 15.6 and 0 pg/mL), according to the manufacturer’s instructions. ## Q-PCR assays As previously reported [57], Q-PCR was carried out according to the manufacturer’s instructions. Using a high purity total RNA rapid extraction kit (Gibco BRL; Thermo Fisher Scientific), total RNA was isolated from mouse uterine tissue. In addition, the purity and integrity of RNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc.). Genomic DNA was then removed at 42°C for 2 min, reverse transcribed at 37°C for 15 min, and reverse transcriptase inactivated at 85°C for 5 s to synthesize cDNA. Next, Quantitative real-time PCR was performed using a 7500HT fast real-time PCR system (ABI; Thermo Fisher Scientifc, Inc.). Next, Quantitative real-time PCR was performed using a 7500HT fast real-time PCR system (ABI; Thermo Fisher Scientifc, Inc.). Forty cycles at 95˚C for 30 sec and 60˚C for 30 sec were conducted, preceded by 1 min at 95˚C. Then use the 2–ΔΔ Ct comparison method to calculate the mRNA level, and finally use the GAPDH mRNA expression normalization analysis. The following primers were used in reference to the previous literature [58]: TNFα sense, 5’-GTGGAACTGGCAGAAGAGGCA-3’ and antisense, 5’AGAGGGAGGCCATTTGGGAAC-3’; IL-1β sense, 5’-GTGTCTTTCCCGTGGACCTTC-3’ and antisense, 5’TCATCGAGCTGTAGTGC-3’. ## Western blot analysis Standard methods were used to perform Western blotting [58]. To put it simply, the proteins from each group’s uterus were isolated, purified to a specific level of purity. Polyacrylamide gel electrophoresis (SDS-PAGE) was used to separate equal amounts of proteins, which were then transferred to a polyvinylidene fluoride membrane. Following a 2-hour soak in $5\%$ skim milk with Tris-buffered saline and TBST to inhibit nonspecific binding sites, the membrane was incubated with the following primary antibodies overnight at 4 °C. Mouse anti TLR4 (Santa Cruz Biotechnology), rabbit anti-NF-κB (ProteinTech Group), rabbit anti-phosphorylated-NF-kB (p-NF-kB; Abcam), rabbit anti-TGF-β1 (ProteinTech Group), rabbit anti p-Smad2 (Cell Signaling Technology), rabbit anti-Smad2 (ABclonal), rabbit anti-p-Smad3 (Cell Signaling Technology), rabbit anti-Smad3 (ABclonal), rabbit anti-MMP-9 (ABclonal) and rabbit anti-β-actin (Cell Signaling Technology). After being washed three times with TBST for ten minutes each, the membrane was incubated for an hour at 25 °C with goat anti-rabbit secondary antibody (ProteinTech Group) or goat anti-mouse secondary antibody (ProteinTech Group) at a dilution of 1:5000. Enhanced chemiluminescence agents were utilized to determine the protein concentrations, and Image J gel analysis software was used to quantify the intensities. The alterations of associated proteins were then calculated and examined using the internal control. ## Patient samples and treatments January 1, 2019 to June 30, 2020, a total of 125 patients diagnosed with IUA by hysteroscopy in the Jiangxi and Jiujiang maternal and child health hospital in China were enrolled. Patients with untreated IUA were included in I group, and the inclusion criteria were(i) age ranged from 18 to 40; (ii) IUA was the hysteroscopy’s underlying diagnostic in the outpatient setting; (iii) endocrines and ovulation were normal. The exclusion criteria included: (i) fallopian tube problems include hydrosalpinx or obstruction; (ii) other organic gynecological diseases and other basic diseases related to hormones; and (iii) refuse, irregular use of medication and loss to follow-up. Participants were randomly divided into E group (Estrogen therapy) and L group (L. crispatus therapy). All patients underwent a series of medical evaluations prior to participation, including a medical history review, physical examination, blood work and transvaginal ultrasound. For the ease of a later examination, the endometrium and vaginal secretions were also collected and cryopreserved in the specimen tube. Samples of vaginal discharge and clinical data were obtained from the patients. The clinical experiment was approved by the Institutional Review Board (IRB) of the Second Affiliated Hospital of Nanchang University, registered in the Chinese Clinical Trial Registry (registration number: ChiCTR1900022522). And it was conducted according to the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines. All participants also provided a signed written informed consent. Groups E or L were randomly assigned to each participant, and adhesions should be separated by hysteroscopy after completion of relevant examinations and a small amount of endometrium was collected during the operation for routine pathological examination. The most widely utilized hormone therapy was employed to treat the patients in group E [6]: Oral estradiol valerate or its equivalent for 21 days, with a daily dose of 4 mg, and 10mgprogesterone acetate or its equivalent was added after 21 days for one week. However, patients in group L used vaginal capsules made of L. crispatus (Lcr-MH175, number CGMCC 15,938, HarbinMeihua Biotechnology Co., Ltd., Harbin, Heilongjiang, China) lyophilized powder (1 × 108 CFU/ granule) every night for 2 weeks after surgery. All patients came to the hospital three days after their next clean menstruation to check whether they adhered again, and their vaginal secretions were collected for later high-throughput sequencing. To more accurately assess the alterations in vaginal microbiota in intrauterine adhesion patients before and after therapy, not only the vaginal secretions of 10 people in each group of groups I, E and L were randomly selected, but also the vaginal secretions of 10 healthy women meeting the following conditions were selected as control group C. These included: (i) age 18–40 years with normal menstrual cycle (28 ± 7 days); (ii) no obvious abnormalities were found in vaginal discharge, cervical cancer screening and gynecological ultrasound examination; (iii) no history of other chronic underlying diseases or surgical procedures. ## DNA extraction and highthroughput sequencing Vaginal secretions were collected from mice and patients in each group, and bacterial genomic DNA was extracted according to the instructions for use of DNA kit from Tiangen Biotech Co., Ltd. The nano drop spectrophotometer (NanoDrop; Thermo Fisher Scientifc, Inc.) to measure the concentration and quality of DNA. The 16 S ribosomal DNA (rDNA) V4 region was amplified using primers (F, AYTGGGYDTAAAGNG; R, TACNVGGGTATCTAATCC) in each sample, and the Q-PCR products were sequenced on the IlluminaHiSeq 2000 platform (GenBank accession number PRJNA 882,985 and PRJNA 883,005). Amplicon* generation and sequencing were completed in PersonalbioCo., Ltd. (Shanghai, China). ## Statistical analysis QIIME (v1.8.0, http://qiime.org/), FLASH (v. 1.2.7, http://ccb.jhu.edu/software/FLASH/), the UCLUST software package and R software were used to analyze the high-throughput sequencing data and evaluate the diversity both within and between samples. Prism software (version 7.0; GraphPad Software, Inc.) was used to evaluate all the data. The information was presented as mean and standard deviation (SD). The one-way analysis of variance (ANOVA), Student’s t test, and X2 test were all used to determine statistical significance. Statistical significance is thought to be indicated by a P value of 0.05. 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--- title: '‘Get Healthy!’ physical activity and healthy eating intervention for adults with intellectual disability: results from the feasibility pilot' authors: - Carmela Salomon - Jessica Bellamy - Elizabeth Evans - Renae Reid - Michelle Hsu - Scott Teasdale - Julian Trollor journal: Pilot and Feasibility Studies year: 2023 pmcid: PMC10032022 doi: 10.1186/s40814-023-01267-5 license: CC BY 4.0 --- # ‘Get Healthy!’ physical activity and healthy eating intervention for adults with intellectual disability: results from the feasibility pilot ## Abstract ### Background People with intellectual disabilities (ID) experience high rates of lifestyle related morbidities, in part due to lack of access to tailored health promotion programmes. This study aimed to assess the feasibility and preliminary efficacy of a tailored healthy lifestyle intervention, Get Healthy! ### Methods Get Healthy! is a 12-week physical activity and healthy eating programme designed to address lifestyle-related risks for adults with mild-moderate ID. The feasibility pilot was designed to assess subjective participant experience and programme feasibility across: recruitment and screening, retention, session attendance and engagement, adverse events, and practicality and reliability of outcome procedures. Exploratory programme efficacy was assessed across the following measures: anthropometry (body mass index, weight, waist circumference), cardiovascular fitness, physical strength, dietary intake, healthy literacy, and quality of life. ### Results Six participants with moderate ID and two carer participants completed the feasibility trial, representing a $100\%$ retention rate. Qualitative data indicated the programme was well received. Participants with ID attended $75\%$ of sessions offered and displayed a high level of engagement in sessions attended ($91\%$ mean engagement score). While most data collection procedures were feasible to implement, several measures were either not feasible for our participants, or required a higher level of support to implement than was provided in the existing trial protocol. Participants with ID displayed decreases in mean waist circumference between baseline and endpoint ($95\%$ CI: − 3.20, − 0.17 cm) and some improvements in measures of cardiovascular fitness and physical strength. No changes in weight, body mass index, or objectively measured knowledge of nutrition and exercise or quality of life were detected from baseline to programme endpoint. Dietary intake results were mixed. ### Discussion The Get Healthy! programme was feasible to implement and well received by participants with moderate ID and their carers. Exploratory efficacy data indicates the programme has potential to positively impact important cardiometabolic risk factors such as waist circumference, cardiovascular fitness, and physical strength. Several of the proposed data collection instruments will require modification or replacement prior to use in a sufficiently powered efficacy trial. ### Trial registration ACTRN: ACTRN12618000349246. Registered March 8th 2018—retrospectively registered, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374497 UTN: U1111-1209–3132. ## Key messages regarding feasibility There was uncertainty regarding the feasibility of implementing the Get Healthy! group programme for adults with mild-moderate ID, and whether the selected outcome measures could be reliably administered to the population. The Get Healthy! programme was feasible to implement, however, several outcome measures required a greater level of training/support to administer than was provided in the feasibility protocol, and a small number were too complex for the participants with moderate ID.The Get Healthy! programme will be feasible to administer in a sufficiently powered trial; however, several screening and outcome measures will require modification prior to trial commencement. ## Background Despite significant advances in longevity and quality of life, people with intellectual disabilities (ID) continue to experience poorer health outcomes than the general population [1]. The term ‘intellectual disability’ is used to describe any person who experiences ‘significant limitations both in intellectual functioning and in adaptive behaviours, as expressed in conceptual, social and practical adaptive skills. The disability presents or originated during the developmental period before the age of 18 years’ [2]. Many causes of premature mortality in this population are linked to potentially preventable conditions [3]. Lifestyle risks including poor diet quality [4], low levels of physical activity [5], and high rates of sedentary behaviour [6], are prevalent across age groups. People with ID are more likely than the general population to be overweight or obese and have high rates of type 2 diabetes and lipid abnormalities [7, 8]. Common prescribing of high cardiometabolic liability psychotropics in this population [9] further exacerbates risk. Health status, quality of life and health expenditure are all negatively impacted by this high prevalence of lifestyle-related diseases [10, 11]. Tackling lifestyle-related behaviour has been identified as a priority area for improving health outcomes for people with ID [12]. However, people with ID still have low levels of engagement in health promotion initiatives and preventative screenings [1]. Financial, physical, social and disability related barriers limit this population’s ability to access health promotion programmes available to the general population [13]. The limited and inconsistent ID health training received by the medical and allied health workforce [14, 15] means that many care providers lack confidence tailoring health promotion practices to the unique needs of this group. There is also a lack of clarity regarding the essential components of lifestyle change interventions most likely to improve health outcomes. Evidence for the efficacy of general population healthy lifestyle programmes is robust [16]; however, these programmes are not necessarily generalisable to people with ID. Results from ID population-specific interventions reported in the literature are limited and have had mixed results. Weight loss for adults with ID, for example, has been inconsistently reported across interventions, but appears to be most likely in the context of multi-modal interventions encompassing physical activity, dietary and behaviour change components—see [17] for a review. Methodological weaknesses, use of varied outcome measures and differing population characteristics (i.e. level and cause of ID, age-group, gender, living arrangements) across studies limits comparison of findings [18]. A meta-analysis of randomised-controlled healthy lifestyle trials for adults with ID showed statistically significant improvements in waist circumference only [18]. Further trials are needed to clarify the core components of interventions that will promote engagement and positive lifestyle change in this population. The primary aim of this study is to assess the feasibility of implementing a tailored healthy lifestyle programme, Get Healthy! with adults with mild-moderate ID. The secondary study aim is to explore potential programme efficacy. Results from the feasibility pilot will be used to refine the programme content and data collection protocol prior to undertaking a sufficiently powered efficacy trial. ## Background to the ‘Get Healthy!’ programme Get Healthy! is a 12-week multi-modal lifestyle intervention programme focusing on physical activity and healthy eating for adults (40 + years) with mild to moderate ID, however is suitable for all adults with ID. The programme was developed by a consortium of topic experts in the fields of nutrition, ID, ageing, exercise physiology, nursing, psychiatry, and psychology. A series of focus groups with adults with ID and their paid carers [19] contributed consumer input to the programme design. Table 1 summarises the setting, structure and content of the programme, and lists all behaviour-change techniques used in the programme delivery. Table 1‘GET HEALTHY!’ Summary of program structure and contentProgram setting: Lifestyle Clinic in tertiary research instituteProgram structure: 12-week small group program consisting of three face-to-face contact hours per week (1x one hour nutrition session per week plus 2x one hour physical activity sessions held on non-consecutive days). Physical activity sessions were delivered by a practicing Accredited Exercise Physiologist. Nutrition sessions were delivered by Accredited Practicing Dietitians. All instructors had previous experience working with people with intellectual disabilityProgram ContentPhysical ActivityHealthy Eating$10\%$ didactic information Topics covered: What it means to be healthy; Consequences of obesity; Physical activity and screen time guidelines; Appropriate goal setting; Planning for maintenance and self-management; Barriers to Physical activity and how to address them$40\%$ aerobic exercise$30\%$ strength-based exercise$20\%$ balance-based exercise$90\%$ didactic content Topics covered: The five food groups; Discretionary foods and healthy snacks; Healthy drinks; Portion size and mindful eating; Eating out choices$10\%$ practical food related outings/preparationCALO-RE Behavior-change techniques useda1. Information provision (general)✓✓2. Information provision (to the individual)✓✓3. Information provision (others’ approval)✓4. Information provision (others’ behavior)✓✓5. Goal setting (behavior)✓✓6. Goal Setting (outcome)✓7. Action Planning✓✓8. Identifying barriers/Problem resolution✓9. Setting graded tasks✓✓10. Review of behavioral goals✓11. Review of outcome goals✓✓12. Effort or progress contingent rewards✓✓13. Successful behavior contingent rewards✓✓14. Shaping15. Generalization of target behavior16. Self-monitoring of behaviour17. Self-monitoring of behavioral outcome18. Focus on past success✓✓19. Provide feedback on performance✓✓20. Informing when and where to perform the behavior✓✓21. Instruction on how to perform the behavior✓✓22. Demonstrate behavior✓✓23. Training to use prompts24. Environmental restructuring✓25. Agreement of behavioral contract✓✓26. Prompt practice✓27. Use of follow-up prompts28. Facilitate social comparison✓29. Plan social support30. Prompt identification as role model✓✓31. Prompt anticipated regret32. Fear arousal✓33. Prompt self talk✓34. Prompt use of imagery✓35. Relapse prevention36. Stress management37. Motivational interviewing✓38. Time management39. Communication skills training✓40. Stimulate anticipation of future rewards✓aCALO-RE Taxonomy of Behavior Change. ( PDF Download Available). Available from: https://www.researchgate.net/publication/274512164_CALO-RE_Taxonomy_of_Behavior_Change_Techniques [accessed Mar 29 2018] ## Methods The full feasibility pilot protocol has been published elsewhere [20]. Methodology is summarised below for convenience. ## Recruitment Participants were recruited through disability service providers proximal to the healthy lifestyle centre where the intervention was delivered in metropolitan NSW, Australia. Adults who were identified by carers/disability organisations as having mild to moderate ID and concerns about cardiometabolic health were eligible to participate in the programme. The participants’ main carers were also invited to participate either independently in the full programme (carer-protocol A) or as a support person to the enrolled participants with ID (carer-protocol B). Participants who were non-ambulatory, had severe-profound ID, or who were not cleared by their general practitioner (GP) to participate due to either high physical or psychiatric risk, were excluded. ## Consent Written informed consent was obtained from all participants prior to trial commencement. For participants who lacked capacity to consent (~ $70\%$), written consent was provided by their legal guardian/carer as required by law. All participants with ID also obtained a signed medical clearance from their GP prior to enrollment. The study was conducted in accordance with the ethics approval granted by the UNSW HREC (Approval number: HC17471). ## Data analysis Programme feasibility was assessed across the domains of recruitment and screening, retention, adverse events, session attendance and session engagement. Every session the programme facilitators recorded attendance and scored attendees based on their level of engagement in the session (0 = did not attend, 1 = participated minimally, 2 = participated moderately well to very well). At the completion of the intervention combined scores for every session attended were used to categorise participants into high (75–$100\%$), medium (50–$74\%$) or low engagement (< $50\%$) groups. Subjective participant experience was gathered in audio-recorded semi-structured exit interviews with all participants. Qualitative data from exit interviews was transcribed and thematically organised using the software programme NVivo (version 11.0.0). All outcome measures included in the trial are listed in Table 2. The Statistical Package for Social Science (SPSS) was used to analyse percentages, score means and/or frequencies where relevant. Acknowledging the small sample, we used $95\%$ confidence intervals to reported outcomes in order to provide a clinically relevant indication of the direction of the effect being measured. Table 2Clinical outcome measurements/procedures used in the ‘Get Healthy!’ feasibility trialDimension measuredProcedure detailsBody mass index (BMI)BMI = weight/height (kg/m2) [22]Waist circumferenceMeasured at the midpoint between the iliac crest and the lowest rib, in full expiration, to the nearest 0.1 cm while the person is standing [23]Blood pressureTo be measured using sphygmomanometer while the participant is seated and has rested for at least 5 min prior [24]Cardiovascular fitnessYMCA sub-maximal ergometer test 12 min duration [25]:- Minutes/stages performed- Peak heart rate (%APMHR)- Peak workload achievedPhysical activity level and sedentary behaviourSubjective data:- International Physical Activity Questionnaire-proxy respondent (IPAQ-pr) proxy report [26]Objective data:- Waist-based GTX3 actigraph accelerometer to be worn for a period of 3–5 days in each data collection period [27]Physical Strength- 30-s modified push-up test [28]- Medicine ball throw/chest pass [29]- 10 RM testing [30]- 30-s sit-to-stand test [31]Quality of lifePersonal Wellbeing Index-Intellectual Disability (PWI-ID) [32]Dietary intake- 3-day photographic food record [33]- Proxy-assisted 24-h recall [34]Healthy literacyNutrition and Activity Knowledge Scale for Use with People with an Intellectual Disability (NAKS) questionnaire [35] Food intake data was calculated from photographic food and drink records at baseline and endpoint. Data was interpreted and analysed by two Accredited Practicing Dietitians using Foodworks® (version 9) nutrition analysis software (Xyris Software, 2018). Days with less than three meals captured were removed prior to analysis. A Healthy Eating Index for Australian Adults (HEIFA) [21] was then applied to determine overall diet quality. ## Results Six participants with ID and two carer participants completed the full screening process and were enrolled in the trial. ## Participant demographics Table 3 summarises demographics of participants with ID.Table 3Demographics of participants with IDAge (years)GenderLevel of IDMobility statusType of residenceCo-morbiditiesMean: 46SD: 13Range: 28–62 aMale ($$n = 4$$)Female ($$n = 2$$)Moderate b ($$n = 6$$)Able to ambulate independently ($$n = 5$$)Ambulate with cane ($$n = 1$$)Group disability housing ($$n = 4$$)With family ($$n = 1$$)Independently ($$n = 1$$)Obese ($$n = 3$$)Overweight ($$n = 2$$)Autism ($$n = 1$$)Impaired glucose tolerance ($$n = 1$$)Ventricular septal defect, and valvular heart anomalies ($$n = 1$$)aFour of the six participants were aged 40 years and over. A further two participants below this age bracket were included because they expressed an interest in improving cardiometabolic fitnessbLevel of ID was based on the assessment of the research team delivering the ‘Get Healthy!’ intervention ## Carer participant demographics Due to competing time commitments and variations in work schedules no family members or paid carers were able to enrol in the full programme (carer-option A). Two paid carers enrolled in the option B participation pathway. This participation pathway involved attending sessions in a support capacity as able. Both enrolled carer participants were female, over 18 years of age, and employed as paid disability staff. They supported several of the participants with ID in residential and day care settings. On average, these carers attended approximately $50\%$ of the available sessions. Since carer protocol B did not include collection of outcome measures data, all efficacy data reported below pertains to the participants with ID only Table 4. ## Recruitment and screening Recruitment was completed between July 2017 and February 2018. Thirty people with ID expressed an initial interest in participating in the trial programme. Of these, 14 were either unable to complete the consent form, or unable to determine a suitable time to attend the initial assessment. Sixteen participants completed the consent form and participated in the initial assessment. Ten participants dropped out during this screening process. Reasons for drop-out during the screening process included: Scheduling and/or transport problems ($$n = 7$$)Having a level of ID (severe to profound) that meant the person was unable to participate in the group learning structure of the ‘Get Healthy!’ programme ($$n = 3$$). GPs screened each of the remaining six participants and provided signed consent for their participation in the feasibility trial (Fig. 1). Recruitment was ceased in February 2018 (6 months) in accordance with the funding allocated. Fig. 1Consort diagram of participant recruitment ## Retention rate All six participants with ID who completed the full screening process and enrolled in the study went on to complete the programme, representing a $100\%$ retention rate. ## Attendance and session engagement On average, participants with ID attended $75\%$ of sessions offered as part of the programme. Attendance rates at physical activity and nutrition sessions were similar ($74\%$ and $76\%$ respectively). The top reason participants missed scheduled sessions was to attend medical, allied health or dental appointments that had been arranged prior to study enrolment. Mean participant engagement scores across all sessions attended was ‘high’ ($91\%$); however, participants were significantly more engaged in the physical activity sessions compared to the nutrition sessions (respective mean engagement scores of $99\%$ versus $77\%$). ## Outcome measure feasibility Table 4 summarises the feasibility of all outcome measures according to whether they were. (i)Reliably administered to all participants at both baseline and endpoint, or.(ii)Either unable to be administered or administered but returned incomplete or unreliable data sets. For all outcome measures where problems with data reliability or completeness were noted, specific issues of concern are listed. No adverse events were experienced by any participants. Table 4Feasibility of outcome measuresOUTCOMEMEASUREMeasure completed reliably and fully at both time pointsIncomplete data returned or problems with measure validity notedIdentified problems with administration and or validity of measureAnthropometric measuresXMeasures of cardiovascular fitnessXMeasures of physical strength (excluding 10RM)XKnowledge Scale for Use with People with an Intellectual Disability (NAKS) questionnaireXWhile all participants completed this measure, wide variability in baseline to endpoint scores raise questions about instrument reliability for our cohort: For example, one participant scored 13 at baseline but went on to score a significantly lower score of 5 at endpoint. Since it is unlikely that participants would ‘lose’ this amount of knowledge in a 12-week time frame it is possible that scores reflect guess-work rather than change in knowledgeThe Personal Wellbeing Index- Intellectual Disability (PWI-ID)XWhile all participants agreed to undertake the measure significant differences in pre-testing scores from baseline to endpoint raise concerns about instrument validity in our cohort: At baseline two participants were unable to complete step two of the pre-testing process. We were therefore unable to administer the measure to them. However, at endpoint, the same two participants were able to complete the full pre-testing protocol and the 11-point scale. The extremely high scores these participants recorded on the measure at endpoint (100 and 92.9 respectively) raise questions about the reliability of their responses, however. At baseline the remaining four participants pre-testing scores indicated that they were unable to complete the 11-point scale, however, at endpoint they were all able to appropriately answer the pre-testing questions and thus had the 11-point scale administered to them24 hour food recallXWhile this form was handed to each participant and the support worker who attended the session with them, no completed or partially completed forms were returned at baseline or endpoint: Participants were unable to independently recall what they had eaten at previous meals, and family members and carers did not complete the form on their behalfFood photographyXOnly two participants provided photographic data at both the pre-and post-program data collection periods. While the two participants captured three full days at baseline, neither reached the target of a three complete photographic records at endpoint (capturing 1 and 2 days only). One participant declined to undertake this task at both time-points (reason was not stated). The remaining three participants either did not take photos despite agreeing to undertake the task, took incomplete days of records or took photos in which they had blocked the camera lens with their hand or clothesAccelerometer dataXOne participant (baseline) and two participants (endpoint) did not meet the minimum wear time of at least six hours on three out of the five wear days that was stipulated in our protocol. One participant who had a co-occurring diagnosis of autism, struggled with wearing the device due to sensory issues (stated he dislikes the feel of the device around his waist)IPAQ-proxyXWhile this form was handed to each participant and the support worker who attended the session with them, only two of the forms were returned at baseline or endpoint, and these were insufficiently completed to provide meaningful dataPhysical Strength: 10RM strength testingXWe were unable to reliably establish participants’ rate of perceived exertion in the pre-testing phase of the protocol and thus were unable to administer this outcome measure. Inability to establish perceived rate of exertion was related to difficulties participants experienced using even a modified scale to rate their level of exertion. For example, participants, both in cases where the weight used was extremely light and in cases where the weight used was so heavy the participants could not attempt the task, reported the exercise as “easy”. Without this baseline measurement, all participants commenced the program on the lowest weight available and the decision to increase weight was based on technique alone and experienced Exercise Physiologist decision making ## Anthropometric measures Table 5 lists the groups’ mean baseline and endpoint anthropometric data. There was a decrease in the groups mean waist circumference (WC) from baseline to endpoint ($95\%$ CI: − 3.20, − 0.17 cm). Individually, one participant gained 0.5 cm in WC during the intervention, while all five other participants displayed reductions in WC (− 0.4 cm; − 2.2 cm; − 2.4 cm; − 3.4 cm; − 2.2 cm). There was no clinically significant change in the groups mean weight ($95\%$ CI: − 1.6, 1.9) or BMI ($95\%$ CI: − 0.80, 0.90) from intervention baseline to endpoint, with three participants displaying a non-significant increase in BMI post intervention, and three participants displaying a non-significant decrease. Table 5Anthropometric means (± SD) at intervention baseline and endpointNBaseline mean (± SD)Endpoint mean (± SD)Weight (kg)679.87 (11.78)80.00 (10.27)BMI (kg/m2)632.86 (7.53)32.88 (7.12)Waist Circumference (cm)6108.32 (16.02)106.63 (15.50) ## Cardiovascular fitness (CV fitness) Table 6 lists the groups mean CV data at baseline and endpoint. The mean number of minutes participants were able to undertake the activity increased from baseline to endpoint ($95\%$ CI: 2.44, 7.73). Similarly, the mean number of stages participants were able to perform increased from baseline to endpoint ($95\%$ CI: 1.16, 3.24 stages). The peak workload participants were able to achieve also increased from baseline to endpoint ($95\%$ CI: 49.17, 64.98). While none of the six participants were able to complete the full protocol at baseline, three participants were able to complete the protocol at endpoint. There were numerical improvements for mean YMCA Peak HR and APMHR from baseline to endpoint. Table 6Cardiovascular fitness-means (± SD) at intervention baseline and endpointBaseline mean (± SD)$$n = 6$$Endpoint mean (± SD)$$n = 5$$YMCA minutes completed2.81 (2.19)7.80 (1.64)YMCA stages completed.50 (.84)2.6 (.55)YMCA peak heart rate125.83 (25.93)129.60 (22.53)YMCA % APMHR71.47 (13.30)74.74 (13.68)YMCA peak workload70.83 (43.06)120.00 (57.00) ## Physical strength All physical strength parameters showed numerical improvements across the intervention The mean improvement in the Sit To Stand (STS) exercise of 2.93 ($95\%$ CI: − 0.18, 7.00) from baseline to endpoint is promising, given that an improvement of STS = > 2 reps may be clinically significant [36] particularly in relation to falls risk Table 7.Table 7Physical strength-means (± SD) at intervention baseline and endpointNBaseline mean (± SD)NEndpoint mean (± SD)30sec push-ups516.20 (3.56)616.83 (2.48)5kg medicine ball chest pass (m)62.49 (0.43)62.74 (0.48)30sec sit to stand512.40 (2.88)615.33 (2.73) ## Structured aerobic exercise conducted throughout intervention Cycling duration—session 1 started at 9.0 (± 2.0) min ($$n = 4$$) increased to 13.4 (± 1.4) min by session 24 ($48.9\%$ increase) ($$n = 6$$). Similarly, this is reflected by the distance cycled during each session—session 1 started at 2.7(± 1.3) km ($$n = 4$$), which increased to 5.6 (± 1.1) km by session 24 ($107.4\%$ increase) ($$n = 6$$). ## Accelerometer data Five participants (baseline) and four participants (endpoint) had sufficient accelerometer wear-time to meet the threshold for data analysis set in our protocol. Their results are summarised in Table 8 and Fig. 1.Table 8Objective physical activity data—means (± SD) at intervention baseline and endpointBaseline mean (± SD)$$n = 5$$Endpoint mean (± SD)$$n = 4$$Sedentary643.94 ± 198.07652.74 ± 128.57Light108.02 ± 78.7273.42 ± 27.08Moderate24.96 ± 13.3829.73 ± 10.21Vigorous0.60 ± 0.970.36 ± 0.25MVPA25.56 ± 12.9820.06 ± 17.42 Total MVPA across the week (pre) —105.35 ± 47.11 min ($$n = 5$$) vs. post-intervention 133.48 ± 73.21 min ($$n = 4$$).Those meeting the PA guidelines (150 min of moderate PA) pre: 1, increased to 2 post-intervention. ## Food intake Only two participants (E and A) completed the food photography task to a sufficient extent to allow for a preliminary analysis to be undertaken. Key nutrition baseline and endpoint data for these participants are summarised in Table 9. Both participants decreased their total fat and saturated fat intake from baseline to endpoint. Wholegrain intake improved at endpoint; however, results for refined grain intake were mixed. While participant E’s HEIFA score increased from baseline to endpoint, indicating better overall diet quality, Participant A’s score decreased over the same period. Overall, average daily energy was lower for both participants at endpoint, along with most macronutrient and many micronutrients. It is unclear if these patterns reflect real changes in diet quality or the fact that both participants recorded fewer complete days of data at endpoint compared with baseline. Table 9Food intake at intervention baseline and endpointParticipant IDAETimepointBaselineEndpointBaselineEndpointHEIFA scorea433843.7547Whole grain1.869282.5888643.0108513.972731Refined grain2.8051172.0172185.4012685.543118Energy DF (kj)6973.1635663.7428558.6754794.232Protein(g)91.1796672.9627392.1580344.85995Carbs available (g)180.365134.8632245.0325191.0486Total fat (g)57.9396651.9512772.1186917.68252Saturated fat (g)20.4311219.2483630.195457.440686Dietary fibre (g)27.3120622.5768820.6422319.43015aHeifa score is out of 100 possible points- higher scores indicate better diet quality and correlates to greater adherence to national dietary guidelines ## Health literacy Results from this trial showed no difference in mean NAKS scores from baseline (15.17) to Endpoint (13.17). Two of the participants recorded higher scores at endpoint while the remaining three participants recorded lower scores at endpoint. As identified in Table 3, there were concerns about the reliability of these data. All six participants completed the NAKS questionnaire pre- and post-intervention ($$n = 6$$). ## Quality of life Only three of the six participants passed the baseline pre-testing phase for the PWI-ID measure. Matched pre-post intervention data for these three participants shows no significant change in mean quality of life scores (baseline mean 88.2 vs. endpoint mean of 83.3). One of the three participants showed an increased score at endpoint, while the other two recorded decreased scores. As identified in Table 3, there were concerns about the reliability of these data. ## Participant experience All participants with ID, along with the two carer participants, participated in exit interviews. Qualitative feedback, including programme highlights and suggestions for improvement, were elicited, and thematically analysed. Core themes emerging from the exit interviews are summarised below. ## Programme benefits Participants highlighted several beneficial impacts from being involved in the Get Healthy! programme, including a sense of pride and achievement; improved knowledge of and commitment to healthy lifestyle change; increased opportunities for positive social interactions; and improved ability to set future healthy lifestyle goals. Table 10 provides contextualised data illustrating these positive impacts. Table 10Programme highlights and benefits: qualitative participant feedbackProgramme highlightQuotes from participants with intellectual disabilityQuotes from carer participantsProgramme fostered a sense of pride and achievement“When I was riding the bikes [the instructor] would say, ‘come on, keep on going, keep on going’. I thought that was good because it made me feel like I was losing weight...pushing that bit more.” Participant F“I liked them [reward stickers] …That means I went on the bike with a high score… [so I was motivated] I did more”. Participant E“I just really really like the whole [program] environment. I mean, [the instructor] was just amazing with the guys. She was always extremely positive. All the guys wanted to go each there because of her. … she was so calm and confident, that she was able to transmit that energy to the guys and then when they were there they wanted to be part of the activity, they wanted to be part of the exercise.” Carer 2““[Their] confidence grew with the [bike] equipment. In the beginning they were really hesitant and just did two minutes, then towards the end they were competing against each other- you know, pushing buttons themselves, and setting things up and all that sort of stuff. So the equipment confidence grew, they jumped in.” Carer 2Programme Increased knowledge of and commitment to healthy lifestyles:Healthy eating“I liked learning about how much sugar was in the coca cola… “[I eat] better now [post-program]- at home, eat salad” Participant D“[I liked] learning about different foods, and what you can have and can’t have. I thought it [the program] was very good because it helped me out a lot.” Participant F“Well I [still] love eating food and sometimes I love ice-cream. But only one. Not every day”. Participant A“I eat a small amount now…a small one [plate of pasta] and vegetables and that…I started on the program, drinking it [water]…[and now I] eat fruit. Its really healthy- eat bananas and that and eat grapes” Participant E“The guys used to come back and talk a lot about the healthy eating choices, and what they spoke about in the education sessions. And I know that they liked going out to different restaurants or shopping centres and participating in a healthy choice option. And there were a few of them that, you know, on days where they had brought their own morning tea or lunch in from home, you could see that they had made that healthy, conscious choice about buying the right things.” Carer 2Physical activity“[I exercise more now because] It’s healthy for your heart” Participant DYeah, walk- park- everyday [now]” …Exercise is very good. Very good idea. Makes sense. I loved it so much”. Participant A“I think exercising does you good because if you’re not exercising then you’re watching TV and I think you’ve got to get out, get away from it and go for a walk” Participant F“A few of the guys [in the program], their doctors had actually reported improvements and were asking what was going on. One participant who was in the group, his respiratory doctor, said his lung capacity was, he couldn’t believe the change that had happened. Because you know that test where you blow into the thing and the balls go up- he said it was like a different person from when he did it the last time, to when he happened to do it just towards the end of the program.” Carer 2The programme provided valued opportunities for social interaction“[I liked exercising] with my friends… I liked the people that taught me how to do it [exercise] Participant F”“[I liked spending time with] the staff’ Participant E“I think they enjoyed mixing with the university staff and the other different physiotherapists … they developed a bit of a friendship with a few people there. And also some of the other, some of the others participants- patients- some of the other older people coming in, they enjoyed having a chat with them every week as well” Carer 1“They all had fun [doing the program together], they were always- because I used to drive them [to the program] on the Tuesdays- they were lined up at my car ready to jump in to go every week. I was never having to chase anyone or find out where they were.” Carer 2The programme helped participants to identify future healthy lifestyle goals“I do need help, because sometimes I’ve got problems with what I eat…I think I have to cut back on sweets. I eat too much from the top [of the food pyramid]…. [ I’m going to] give up sugar, it’s not good. Too many Coca Colas not good” Participant A“I want to change, so I get back to the size that I was before, instead of all of this weight, a healthier weight, maybe get help staying away from fatty foods” Participant F“[one participant] you made such a positive change in her life that now when we are discussing her future goals, one of her goals is that she says she wants to do a cooking program. And the reason she wants to do a cooking program is because she wants to learn how to eat healthy…So I just want to point out that the program has really made a positive change in someone’s life.” Carer 1 ## Programme problems and challenges Participants with ID did not identify many areas for programme improvement, despite being explicitly asked. One participant stated finding that using the bike, “made me tired”, and another participant described struggling with motivation to get out of bed and attend the programme: “Maybe getting out of bed [to come, was hard]. I wanted to stay snuggly and warm and I didn’t want to get out of a warm bed” (participant F). Carer participants, however, identified several areas for programme improvement. These are summarised in Table 11.Table 11Programme problems and challenges: qualitative participant feedbackProgramme challengeQuotes from carer participantsThe programme was inadequately resourced:While the costs of the programme sessions were covered by the research team, transport to and from the sessions as well re-imbursement for carer time, was not covered. These out-of-pocket costs created financial stress for the participating organisation“There wasn’t any additional resources or, um, I don’t know, supports we had available to us for the program. Like it was, time that I had to put aside out of my week and the other carer had to do the same, and we had, you know, to stop other clients using the vehicle so that we could use the vehicle on a Thursday [to get to the program]. So it was a bit of a challenge because we had clients from all different parts of [the disability organization]…like on the days we couldn’t get a vehicle, the taxi to get us all there ended up being, like $120 bucks, just in a taxi to do a turn around there and back” Carer 2Communication between programme facilitators and formal and informal carers was inadequate“I did get a copy of handouts [from the program] because I would request it, but in other cases the guys were getting a copy of the handouts but I’m not sure if the support worker or the homes they were living were getting a copy as well. So you don’t know if the guys are taking the paper then they don’t want it anymore and then they trash it… the group homes or the families where they are living need to told, even if its on an email so the group homes or families have access to the same information because some guys are very particular about their things being touched.” Carer 1Issues with malfunctioning physical activity equipment created stress for some participants“Towards the end of the program the straps on the exercise bike on the seat broke, and, um, because they weren’t working for a few weeks, it just was a little bit difficult for some of our clients to say, use the exercise bikes without the straps because they’d got used to them.” Carer 2Healthy eating component of the programme was too theoretical“For the nutrition [sessions]….I think it would have been more beneficial for the guys to learn in practical ways about food… I think that the talking was good, but I just know from experience that they need practice…. so perhaps teaching them to make a healthy lunch would have been a bit better than to just talk about it… Because I know that they learn by seeing, by doing, by touching” Carer 1Lifestyle changes may not be sustained once programme is over:While carers highlighted a number of benefits resulting from programme participation, they expressed concern that changes may be unsustainable without further buy-in from family members and paid support staff“If they [participants with ID] don’t have a constant support, or a program in place with someone, or a group of people will be taking them every week to continue these [healthy lifestyle] approaches, …it just won’t happen. They won’t independently go and do it. Either they need the assistance to travel somewhere, or they need someone’s guidance to help them use the equipment in the gym, and …they need someone there to give them that push”. Carer 2So I think that it’s got to be a real commitment, not just from the practitioners perspective but also from the families perspective, because without their support they can't really do it alone. The ones that did [make healthy lifestyle changes] had extra support, whether that was in a group home or it was at home. So, yeah, it’s got to be a group agreement, it’s not just the participant. Because if the participant wants to lose weight, they want to do exercise but they live in a group home unless the carer takes them out they wont be able to do exercise. Its compromising, it’s finding a comprise, within the organisations where they are living. And keeping them accountable as well, do you know what I mean?” Carer 1 ## Discussion Results from the Get Healthy! feasibility pilot indicate that the programme was well received by a small group of adult participants with moderate ID and their carers. The programme has potential to positively impact several indicators of cardiometabolic health. ## Reflections on programme feasibility Screening: Only participants screened by GPs as safe to participate were included in the trial, however, GPs were not required to provide programme facilitators with details of each participant’s specific health conditions. Unfortunately, not all participants and/or carers in this feasibility trial were able to reliably self-report relevant medical conditions. For the planned efficacy trial we therefore recommend replacing the generic medical consent form, which only asks if any restrictions should be placed on the person’s participation, with a more detailed form prompting the GP’s to indicate whether or not the person has a known diagnosis of: high blood pressure, diabetes, asthma, allergies, cardiac complications, lipid abnormalities, musculoskeletal conditions, or psychiatric or behavioural issues that may impact on programme participation. GPs should also be requested to provide an up-to-date list of all medications the person is currently prescribed. Knowledge of these conditions can support programme facilitators to better manage risk and tailor the programme more effectively to each participant’s needs. ## Increasing programme engagement While overall programme attendance rates were acceptable and mean engagement scores were high, participants were notably less engaged in the nutrition component of the programme, compared to the physical activity sessions. Qualitative feedback from the exit interviews suggests that decreasing didactic teaching content and increasing practical activities related to food choice and preparation may increase engagement in nutrition sessions for the efficacy trial. An additional issue detracting from programme feasibility was limited carer involvement. Only two carers regularly attended the programme with participants, and no clear channels of communication were established between programme facilitators and carers who did not attend. Prior research has highlighted that carer buy-in can significantly improve the extent to which people with ID engage in and sustain healthy lifestyle behaviours [37–39]. Developing supplementary on-line or other written teaching content that carers can engage with remotely and developing a schedule of home-visits by programme facilitators, may help to build closer relationships with carers during the efficacy trial. ## Improving data collection Problems arose with the completeness and/or reliability of data from several of the outcome measures used in the feasibility pilot. A number of factors are likely to have contributed to this issue: Firstly, several of the measures (i.e. 24-h food recall, food photography, accelerometers, IPAQ-pr) required considerable carer support to complete. Retrospectively it is clear that the pilot protocol did not include a sufficiently robust carer training and follow-up schedule to ensure that full data sets were collected. The carer handouts and instructions sheets, for example, were not necessarily passed on from the participants with ID to their home carers and Get Healthy! programme facilitators did not have access to home carer contact information. Since food photography [40–42], use of accelerometers [43, 44] and the IPAQ-proxy [26] have all been shown in previous studies to be reliable and viable to implement in adult populations with ID, we recommend keeping these measures in the protocol for the efficacy trial. However, the protocol should be modified to allow programme facilitators to liaise directly with carers to provide them with task training. A schedule of telephone prompts and face-to-face support should also be implemented during data collection periods. Secondly, it is possible that several of the trial outcome measures, specifically, NAKS, PWI-ID and 10RM strength testing, were too complex and therefore inappropriate for our study participants, whom had a more ‘moderate’ spectrum of ID. The planned 10RM physical strength testing, for example, was unable to be implemented due to cognitive difficulties participants experienced using even a simplified rate of perceived exertion scale. Despite our AEP using clinical judgement to determine endpoint of 10RM testing (e.g. facial grimacing, perceived exertion, and technique safety), we believe that the values obtained do not represent individual’s true 10RM. To increase trial efficacy, we recommend replacing this measure with an objective assessment with simplified protocol measures (and reduced risk), such as a hand-grip strength test for upper body strength. Functional testing parameters, inclusive of normative data validated within this population remains limited, with future research looking to widen appropriate assessment selection. Similarly, the NAKS measure may have been too complex for several participants in this study. While the NAKS has been validated in populations with mild ID [35], it requires participants to be able to meaningfully choose from four options. We recommend that a pre-testing protocol be implemented in the efficacy trial to assess whether participants are capable of meaningfully choosing from four options. Another issue of concern that arose with administrations of the NAKS was presence of carers, who in some cases attempted to ‘prompt’ participants with correct answers. For the planned efficacy trial, we recommend administering the NAKS without a carer present wherever possible. Should the participant wish to have a carer present in a support capacity, we recommend providing additional guidance to the carer to refrain from prompting the participant’s answers. In the PWI-ID validation study [45], which included adults with mild and moderate ID, all participants were able to be administered at least the most basic (2-point scale) index. However, in our pilot, baseline pre-testing identified participants who were unable to be administered even this 2-point scale. This finding suggests several of our participants may have had a greater degree of intellectual impairment compared to the validation study cohort. The other issue of concern we experienced with the PWI-ID involved participants passing the pre-testing phase but then scoring at the top of the response range across all seven domains. Such a scoring pattern is most likely the result of acquiescent responding, a known issue among populations with ID [46]. The original validation study for this measure [45] also encountered this issue with data from $32\%$ of respondents needing to be removed prior to analysis due to suspected acquiescent responding. We recommend excluding suspected acquiescent response data from analysis in the efficacy trial. Participants who fail the baseline pre-testing protocol should not have the measure re-administered at endpoint. ## Reflections on potential programme efficacy Efficacy data from the trial are exploratory in nature, given the small sample size, and multiple missing data-points. Preliminary findings, however, indicate that compared to baseline, most participants in the ‘Get Healthy!’ programme recorded clinically meaningful reductions in waist circumference and some improvement in measures of cardiovascular fitness. Some participants also displayed clinically meaningful improvements in physical strength at programme endpoint. BMI, quality of life, and objectively measured health literacy did not appear to improve from baseline to endpoint. Dietary intake patterns were mixed and analysis was limited due to incomplete data. The decreases in waist circumference recorded for all but one participant is a promising finding, given that waist circumference provides a relatively simple and accurate reflection of central adiposity [47, 48]. Decreased central adiposity, in turn, is a strong predictor of lower risk for hypertension, diabetes mellitus, dyslipidemia, metabolic syndrome, and coronary heart disease [49, 50]. Reassuringly, given the lack of weight loss among study participants, this finding holds true irrespective of changes to BMI [50]. Study participants displayed some improvements in cardiovascular fitness from baseline to endpoint based on the YMCA sub-maximal testing protocol. Our participants significantly increased their clinical cardiovascular fitness throughout this intervention. Participants not only increased ($178\%$) their duration of cycling (2.81 vs. 7.80 min) but also their workload (70.83 vs. 120 W) by $69\%$ post-intervention, while maintaining a steady HR (70–$65\%$ APMHR). This indicates that participants were able to exercise longer at an increased workload, using the same amount of energy, indicating increased cardiovascular fitness. This is supported by the number of participants able to complete the YMCA sub-maximal testing protocol post-intervention (3 participants vs 0 participants pre-intervention). Based on post-intervention data, the average estimated VO2 was 2.14 L/min (31.51 ml/kg/min) indicating ‘poor’ cardiovascular fitness [51]. Poor cardiovascular ability to sustain prolonged physical work is a powerful predictor of morbidity and all-cause mortality as well as cardiovascular specific mortality [52, 53]. Improvements in measures of cardiovascular fitness, if confirmed in a sufficiently powered efficacy trial, would thus be another strong argument to implement the programme more widely among this at-risk population. Given that no participants were able to complete this incremental YMCA protocol pre-intervention, in addition to the poor cardiovascular fitness measured, we suggest fellow researchers consider the inclusion of a steady-state cardiovascular cycling protocol, such as the Astrand Rhyming Test, or modification to the YMCA step test to further increase data collection and efficacy. Improvements in measures of physical strength were also noted for some participants from baseline to programme endpoint. Again, this result, if replicated in a sufficiently powered efficacy trial, would be promising in terms of cardiovascular risk reduction. Improved physical strength has been shown to have an attenuating effect on premature all-cause mortality [54], as well as lifestyle-related disease such as diabetes [55], stroke [56] and obesity [54]. Our physical strength data highlights poor upper and lower body strength for adults with ID. Of particular note, is lower limb endurance and falls risk, indicative through the 30-sec STS data. Despite our cohort having a mean age of 46, this 30-sec STS data indicates increased falls risk for adults aged 60–64 years. Despite our intervention showing clinically meaningful improvements (12.40 pre- vs. 15.33 post-intervention) in this outcome measure, post-intervention data continued to represent increased falls risk for an age bracket 14–18 years their senior, highlighting the need for continued exercise interventions and health supports in this population. A point of further discussion includes the relatively large age range of the study participants (28–62 years of age). Despite concerted efforts of the research team to recruit people with ID 40 + years of age, due to the nature of the disability service providers who expressed interest in this study, we received a large age range of eligible participants. We must highlight the variances in physiological adaptations based on the ageing process, particularly on the ability to build muscular strength and improve cardiovascular fitness as a limitation of this study. This large age range could be a contributing factor in the diversity of change seen across our physical outcome measures. Further efficacy studies should look to either narrow the demographic age bracket of participants, or perhaps target the exercise intervention dependent on age. ## Conclusion The ‘Get Healthy!’ feasibility pilot was well attended and positively received by participants and carers. 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--- title: Association of Cardiovascular Medications With Adverse Outcomes in a Matched Analysis of a National Cohort of Patients With COVID-19 authors: - Leonard K. Wang - Yong-Fang Kuo - Jordan Westra - Mukaila A. Raji - Mohanad Albayyaa - Joseph Allencherril - Jacques Baillargeon journal: American Journal of Medicine Open year: 2023 pmcid: PMC10032048 doi: 10.1016/j.ajmo.2023.100040 license: CC BY 4.0 --- # Association of Cardiovascular Medications With Adverse Outcomes in a Matched Analysis of a National Cohort of Patients With COVID-19 ## Abstract ### Background The use of statins, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), and anticoagulants may be associated with fewer adverse outcomes in COVID-19 patients. ### Methods Nested within a cohort of 800,913 patients diagnosed with COVID-19 between April 1, 2020 and June 24, 2021 from the Optum COVID-19 database, three case-control studies were conducted. Cases—defined as persons who: [1] were hospitalized within 30 days of COVID-19 diagnosis ($$n = 88$$,405); [2] were admitted to the intensive care unit (ICU)/received mechanical ventilation during COVID-19 hospitalization ($$n = 22$$,147); and [3] died during COVID-19 hospitalization ($$n = 2300$$)—were matched 1:1 using demographic/clinical factors with controls randomly selected from a pool of patients who did not experience the case definition/event. Medication use was based on prescription ≤90 days before COVID-19 diagnosis. ### Results Statin use was associated with decreased risk of hospitalization (adjusted odds ratio [aOR], 0.72; $95\%$ confidence interval [$95\%$ CI], 0.69, 0.75) and ICU admission/mechanical ventilation (aOR, 0.90; $95\%$ CI, 0.84, 0.97). ACEI/ARB use was associated with decreased risk of hospitalization (aOR, 0.67; $95\%$ CI, 0.65, 0.70), ICU admission/mechanical ventilation (aOR, 0.92; $95\%$ CI, 0.86, 0.99), and death (aOR, 0.60; $95\%$ CI, 0.47, 0.78). Anticoagulant use was associated with decreased risk of hospitalization (aOR, 0.94; $95\%$ CI, 0.89, 0.99) and death (aOR, 0.56; $95\%$ CI, 0.41, 0.77). Interaction effects—in the model predicting hospitalization—were statistically significant for statins and ACEI/ARBs ($P \leq .0001$), statins and anticoagulants ($$P \leq .003$$), ACEI/ARBs and anticoagulants ($P \leq .0001$). An interaction effect—in the model predicting ventilator use/ICU—was statistically significant for statins and ACEI/ARBs ($$P \leq .002$$). ### Conclusions Statins, ACEI/ARBs, and anticoagulants were associated with decreased risks of the adverse outcomes under study. These findings may provide clinically relevant information regarding potential treatment for patients with COVID-19. ## Background As health care systems across the globe continue to battle the coronavirus disease 2019 (COVID-19) pandemic, identifying effective therapeutic options for the virus, particularly for patients with underlying comorbidities, is critically important.1, 2, 3 Biological pathways through which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes disease and mortality include systemic inflammation associated with a cytokine storm,4 immune dysregulation, and increased coagulopathy, associated with an increased risk of venous thromboembolism, stroke, and myocardial infarction.5,6 The use of existing drugs and their association with reduced COVID-19 disease severity and mortality is of great public health relevance. There is particular interest in examining the extent to which the use of three widely used cardiovascular medications—statins, renin-angiotensin-aldosterone system (RAAS) blockers, and anticoagulants—may be associated with reductions in COVID-19 morbidity and mortality.1,7 Statins, first-line lipid-lowering therapies, have consistently demonstrated potent antiinflammatory, antithrombotic, and immunomodulatory effects.8, 9, 10, 11 These pleiotropic effects may improve the clinical outcomes in patients with COVID-19 by reducing viral replication, endothelial dysfunction, and inflammatory dysregulation.12, 13, 14 Angiotensin-converting enzyme inhibitor (ACEI)/angiotensin II receptor blockers (ARBs), used as first-line hypertension therapies, have demonstrated antiinflammatory effects,15,16 specifically by blocking angiotensin-converting enzyme 2 (ACE2) downregulation-induced hyperactivation of the RAAS and attenuating oxidative stress, vasoconstriction, and inflammation.7,17 While there were initial theoretical concerns that ACEI/ARBs may increase the expression of the ACE2 receptor—the entry point for COVID-19 infection—thereby exacerbating COVID-19 infection susceptibility and disease progression,7,18 recent data from observational studies, randomized controlled trials, and metaanalyses have demonstrated that ACEI/ARBs do not have an adverse impact on clinical outcomes or prognosis of COVID-19 patients17,19, 20, 21, 22, 23, 24 and may, in fact, be associated with protective benefits.7,25,26 Anticoagulants are believed to mitigate the risk of microvascular and macrovascular thrombosis in COVID-19 patients.27,28 Anticoagulants such as unfractionated heparin have been shown to be efficacious and suppress the cytokine-mediated inflammatory process that results in endothelial dysfunction caused by COVID-19.27,29 Nevertheless, among the current data, controversy exists regarding the potential of anticoagulants to reduce adverse outcomes in COVID-19 patients.29, 30, 31, 32, 33 Our purpose was to examine the independent effect of each of these three classes of medication—statins, ACEI/ARBs, and anticoagulants—on COVID-19 outcomes in a large, demographically and clinically diverse cohort of COVID-19 patients. ## Data Source This series of nested case-control studies was based on Optum's longitudinal COVID-19 electronic health record (EHR) database, which was generated from the larger Optum EHR database to specifically study COVID-19. Including more than 90 million patients from 38 hospital networks and 18 nonnetwork hospitals in the United States, the database incorporates clinical and medical administrative data from both inpatient and ambulatory EHRs, practice management systems, and numerous other internal systems. Information was processed from across the continuum of care, including acute inpatient stays and outpatient visits. The database contains structured information, such as diagnosis, procedure codes, laboratory results, and clinical observations, including vital signs, blood pressure, pain, and body mass index (BMI). It also contains unstructured information in the form of clinical notes from office visits; consultation reports; discharge summaries; and reports from nursing records, pathology, radiology, and cardiology. Using a natural language processing (NLP) system, the unstructured data in the clinical notes are scanned to discover, interpret, and extract important clinical information. The COVID-19 database captures point-of-care diagnostics specific to the COVID-19 patient during initial presentation, acute illness, and convalescence with over 500 mapped labs and bedside observations, including COVID-19-specific testing. The database includes all patients in the EHR database from January 2007 through June 24, 2021. The data were certified as deidentified by an independent statistical expert following Health Insurance Portability and Accountability Act (HIPAA) statistical deidentification rules and managed according to Optum® customer data use agreements. A data use agreement was developed with Optum, and this study was approved by the Institutional Review Board of the University of Texas Medical Branch at Galveston. ## COVID-19 Study Cohort The study cohort is based on a cohort from the Optum COVID-19 database consisting of patients who were diagnosed with COVID-19 (by positive laboratory test (Appendix Table 4) or diagnosis [ICD-CM-10: U07.1]) between April 1, 2020, and June 24, 2021, and who received care in above-described health care delivery network, captured in the Optum EHR database, in the 12 months prior to COVID-19 diagnosis. ## Case Selection Three nested case-control studies of COVID-19 outcomes were conducted (Table 1). For study 1, cases ($$n = 88$$,405) were defined as patients who were hospitalized within 30 days of their COVID-19 diagnosis that occurred 0-30 days following the COVID-19 diagnosis date. For study 2, cases ($$n = 22$$,147) were defined as patients who were admitted to the intensive care unit (ICU; had one encounter during the hospitalization that was coded as “Critical Care Unit [CCU]/Intensive Care Unit [ICU]”) or received mechanical ventilation (ICD-10-PCS: 5A09357, 5A09358, 5A09359, 5A0935A, 5A0935B, 5A0935Z, 5A09457, 5A09458, 5A09459, 5A0945A, 5A0945B, 5A0945Z, 5A09557, 5A09558, 5A09559, 5A0955A, 5A0955B, 5A0955Z, 5A19054, 5A1935Z, 5A1945Z, 5A1955Z; CPT: 94,002, 94,003, 94,004) during their COVID-19 hospitalization. For study 3, cases were defined as patients who died during their hospitalization for COVID-19 ($$n = 2300$$). Because the Optum EHR data only has month and year of death, along with a death indicator, patients needed to meet three criteria to be considered to have died during their hospitalization: [1] the patient had a positive death indicator; [2] the patient had a death month and year equal to the month and year of their hospitalization end; and [3] the patient had no encounters of any kind following the hospitalization end date. Table 1Cohort Flow Chart. Table 1HospitalizationICU admission/mechanical ventilationHospital mortalityStepDescriptionNDescriptionNDescriptionN1All patients in data6,421,125All patients in data6,421,125All patients in data6,421,1252Patients in an integrated delivery network5,704,773Patients in an integrated delivery network5704,773Patients in an integrated delivery network5704,7733Patients with complete demographic data5,694,848Patients with complete demographic data5,694,848Patients with complete demographic data5,694,8484Patients with a COVID-19-positive lab test or diagnosis800,913Patients hospitalized within 30 days of diagnosis87,758Patients hospitalized within 30 days of diagnosis87,7584aPatients hospitalized within 30 days of diagnosis88,405Patients with ventilation use or ICU stay in hospital22,147Patients who died in hospital23284bPatients not hospitalized712,508Patients with no ventilation/ICU65,611Patients who did not die85,4305Patients matched175,516Patients matched43,250Patients matched46005aCases87,758Cases21,625Cases23005bControls87,758Controls21,625Controls2300 ## Control Selection The underlying cohort from which controls were selected included patients in the Optum COVID-19 database who met the following criteria: were in an integrated delivery network, had complete demographic data, and had a positive COVID-19 lab test or diagnosis (Table 1). For study 1, controls ($$n = 87$$,758) were randomly selected from patients who were not hospitalized ($$n = 712$$,508). For study 2, controls ($$n = 21$$,625) were randomly selected from patients who were not admitted to the ICU or had mechanical ventilation ($$n = 65$$,611). For study 3, controls ($$n = 2300$$) were randomly selected from patients who did not die during their hospitalization ($$n = 85$$,430). For each of the three case-control studies, cases were matched 1:1 with controls based on: sex, age (0-17, 18-39, 40-49, 50-59, 60-69, 70-79, ≥ 80), race (white, non-white), COPD (ICD-10-CM: I27.8x, I27.9, J41.x-J47.x, J60.x-J67.x, J68.4, J70.1, J70.3), obesity (ICD-10-CM: E66.x), atherosclerotic cardiovascular disease (ASCVD) (ICD-10-CM: I25.10), angina (ICD-10-CM: I20.x), diabetes (ICD-10-CM: E11.x-E13.x), hypertension (ICD-10-CM: I10.x-I15.x), and hyperlipidemia (ICD-10-CM: E78.x, E88.1). ## Exposure to CVD Medications For each of the three CVD medication groups—statins, ACEI/ARBs, and anticoagulants—patients who received at least one prescription for the medication ≤90 days prior to their COVID-19 diagnosis date were defined as exposed. ## Covariates For each case-control study, multivariable analyses were used to adjust for the following unmatched variables: Elixhauser comorbidity index score (with the six matched conditions removed)34 (0, 1, 2, ≥3), ethnicity (non-Hispanic, Hispanic), United States Census Bureau geographic region (Midwest, Northeast, South, West),35 receipt of remdesivir, and receipt of dexamethasone. ## Statistical Analysis Patient demographic and clinical characteristics for the overall COVID-19 cohort and each matched case-control study were summarized using percentages. To compare the baseline covariates between groups, standardized differences were calculated36 in the cohort of COVID-19-positive patients based on three comparisons: hospitalization status, ventilation use or admission to ICU, and mortality. For each nested matched case-control study, multivariable conditional logistic regression analysis, adjusting for each of the aforementioned unbalanced covariates, was used to calculate adjusted odds ratios (aORs) and $95\%$ confidence intervals ($95\%$ CIs) for the three outcomes: hospitalization within 30 days of COVID-19 diagnosis, ICU admission/mechanical ventilation during COVID-19 hospitalization, and death during COVID-19 hospitalization. Finally, to assess the robustness of our findings, several sensitivity analyses were conducted. First, for each medication group, analyses using prescription periods of ≤60 days and ≤120 days before the COVID-19 diagnosis date were assessed (Appendix Tables 1 and 2). Second, analyses were conducted in which patients who were in hospital networks with fewer than 100 COVID-19 patients (along with their matched pairs) were removed (Appendix Table 2). All analyses were performed using SAS version 9.4 (SAS Institute). ## Results Table 2 presents the demographic and clinical characteristics of the overall COVID-19 study cohort and each of the three primary samples used in the case-control studies. For the ventilation/ICU status group, there were larger standardized differences37 in in-hospital dexamethasone and remdesivir use. For the hospitalization status group, there was a larger standardized difference37 in the Elixhauser comorbidity index. Table 2Patient Characteristics by Hospitalization, ICU Admission/Mechanical Ventilation, and Hospital Mortality. Table 2Hospitalization statusVentilation or ICU statusDeath statusParameterLevelCases N (Column%)Controls N (Column%)Standardized differenceCases N (Column%)Controls N (Column%)Standardized differenceCases N (Column%)Controls N (Column%)Standardized differenceTotal87,75887,75821,62521,62523002300Dexamethasone Use In-HospitalNo10,069($46.6\%$)12,707($58.8\%$)0.24621082($47.0\%$)1304($56.7\%$)0.4805Yes11,556($53.4\%$)8918 ($41.2\%$)1218($53.0\%$)996 ($43.3\%$)Remdesivir Use In-HospitalNo18,848($87.2\%$)19,919($92.1\%$)0.1631756($76.3\%$)2076($90.3\%$)0.4482Yes2777 ($12.8\%$)1706 ($7.9\%$)544 ($23.7\%$)224 ($9.7\%$)EthnicityNon-Hispanic70,790($80.7\%$)71,460($81.4\%$)0.138817,398($80.5\%$)17,639($81.6\%$)0.05191839($80.0\%$)1901($82.7\%$)0.0841Hispanic10,453($11.9\%$)8214 ($9.4\%$)2549 ($11.8\%$)2440 ($11.3\%$)247 ($10.7\%$)238 ($10.3\%$)Unknown6515 ($7.4\%$)8084 ($9.2\%$)1678 ($7.8\%$)1546 ($7.1\%$)214 ($9.3\%$)161 ($7.0\%$)RegionMidwest39,564($45.1\%$)42,363($48.3\%$)0.144110,182($47.1\%$)9385 ($43.4\%$)0.1672823 ($35.8\%$)1031($44.8\%$)0.3671Northeast21,801($24.8\%$)24,282($27.7\%$)4414 ($20.4\%$)5840 ($27.0\%$)448 ($19.5\%$)588 ($25.6\%$)Other/Unknown2796 ($3.2\%$)2894 ($3.3\%$)606 ($2.8\%$)654 ($3.0\%$)54 ($2.3\%$)79 ($3.4\%$)South18,395($21.0\%$)13,614($15.5\%$)5004 ($23.1\%$)4475 ($20.7\%$)789 ($34.3\%$)456 ($19.8\%$)West5202 ($5.9\%$)4605 ($5.2\%$)1419 ($6.6\%$)1271 ($5.9\%$)186 ($8.1\%$)146 ($6.3\%$)Elixhauser Category021,824($24.9\%$)36,281($41.3\%$)0.39223406 ($15.8\%$)4751 ($22.0\%$)0.2908454 ($19.7\%$)438 ($19.0\%$)0.1579116,789($19.1\%$)17,874($20.4\%$)3090 ($14.3\%$)4334 ($20.0\%$)292 ($12.7\%$)389 ($16.9\%$)213,103($14.9\%$)11,111($12.7\%$)3106 ($14.4\%$)3476 ($16.1\%$)267 ($11.6\%$)334 ($14.5\%$)3+36,042($41.1\%$)22,492($25.6\%$)12,023($55.6\%$)9064 ($41.9\%$)1287($56.0\%$)1139($49.5\%$)GenderFemale45,138($51.4\%$)45,138($51.4\%$)09000 ($41.6\%$)9000 ($41.6\%$)0932 ($40.5\%$)932 ($40.5\%$)0Male42,620($48.6\%$)42,620($48.6\%$)12,625($58.4\%$)12,625($58.4\%$)1368($59.5\%$)1368($59.5\%$)Age CategoryMean (SD)59.7 (19.2)59.4 (19.3)0.016163.2 (16.4)63.3 (16.5)−0.002471.3 (12.8)70.9 (13.1)0.0326<181235 ($1.4\%$)1235 ($1.4\%$)238 ($1.1\%$)238 ($1.1\%$)3 ($0.1\%$)3 ($0.1\%$)18-3914,620($16.7\%$)14,620($16.7\%$)1723 ($8.0\%$)1723 ($8.0\%$)39 ($1.7\%$)39 ($1.7\%$)40-498537 ($9.7\%$)8537 ($9.7\%$)1950 ($9.0\%$)1950 ($9.0\%$)91 ($4.0\%$)91 ($4.0\%$)50-5914,172($16.1\%$)14,172($16.1\%$)3725 ($17.2\%$)3725 ($17.2\%$)257 ($11.2\%$)257 ($11.2\%$)60-6918,625($21.2\%$)18,625($21.2\%$)5699 ($26.4\%$)5699 ($26.4\%$)550 ($23.9\%$)550 ($23.9\%$)70-7916,246($18.5\%$)16,246($18.5\%$)4848 ($22.4\%$)4848 ($22.4\%$)679 ($29.5\%$)679 ($29.5\%$)80+14,323($16.3\%$)14,323($16.3\%$)3442 ($15.9\%$)3442 ($15.9\%$)681 ($29.6\%$)681 ($29.6\%$)RaceAfrican American15,640($17.8\%$)14,934($17.0\%$)0.08633802 ($17.6\%$)4015 ($18.6\%$)0.0366353 ($15.3\%$)384 ($16.7\%$)0.0747Asian2286 ($2.6\%$)1907 ($2.2\%$)609 ($2.8\%$)556 ($2.6\%$)76 ($3.3\%$)69 ($3.0\%$)Caucasian57,899($66.0\%$)57,899($66.0\%$)14,177($65.6\%$)14,177($65.6\%$)1564($68.0\%$)1564($68.0\%$)Other/Unknown11,933($13.6\%$)13,018($14.8\%$)3037 ($14.0\%$)2877 ($13.3\%$)307 ($13.3\%$)283 ($12.3\%$)COPD22,192($25.3\%$)22,192($25.3\%$)06267 ($29.0\%$)6267 ($29.0\%$)0640 ($27.8\%$)640 ($27.8\%$)0Obesity26,748($30.5\%$)26,748($30.5\%$)07540 ($34.9\%$)7540 ($34.9\%$)0706 ($30.7\%$)706 ($30.7\%$)0Atherosclerotic Cardiovascular Disease15,785($18.0\%$)15,785($18.0\%$)04645 ($21.5\%$)4645 ($21.5\%$)0604 ($26.3\%$)604 ($26.3\%$)0Angina1932 ($2.2\%$)1932 ($2.2\%$)0447 ($2.1\%$)447 ($2.1\%$)039 ($1.7\%$)39 ($1.7\%$)0Diabetes29,032($33.1\%$)29,032($33.1\%$)08971 ($41.5\%$)8971 ($41.5\%$)0919 ($40.0\%$)919 ($40.0\%$)0Hyperlipidemia37,848($43.1\%$)37,848($43.1\%$)010,765($49.8\%$)10,765($49.8\%$)01145($49.8\%$)1145($49.8\%$)0Hypertension50,827($57.9\%$)50,827($57.9\%$)014,672($67.8\%$)14,672($67.8\%$)01537($66.8\%$)1537($66.8\%$)0⁎Standardized differences were calculated using SAS version 9.4 (SAS Institute, Cary, North Carolina).36 Table 3 presents the results for each of the 3 case-control studies. For study 1, the multivariable conditional logistic regression showed that use of statins (aOR, 0.72; $95\%$ CI, 0.69, 0.75), ACEI/ARBs (aOR, 0.67; $95\%$ CI, 0.65, 0.70), and anticoagulants (aOR, 0.94; $95\%$ CI, 0.89, 0.99) was associated with a decreased risk of hospitalization. For study 2, the multivariable conditional logistic regression showed that use of statins (aOR, 0.90; $95\%$ CI, 0.84, 0.97) and ACEI/ARBs (aOR, 0.92; $95\%$ CI, 0.86, 0.99) was associated with a decreased risk in ICU admission/mechanical ventilation. For study 3, the conditional logistic regression showed that use of both ACEI/ARBs (aOR, 0.60; $95\%$ CI, 0.47, 0.78) and anticoagulants (aOR, 0.56; $95\%$ CI, 0.41, 0.77) was associated with a decreased risk of hospital mortality. Table 3Unadjusted and Adjusted Odds Ratios for Hospitalization, ICU Admission/Mechanical Ventilation, and Hospital Mortality Due to COVID-19.⁎Table 3CharacteristicCase no. (%) Control⁎⁎ no. (%) Unadjusted OR† ($95\%$ CI‡)Adjusted OR ($95\%$ CI)Case-control study predicting hospitalization Statin Use7875 ($9.0\%$)11,607 ($13.2\%$)0.61 (0.59, 0.63)0.72 (0.69, 0.75) ACEI/ARB Use7690 ($8.8\%$)12,195 ($13.9\%$)0.56 (0.55, 0.58)0.67 (0.65, 0.70) Anticoagulant Use3822 ($4.4\%$)3558 ($4.1\%$)1.08 (1.03, 1.13)0.94 (0.89, 0.99)Case-control study predicting ICU admission/mechanical ventilation Statin Use2111 ($9.8\%$)2380 ($11.0\%$)0.86 (0.81, 0.92)0.90 (0.84, 0.97) ACEI/ARB Use2051 ($9.5\%$)2289 ($10.6\%$)0.88 (0.82, 0.94)0.92 (0.86, 0.99) Anticoagulant Use1064 ($4.9\%$)1014 ($4.7\%$)1.05 (0.96, 1.15)0.96 (0.88, 1.06)Case-control study predicting in-hospital mortality Statin Use164 ($7.1\%$)233 ($10.1\%$)0.66 (0.53, 0.82)0.84 (0.65, 1.07) ACEI/ARB Use152 ($6.6\%$)238 ($10.3\%$)0.60 (0.48, 0.74)0.60 (0.47, 0.78) Anticoagulant Use75 ($3.3\%$)135 ($5.9\%$)0.55 (0.41, 0.73)0.56 (0.41, 0.77)⁎Multivariable analyses were adjusted for all unmatched variables including statin use, ACEI/ARB use, anticoagulant use, ethnicity, region, and Elixhauser score.⁎⁎Each case-control study was matched on sex, race, age group, and each of the following medical conditions: COPD, obesity, cardiovascular disease, angina, diabetes, hyperlipidemia, and hypertension.†OR, odds ratio.‡$95\%$ CI, $95\%$ confidence interval. Two-way interaction effects—assessed in the multivariable model predicting hospitalization—were statistically significant for: statins and ACEI/ARBs ($P \leq .0001$), statins and anticoagulants ($$P \leq .003$$), and ACEI/ARBs and anticoagulants ($P \leq .0001$). Likewise, a two-way drug interaction effect—assessed in the model predicting ICU admission/mechanical ventilation—was statistically significant for statins and ACEI/ARBs ($$P \leq .002$$). Stratified analysis showed that use of ACEI/ARBs was protective against hospitalization in persons who were not concomitantly taking statins (odds ratio [OR], 0.56; $95\%$ CI, 0.54, 0.59) compared to those who were (OR, 0.97; $95\%$ CI, 0.85, 1.11) or in persons who were not concomitantly taking anticoagulants (OR, 0.65; $95\%$ CI, 0.62, 0.67) compared to those who were (OR, 1.01; $95\%$ CI, 0.69, 1.48); and statins were protective against hospitalization in persons who were not concomitantly taking ACEI/ARBs (OR, 0.63; $95\%$ CI, 0.60, 0.66) compared to those who were (OR, 0.95; $95\%$ CI, 0.82, 1.11). To assess the robustness of our findings, we conducted a number of sensitivity analyses. First, logistic regression models using prescription periods of ≤60 days and ≤120 days before the COVID-19 diagnosis date were assessed (Appendix Tables 1 and 2). Next, logistic regression models in which patients who were in hospital networks with ≤100 COVID-19 patients (along with their matched pairs) were removed (Appendix Table 3) were conducted. In each of the above sensitivity analyses, the direction and magnitude of the ORs for each of the three medications under study were consistent with the overall study findings. Appendix Table 1Sensitivity Analyses Based on Prescription Period of ≤60 Days.⁎Appendix Table 1CharacteristicCase no. (%) Control⁎⁎ no. (%) Unadjusted OR† ($95\%$ CI‡)Adjusted OR ($95\%$ CI)Case-control study predicting hospitalization Statin Use5748 ($6.6\%$)8565 ($9.8\%$)0.62 (0.60, 0.65)0.72 (0.69, 0.75) ACEI/ARB Use5639 ($6.4\%$)9137 ($10.4\%$)0.57 (0.55, 0.59)0.67 (0.64, 0.70) Anticoagulant Use3035 ($3.5\%$)2758 ($3.1\%$)1.11 (1.05, 1.17)0.95 (0.90, 1.01)Case-control study predicting ICU admission/mechanical ventilation Statin Use1566 ($7.2\%$)1741 ($8.1\%$)0.88 (0.82, 0.95)0.93 (0.86, 1.01) ACEI/ARB Use1480 ($6.8\%$)1693 ($7.8\%$)0.86 (0.80, 0.93)0.88 (0.81, 0.96) Anticoagulant Use817 ($3.8\%$)805 ($3.7\%$)1.02 (0.92, 1.12)0.93 (0.84, 1.04)Case-control study predicting hospital mortality Statin Use123 ($5.4\%$)170 ($7.4\%$)0.69 (0.54, 0.89)0.89 (0.67, 1.18) ACEI/ARB Use99 ($4.3\%$)172 ($7.5\%$)0.55 (0.42, 0.71)0.58 (0.43, 0.77) Anticoagulant Use56 ($2.4\%$)103 ($4.5\%$)0.53 (0.38, 0.74)0.52 (0.36, 0.75)⁎Multivariable analyses were adjusted for all unmatched variables including statin use, ACEI/ARB use, anticoagulant use, ethnicity, region, and Elixhauser score.⁎⁎Each case-control study was matched on sex, race, age group, and each of the following medical conditions: COPD, obesity, cardiovascular disease, angina, diabetes, hyperlipidemia, and hypertension.†OR, odds ratio.‡$95\%$ CI, $95\%$ confidence interval. Appendix Table 2Sensitivity Analyses Based on Prescription Period of ≤120 Days.⁎Appendix Table 2CharacteristicCase no. (%) Control⁎⁎ no. (%) Unadjusted OR† ($95\%$ CI‡)Adjusted OR ($95\%$ CI)Case-control study predicting hospitalization Statin Use9395 ($10.7\%$)13,824 ($15.8\%$)0.60 (0.58, 0.61)0.71 (0.69, 0.74) ACEI/ARB Use9184 ($10.5\%$)14,488 ($16.5\%$)0.55 (0.53, 0.57)0.66 (0.64, 0.69) Anticoagulant Use4369 ($5.0\%$)4159 ($4.7\%$)1.06 (1.01, 1.10)0.92 (0.88, 0.96)Case-control study predicting ICU admission/mechanical ventilation Statin Use2542 ($11.8\%$)2837 ($13.1\%$)0.87 (0.82, 0.92)0.91 (0.85, 0.98) ACEI/ARB Use2486 ($11.5\%$)2734 ($12.6\%$)0.89 (0.84, 0.95)0.92 (0.86, 0.99) Anticoagulant Use1210 ($5.6\%$)1164 ($5.4\%$)1.04 (0.96, 1.13)0.94 (0.86, 1.03)Case-control study predicting hospital mortality Statin Use208 ($9.0\%$)284 ($12.3\%$)0.68 (0.56, 0.83)0.86 (0.68, 1.08) ACEI/ARB Use196 ($8.5\%$)292 ($12.7\%$)0.63 (0.52, 0.76)0.64 (0.51, 0.81) Anticoagulant Use94 ($4.1\%$)165 ($7.2\%$)0.53 (0.41, 0.70)0.54 (0.40, 0.73)⁎Multivariable analyses were adjusted for all unmatched variables including statin use, ACEI/ARB use, anticoagulant use, ethnicity, region, and Elixhauser score.⁎⁎Each case-control study was matched on sex, race, age group, and each of the following medical conditions: COPD, obesity, cardiovascular disease, angina, diabetes, hyperlipidemia, and hypertension.†OR, odds ratio.‡$95\%$ CI, $95\%$ confidence interval. Appendix Table 3Sensitivity Analyses Restricted to Patients From Health Systems With ≥100 COVID-19 Patients With Hospitalization and ≥100 COVID-19 Patients With ICU Admission/Mechanical Ventilation and Hospital Mortality.⁎Appendix Table 3CharacteristicCase no. (%) Control⁎⁎ no. (%) Unadjusted OR† ($95\%$ CI‡)Adjusted OR ($95\%$ CI)Case-control study predicting hospitalization Statin Use7870 ($9.0\%$)11,606 ($13.2\%$)0.61 (0.59, 0.63)0.72 (0.69, 0.75) ACEI/ARB Use7690 ($8.8\%$)12,195 ($13.9\%$)0.56 (0.55, 0.58)0.67 (0.65, 0.70) Anticoagulant Use3822 ($4.4\%$)3558 ($4.1\%$)1.08 (1.03, 1.13)0.94 (0.89, 0.99)Case-control study predicting ICU admission/mechanical ventilation Statin Use2111 ($9.8\%$)2379 ($11.0\%$)0.86 (0.81, 0.92)0.90 (0.84, 0.97) ACEI/ARB Use2051 ($9.5\%$)2289 ($10.6\%$)0.88 (0.82, 0.94)0.92 (0.86, 0.99) Anticoagulant Use1064 ($4.9\%$)1014 ($4.7\%$)1.05 (0.96, 1.15)0.96 (0.88, 1.06)Case-control study predicting hospital mortality Statin Use164 ($7.1\%$)233 ($10.1\%$)0.66 (0.53, 0.82)0.84 (0.65, 1.07) ACEI/ARB Use152 ($6.6\%$)238 ($10.3\%$)0.60 (0.48, 0.74)0.61 (0.47, 0.78) Anticoagulant Use75 ($3.3\%$)135 ($5.9\%$)0.55 (0.41, 0.73)0.56 (0.41, 0.77)⁎Multivariable analyses were adjusted for all unmatched variables including statin use, ACEI/ARB use, anticoagulant use, ethnicity, region, and Elixhauser score.⁎⁎Each case-control study was matched on sex, race, age group, and each of the following medical conditions: COPD, obesity, cardiovascular disease, angina, diabetes, hyperlipidemia, and hypertension.†OR, odds ratio.‡$95\%$ CI, $95\%$ confidence interval. ## Discussion In this matched nested case-control study of a national cohort of patients diagnosed with COVID-19, we found that the use of statins, ACEI/ARBs, and anticoagulants was protective against a number of adverse COVID-19 outcomes. Using matching and multivariable adjustment, we accounted for multiple potentially confounding factors, including sex, age, race, ethnicity, region, and underlying medical conditions and medications. Moreover, our primary findings persisted across multiple sensitivity analyses, using exposure periods that extended to 60 and 120 days before the COVID-19 diagnosis and restricting to a cohort of patients treated in health systems with a high volume of COVID-19 patients. It is likely that the observed protective effects on COVID-19 by statins are attributable, in some measure, to their antiinflammatory, antithrombotic, and immunomodulatory effects via a reduction in viral replication and suppression of the damaging effects of cytokine storm and microvascular and macrovascular thrombosis.8,11, 12, 13, 14 Zhang et al. showed an association between statin use and decreased risk of mortality and ventilator use, with lower levels of inflammatory markers among statin users.8 However, observational studies, randomized controlled trials, and metaanalyses have produced conflicting data on the utility of statin use for COVID-19 clinical outcomes.8,11,38, 39, 40, 41, 42, 43, 44, 45, 46 While a* number of studies reported that the use of statins was associated with lower mortality rates and decreased inflammatory response in hospitalized COVID-19 patients,8,39,41,42,44 especially critically ill patients,11 other studies did not support the use of statins for hospitalized COVID-19 patients.45,47 Cariou et al. found that statin treatment was associated with increased mortality in a cohort of 2449 hospitalized patients with both type 2 diabetes mellitus and COVID-19,38 and one randomized controlled trial reported that atorvastatin use significantly increased the frequency of ICU admission and length of stay at the hospital for inpatients with COVID-19.45 The protective effects of ACEI/ARBs were possibly driven, to some degree, by antiinflammatory effects in COVID-19 patients,15,16 specifically by inhibiting ACE2 downregulation-induced hyperactivation of the RAAS caused by the SARS-CoV-2 virus.7 By attenuating the deleterious effects of angiotensin II, ACEI/ARBs may reduce oxidative stress, vasoconstriction, and inflammation in the lungs.17 Notably, there is concern that critical biases may have distorted the evidence in past observational research.48 Further, Bauer et al. reported that older, vulnerable patients might benefit from temporary discontinuation of RAAS inhibition for improved recovery from COVID-19.49 Given that coagulopathy—which commonly occurs as a result of COVID-19-related systemic thrombo-inflammation and results in a consequent risk of vascular permeability and hypercoagulation—is associated with increased disease severity and mortality, there has been interest in examining the effect of anticoagulants in COVID-19 patients. Postmortem examinations of COVID-19 patients showed thrombosis in pulmonary vessels and other organs without SARS-CoV-2 penetration.27,50 The observed protective effects of anticoagulants in this study were generally consistent with some past studies28,29,31,51 but inconsistent with others,6,32,33 including a randomized controlled trial by Connors et al. that was terminated early.30 Overall, our observation that the outpatient use of statins, ACEI/ARBs, and anticoagulants was protective against adverse outcomes in patients diagnosed with COVID-19 is consistent with a review conducted by Alves et al.52 and previous studies that assessed premorbid use of these classes of cardiovascular medications.53, 54, 55, 56, 57 It is important to note that ours is, to our knowledge, the largest matched, national comparative analysis of cardiovascular medications and COVID-19 outcomes thus far. The results of our study may have been influenced by several limitations. First, medication use was defined by written prescriptions identified in the patient's EHR prior to COVID-19 diagnosis. It is possible that this record did not include information on over-the-counter (OTC) medications and thus contained no information on coadministration of these drugs. Many OTC medications, including aspirin and nonsteroidal antiinflammatory drugs, may influence the study outcomes and these data were not available in our database. Second, reliance on EHR data precluded assessment of a number of potential confounding factors such as diet, alcohol, medication adherence, lifestyle habits, prior state of health, and other health behaviors. Medication adherence to statins in particular has been recognized as a global challenge.58 Using EHR data also leads to a certain degree of misclassification by relying on codes in administrative claims data, but we attempted to minimize this by using standardized categorization algorithms, as well as multiple sensitivity analyses. Third, our data provide information on the date the medication was prescribed but not on the date it was filled, purchased, or picked up by the patient. It is possible, therefore, that some of the drug exposure periods used in this study underestimated the true medication exposure period. Our sensitivity analyses assessing 60- and 120-day exposure windows, however, help to address this issue. Despite these limitations, this study has a number of strengths, including a large nationally representative sample, matching based on sociodemographic and disease risk factors, simultaneous adjustment for potentially confounding medical conditions and medications, and assessment of multiple exposure windows. This large, national matched comparative analysis of cardiovascular medications and COVID-19 outcomes addresses a critically important clinical and public health issue. Our findings also suggest the need for a large randomized comparative effectiveness trial of statin, ACEI/ARB, and anticoagulant therapies alone or in combination as potential therapies for COVID-19 patients, regardless of history of ASCVDs. Other areas of study include examining comparative effectiveness of different statins—based on their lipophilic versus hydrophilic property—in COVID-19 patients as well as the effect of dose and duration with statins, ACEI/ARBs, and anticoagulants. ## Conclusion Our study suggests that all three medications studied—statins, ACEI/ARBs, and anticoagulants—were associated with decreased risks of the adverse outcomes under study. Even with the increased availability of COVID-19 vaccines and treatments, immunocompromised and elderly patients with COVID-19 remain at risk for severe adverse outcomes. As of February 2023, COVID-19 is still a major contributor to premature deaths in the United States, with an approximately 21-day average of 452 new COVID-19 deaths per day.59 Our findings may help to guide clinical decision-making on choices of cardiovascular medications to continue or adjust for COVID-19 patients with multimorbidity and ASCVD. ## Role of the Sponsors The funding organizations had no role in the design or conduct of the study; in the collection, analysis, or interpretation of data; or in the preparation, review, or approval of the manuscript. ## CRediT authorship contribution statement Leonard K. Wang: Conceptualization, Writing – original draft, Writing – review & editing, Visualization. Yong-Fang Kuo: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. Jordan Westra: Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Mukaila A. Raji: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Funding acquisition. Mohanad Albayyaa: Writing – original draft, Writing – review & editing. Joseph Allencherril: Writing – original draft, Writing – review & editing. Jacques Baillargeon: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. ## Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ## Appendix Appendix Tables 1–4Appendix Table 4LOINC Codes for Positive COVID-19 Laboratory Test. Appendix Table 494306-8SARS-CoV-2 RNA panel, unspecified specimen94307-6SARS-CoV-2 N gene, unspecified specimen94308-4SARS-CoV-2 N gene, unspecified specimen94309-2SARS-CoV-2 RNA, unspecified specimen94311-8SARS-CoV-2 N gene, unspecified specimen94312-6SARS-CoV-2 N gene, unspecified specimen94314-2SARS-CoV-2 RdRp gene, unspecified specimen94316-7SARS-CoV-2 N gene, unspecified specimen94500-6SARS-CoV-2 RNA, respiratory specimen94503-0SARS-CoV-2 IgG and IgM panel, serum or plasma94504-8SARS-CoV-2 IgG and IgM panel, serum or plasma94505-5SARS-CoV-2 IgG, serum or plasma94506-3SARS-CoV-2 IgM, serum or plasma94507-1SARS-CoV-2 IgG, serum or plasma94508-9SARS-CoV-2 IgM, serum or plasma94510-5SARS-CoV-2 N gene, unspecified specimen94511-3SARS-CoV-2 ORF1ab region, unspecified specimen94531-1SARS coronavirus 2 RNA panel, respiratory specimen94533-7SARS-CoV-2 N gene, respiratory specimen94534-5SARS-CoV-2 RdRp gene, respiratory specimen94547-7SARS-CoV-2 IgG+IgM, serum or plasma94558-4SARS-CoV-2 Ag, respiratory specimen94559-2SARS-CoV-2 ORF1ab region, respiratory specimen94562-6SARS-CoV-2 IgA, serum or plasma94563-4SARS-CoV-2 IgG, serum or plasma94564-2SARS-CoV-2 IgM, serum or plasma94565-9SARS-CoV-2 RNA, nasopharyngeal specimen94639-2SARS-CoV-2 ORF1ab region, unspecified specimen94640-0SARS-CoV-2 S gene, respiratory specimen94641-8SARS-CoV-2 S gene, unspecified specimen94642-6SARS-CoV-2 S gene, respiratory specimen94643-4SARS-CoV-2 S gene, unspecified specimen94644-2SARS-CoV-2 ORF1ab region, respiratory specimen94645-9SARS-CoV-2 RdRp gene, unspecified specimen94646-7SARS-CoV-2 RdRp gene, respiratory specimen94660-8SARS-CoV-2 RNA, serum/plasma94661-6SARS-CoV-2 antibody interpretation94720-0SARS-CoV-2 IgA, serum or plasma94745-7SARS-CoV-2 RNA, respiratory specimen94746-5SARS-CoV-2 RNA, unspecified specimen94756-4SARS-CoV-2 N gene, respiratory specimen94757-2SARS-CoV-2 N gene, respiratory specimen94759-8SARS-CoV-2 N overall result, nasopharyngeal specimen94760-6SARS-CoV-2 N gene result, nasopharyngeal specimen94761-4SARS-CoV-2 IgG Ab, DBS94762-2SARS-CoV-2 Ab qualitative94763-0SARS-CoV-2, unspecified specimen94764-8SARS-CoV-2 whole genome94766-3SARS-CoV-2 N gene, serum or plasma94767-1SARS-CoV-2 S gene, serum or plasma94768-9SARS-CoV-2 IgA, serum or plasma94769-7SARS-CoV-2 IgA, serum or plasma94819-0SARS-CoV-2 N gene, unspecified specimen94822-4SARS-CoV-2 RNA, saliva94845-5SARS-CoV-2 RNA, saliva95125-1SARS-CoV-2 RNA, serum or plasma95406-5SARS-CoV-2 RNA, nose95409-9SARS-CoV-2 N gene, nose95410-7SARS-CoV-2 neutralizing antibody, serum95411-5SARS-CoV-2 neutralizing antibody, serum95416-4SARS-CoV-2 IgM Ab, DBS95424-8SARS-CoV-2 RNA, respiratory specimen95425-5SARS-CoV-2 N gene, saliva95427-1SARS-CoV-2 IgA Ab, serum or plasma95428-9SARS-CoV-2 IgM Ab, serum or plasma95429-7SARS-CoV-2 IgG Ab, serum or plasma95521-1SARS-CoV-2 N gene, respiratory specimen95522-9SARS-CoV-2 N gene, respiratory specimen ## References 1. 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--- title: Exercise and Physical Activity Levels and Associated Factors Among High-Risk Pregnant Women authors: - Larissa Antunes Miranda - Anna Caroline Ribeiro de Moura - Karina Tamy Kasawara - Fernanda Garanhani Surita - Mayle Andrade Moreira - Simony Lira do Nascimento journal: RBGO Gynecology & Obstetrics year: 2022 pmcid: PMC10032053 doi: 10.1055/s-0042-1743099 license: CC BY 4.0 --- # Exercise and Physical Activity Levels and Associated Factors Among High-Risk Pregnant Women ## Abstract Objective To assess the levels of physical activity and exercise practice, and examine the associated maternal characteristics; as well as the anxiety levels of high-risk pregnant women. Methods A cross-sectional study conducted with pregnant women at a High-risk Prenatal Clinic (HRPC) in a tertiary maternity. Pregnant women of 18 to 40-years-old, with a single fetus, and with gestational age up to 38 weeks were included. The level of physical activity and exercise practice of the study's participants were investigated using the Pregnancy Physical Activity Questionnaire (PPAQ). Maternal sociodemographic, anthropometric, and medical data were investigated using a specific form. For anxiety levels, the short version of the State-Trait Anxiety Inventory (STAI) was applied. We used the Student t -test, chi-square test, odds ratio (OR) with $95\%$ confidence interval ($95\%$ CI) and multiple logistic regression. The significance level was $5\%$. Results Among the 109 pregnant women included, 82 ($75.2\%$) were classified as sedentary/little active. The higher energy expenditure were for domestic activities (133.81 ± 81.84 METs), followed by work-related activities (40.77 ± 84.71 METs). Only $19.3\%$ women exercised during pregnancy (4.76 ± 12.47 METs), with slow walking being the most reported exercise. A higher level of education was the most important factor associated with women being moderately or vigorously active (OR = 29.8; $95\%$ CI 4.9–117.8). Nulliparity (OR = 3.1; $95\%$ CI 1.0–9.1), low levels of anxiety (OR = 3.6; $95\%$ CI 1.2–10.7), and unemployment (OR = 4.8; $95\%$ CI 1.1–19.6) were associated with the practice of exercise during pregnancy. Conclusion Most women with high-risk pregnancies exhibited a sedentary pattern, with low prevalence of physical exercise practice. Recognizing factors that hinder the adoption of a more physically active lifestyle is essential for an individualized guidance regarding exercise during pregnancy. ## Introduction In 1985, a guideline with recommendations regarding physical activity during pregnancy was published for the first time by the American College of Obstetricians and Gynecologists (ACOG), though they are now considered conservative. Since then, the practice of physical activity and exercise during pregnancy has gained notoriety, due to its potential benefits to maternal and fetal health. 1 2 3 4 Given this context, it is essential to properly differentiate the concepts of physical activity and exercise. According to Caspersen et al., 5 physical activity is related to any body movements performed by skeletal muscles, in which energy expenditure is above basal, such as labor, domestic, and leisure activities. On the other hand, exercise is defined as a planned, structured and repetitive physical activity, which is desired to achieve improvement or maintenance of physical fitness. Although the numerous maternal and fetal health benefits of an active pregnancy are recognized, several studies in Brazil demonstrate alarming data on sedentary behavior among women with habitual risk pregnancies, and the restriction of physical activities is even more reinforced for high-risk pregnant women. 6 7 8 9 10 The evaluation of energy expenditure and knowledge of the physical activities in which pregnant women participate allows for a better understanding of the women's profile and an adequate exercise prescription by health professionals. In this context, the application of questionnaires to measure the level of physical activity and exercise practice is a valid and useful tool, in the absence of more objective methods, such as direct or indirect calorimetry, accelerometers, and electronic movement sensors. The Pregnant Physical Activity Questionnaire (PPAQ) has been proven to be effective, since it addresses activities which are frequently present in the daily lives of pregnant women, such as domestic, sports, and work-related activities. Thus, it can be applied to women with low and high-risk pregnancies. 9 11 12 Several conditions can make a pregnancy high risk, including biological factors (health conditions, chronic diseases, mother's age, and nutritional and genetic aspects), psychosocial (lifestyle, emotional disturbance, and relationships), social aspects (prenatal negligence and social vulnerability), and clinical or obstetric complications that happen during pregnancy. Thus, high-risk pregnancy is defined here as any medical or obstetric conditions associated with a pregnancy with an actual or potential hazard to the health or well-being of the mother or fetus that requires specialized care. 13 14 15 Chronic diseases such as diabetes mellitus and arterial hypertension, as well as overweight and obesity during pregnancy are described as contributing factors to the low adherence of women to activities with higher energy expenditure. Other reasons that also contribute to a less active lifestyle during pregnancy are: lack of infrastructure (for example, parks and places to walk), number of children at home, other occupations that limit time, little family incentive, perception of safety in public spaces. Furthermore, psychological changes, such as anxiety and depression, can be barriers capable of hindering practice of physical activities and exercise. Among sociodemographic factors, lower educational levels and income, and higher number of children at home are most frequently associated with reduced physical activity. 6 16 17 As sedentary behaviors seem to be even more reinforced to high-risk pregnant women, we hypothesized that women with high-risk pregnancies would have a sedentary profile. Thus, this study aimed to assess the physical activity and exercise practice levels of high-risk pregnant women and to examine the maternal characteristics associated with exercising and level of physical activity during pregnancy. 7 ## Methods This is a cross-sectional study involving pregnant women attended at Maternal Fetal Medicine Service (MFMS) and High-Risk Prenatal Clinic (HRPC) of the Maternity Hospital School Assis Chateaubriand (MEAC) of the Federal University of Ceará, a reference center in maternal and childcare, in Fortaleza, Ceará, Brazil. Our facility provides care for women and newborns from the least favored segment of the population of Ceará and the Northeastern region. Data collection was performed from August 2017 to July 2019. Eligibility criteria were pregnant women between 18 and 40 years old, single fetus, gestational age up to 38 weeks, who were attended in the service at the HRPC. High-risk pregnancy is defined as any medical or obstetric condition related to pregnancy, with an actual or potential hazard to the health or well-being of the mother or fetus, and requires specialized care. 13 14 15 Pregnant women with absolute contraindication to perform physical activity during pregnancy according to ACOG criteria were excluded (hemodynamically significant heart disease, restrictive lumbar disease, incompetent cervix or cerclage, multiple pregnancy at risk of premature birth, premature labor during the current pregnancy, rupture of membranes, pre-eclampsia, and severe anemia). 18 During antenatal care (ANC) visits, women who met the eligibility criteria were invited to participate of study. Following consent, women were interviewed using standardized questionnaires: a questionnaire developed by the researchers regarding patients' socioeconomic status (age, self-reported skin color/race, marital status, educational degree, monthly family income, employment status), anthropometric data (weight, height, and pre-pregnancy body mass index [BMI]), the State-Trait Anxiety Inventory (STAI) short version, and additional data on participants' obstetric history, such as parity, gestational age, and pregnancy comorbidities were collected from medical records and prenatal care cards—independent variables. Then, all patients answered a questionnaire on physical activities, including daily amount of physical activity and exercise practice, specifically for pregnant women—dependent variables. We assumed physical activity was any voluntary, corporal movement that increased the metabolism above its resting rate, such as labor, domestic, and leisure activities. And exercise was defined as structured, planned, and repetitive physical activity intended to promote health and maintain one or more components of physical ability. 5 For both outcomes, this study used the PPAQ validated for Brazilian Portuguese. The PPAQ requests respondents to report the time spent participating in 31 activities including household (5 activities), caregiving (6 activities), occupational (5 activities), sports/exercise (9 activities), transportation (3 activities), and inactivity (3 activities). 17 18 For the classification of the level of physical activity, the calculation of the energy expenditure in the metabolic equivalent of task (MET) was performed for each domain of physical activity (locomotion, leisure, domestic and occupational activities) based on the type, duration, and frequency of physical activity. The total daily energy expenditure, used to classify the pregnant woman in levels of physical activity (sedentary, little active, moderately active, and vigorously active), was calculated according to the FAO/WHO/UNU [2001] criteria. In this calculation, it is considered that the minimum expenditure of the subjects is equal to their baseline, that is, a MET multiplied by 24 hours (MET-h). The level of physical activity is considered the total energy expenditure expressed as a multiple of the daily basal metabolic rate, based on the ratio: calculated total daily MET / 24 MET. For this reason, we categorized the level of physical activity of the pregnant woman as following: sedentary / little active (≤ 1.69), moderately active (1.70–1.99) and vigorously active (> 2.00). For the analysis, we categorized women in little active versus moderately and vigorously active. 19 The prevalence of exercise was assessed using questions 18 to 26 of the PPAQ (sports/exercise), referring to different types of exercise. To classify the level of anxiety, the short version of the State-Trait Anxiety Inventory (STAI) validated for the Brazilian Portuguese was applied. To calculate the total STAI score (range 20–80) the reverse score of the positive items (calm, at ease, and relaxed) was added to the score of the negative items (tense, nervous, and worried), the result was then multiplied by $\frac{20}{6.}$ Following the classification established by Araújo et al., 20 women with a score ≤ 40 were classified as having a low level of anxiety, and those with a score greater than 40 with a high level. 20 21 22 To minimize information bias, all data were collected in a private environment through a standardized interview by previously trained evaluators. The data were analyzed using the Statistical Package Social Sciences (SPSS, IBM Corp. Armonk, NY, USA) software. Continuous variables are presented as mean (M) and standard deviation (SD), and categorical outcomes in absolute and relative frequencies. To identify the factors associated with the level of physical activity (categorized in sedentary/little vs. moderately/vigorously) and the practice of exercise (sedentary vs. active), either the Chi-square or Fisher exact tests were performed, followed by a multiple logistic regression. Odds ratio (OR) with $95\%$ confidence interval ($95\%$ CI) is present for regression. The level of significance adopted was $5\%$. This study was approved by the Research Ethics Committee of the Federal University of Ceará - CEP / UFC / PROPESQ number 2.474.018 (CAAE; 62916616.0.2002.5050). All participants signed a consent form confirming their agreement to participate and received a copy of it signed by the main researcher. ## Results Of the 148 pregnant women screened at the high-risk prenatal center, 111 met the inclusion criteria, but 2 did not accept to participate in the research, thus constituting a sample of 109 participants. Of the 37 excluded pregnant women, 14 had an absolute contraindication to the practice of physical activity due to preterm labor, cervical incompetence, and vaginal bleeding, 9 were not aged between 18 and 40 years old, and the others had a gestational age greater than 38 weeks. The analysis of participants' demographic characteristics showed an average age of 29.5 years (±5.66), most of the participants referred to themselves as brown ($87.2\%$), had studied up to a high school educational level ($47.7\%$), and were residing with a partner ($93.5\%$). More than $60\%$ of the pregnant women did not work during pregnancy and had low monthly income of approximately a minimum wage or less. Regarding the anthropometric, obstetric, and gynecological profile of the patients, the majority was classified as overweight ($32.1\%$) or obese ($36.7\%$). They were also predominantly multiparous ($70.6\%$) with a mean of 28.8 ± 6.8 weeks of pregnancy. Diabetes mellitus ($38\%$) and hypertensive syndromes ($32.4\%$) were the most prevalent comorbidities in the current pregnancy. The results of the PPAQ demonstrated that the pregnant women in this study had a higher average energy expenditure in domestic activities and lower expenditure in exercise practice. Regarding the METs results related to the intensity classification of the activities performed, there is a greater predominance of energy expenditure in light and sedentary activities practiced by pregnant women, with lower average energy expenditure in moderate and vigorous activities. Thus, $75.2\%$ of our sample were considered sedentary/not very active. The prevalence of exercise practice during pregnancy was $19.3\%$, with slow walking being the most reported activity (Table 1). Table 1 \| Physical activity end exercise variables \| ( n = 109) \| \| --- \| --- \| \| Physical activity intensity (MET-h/week) Sedentary \| Mean ± SD64.81 ± 34.55 \| \| Mild \| 127.38 ± 63.85 \| \| Moderate \| 37.60 ± 51.38 \| \| Vigorous \| 1.15 ± 5.41 \| \| Type of activity (MET-h/week) \| Mean ± SD \| \| Sports/exercise \| 4.76 ± 12.47 \| \| Occupational \| 40.77 ± 84.71 \| \| Household/caregiving \| 133.81 ± 81.84 \| \| Physical activity level classification \| N (%) \| \| Sedentary/Slightly active \| 82 (75.2%) \| \| Moderately active \| 10 (9.2%) \| \| Vigorously active \| 17 (15.6%) \| \| Prevalence of physical exercise \| 21 (19.3%) \| \| Type of physical exercise \| \| \| Slow walk for leisure \| 19 (17.4%) \| \| Quick walk for leisure \| 5 (4.6%) \| \| Other activities* \| 8 (7.3%) \| The analysis of anxiety levels according to the STAI-6 questionnaire shows the mean level of anxiety was 47.61 ± 12.63. Most of the pregnant women ($$n = 72$$; $66.1\%$) had a total score greater than 40, showing high levels of anxiety, while only $33.9\%$ of the women were classified as having a low level of anxiety. Among the analyzed factors related to the level of physical activity reached by patients, bivariate analysis showed that higher education level, employment, and an adequate pre-pregnancy BMI were associated with higher performance of activities with greater energy expenditure ($p \leq 0.05$) (Table 2). However, on logistic regression only the higher education level factor remained significative (OR = 29.8; $95\%$ CI 4.9–177.8) ($p \leq 0.001$) (Table 3). The bivariate analysis (Table 4) and logistic regression model (Table 5) for exercise as the outcome showed that not working during pregnancy (OR = 4.8; $95\%$ CI 1.1–19.6), nulliparity (OR = 3.1; $95\%$ CI 1.05–9.1), and low levels of anxiety (OR = 3.6; $95\%$ CI 1.2–10.7) were associated with exercise throughout pregnancy ($p \leq 0.05$) (Table 4). ## Discussion The findings of this study show that high-risk pregnant women have a preferentially sedentary lifestyle, with a predominance of energy expenditure in domestic activities. Furthermore, the prevalence of exercise during pregnancy is quite low, with only $19.3\%$ of this study's participants reporting some practice. Slow walking was the most common type of activity. The factors associated with higher levels of physical activity are working during pregnancy and higher level of education, whereas the factors associated with exercising during pregnancy are nulliparity, unemployment, and a low level of anxiety. Studies conducted in different countries, including Brazil, reveal that women tend to reduce the level, intensity, and duration of exercise during pregnancy, corroborating the findings presented in this article. Other studies of our research group in Southern Brazil found an even lower prevalence than the one found in the present research: $14.8\%$ of women were active before pregnancy and $12.9\%$ during pregnancy. The prevalence decreased throughout pregnancy, and only $4.3\%$ of the participants remained active until the end of the pregnancy. 9 23 24 25 To assess the physical activity levels, Silva 19 validated the PPAQ with pregnant Brazilian women and found that $80\%$ of the women either performed mild-intensity activities or were sedentary, and that the mild-intensity activities tended to increase during pregnancy, whereas the moderate activities decreased. It is noteworthy that we did not find studies conducted entirely with pregnant women in high-risk prenatal care. We have hypothesized that the recommendations for absolute and relative rest are further reinforced for pregnant women with comorbidities. Despite the fact that the practice of exercise plays a fundamental role in the prevention of comorbidities associated with a sedentary lifestyle, such as diabetes mellitus and arterial hypertension; it also prevents excess weight gain and postpartum weight retention. Recent guidelines make clear the absolute contraindication to exercise during pregnancy such as: ruptured membranes, premature labour, unexplained persistent vaginal bleeding, placenta praevia after 28 weeks’ gestation, pre-eclampsia, incompetent cervix, intrauterine growth restriction, high-order multiple pregnancy (eg, triplets), uncontrolled type I diabetes, uncontrolled hypertension, uncontrolled thyroid disease, other serious cardiovascular, respiratory or systemic disorder. 1 6 Several studies have showed walking is the most common type of physical activity practiced by pregnant women. Walking becomes the most accessible exercise, since it is easily integrated into the daily routine, and it does not require equipment or payment to be performed. Another possible reason is the traditional belief that walking during pregnancy is safe and can make delivery easier. However, in addition to aerobics, the more recent guidelines recommend the practice of strength exercises during pregnancy. 1 6 26 27 Many studies seek to understand which are the barriers and the facilitators for the practice of exercises and for a higher level of physical activity amidst pregnant women. The present study shows that pregnant women with a higher educational level are associated with higher energy expenditure. Women with low educational level seem to have beliefs related to poor diets and physical inactivity, such as: activities associated with daily life can replace activities with greater energy expenditure. 16 26 27 28 29 30 Regarding parity, a systematic literature review identified that pregnant women with at least one child tend to interrupt the practice of sports and exercises during pregnancy when compared with women who do not have children. Nevertheless, while pregnant women who already have one or more children report less time to exercise, they have a higher overall energy expenditure due to the increased activities of daily living, such as playing with children and household activities. 17 Still, some pointed out that women who work outside the house during pregnancy tend to have greater purchasing power, which reflects in healthier behaviors, such as choosing more nutritious foods and maintaining the frequency of exercise. Other studies showed that women who are not employed are more likely to comply with exercise guidelines compared with employed women. A possible explanation for this situation is the greater time availability that unemployed women have, hence including the practice of exercises into their daily routine more easily, as we found in our population. 8 18 27 29 30 31 Maternal BMI has also been associated with physical activity when compared with pre-pregnancy levels. A multicenter cohort study revealed that pregnant women with a BMI greater than 25 kg/m 2 cease moderate to intense physical activity during pregnancy more frequently than women with a “normal” weight (BMI 18.5–24.99 kg/m 2), who continued with moderate to vigorous activities. A possible explanation for obese and overweight pregnant women involved in less strenuous activities is negative body image and lower self-efficacy in relation to physical activities with higher energy expenditure. 16 18 24 25 31 32 33 34 Another factor that we studied was maternal anxiety. The study performed by Araújo et al. 20 with pregnant women in Rio de Janeiro found a similar result to the present study: $64.9\%$ of these women had high levels of anxiety when answering the STAI questionnaire. 20 35 36 37 Two systematic reviews revealed low evidence about the effect of exercise on reducing anxiety symptoms during pregnancy. However, pregnant women who experience higher levels of anxiety tend to reduce self-care and have low adherence to healthy lifestyle habits, therefore, choosing more caloric foods and not exercising. Thus, anxiety symptoms should be routinely screened in pregnant women, mainly those with risk-pregnancies, to help women deal with pregnant related issues. 32 35 36 38 Our results highlight the need for a multidisciplinary approach during pre-natal care to reinforce the adoption of health-related behaviors during pregnancy, since modifiable factors such as mental health, nutritional status and physical activity patterns have been associated with better perinatal outcomes, which is the main goal of a pre-natal service. 39 As a limitation, the present study was carried in a single reference center with women from low socioeconomic level, which prevents us from generalizing our results for all high-risk pregnant women in Brazil. We used a questionnaire to assess the pattern of activity and exercise, which can generate an information bias. However, the questionnaire is a validated one, and it is also widely applied in national and international studies. Another limitation is the short period of time covered by the questionnaire which is, in this case, of three months; such period is insufficient to cover the entire pregnancy, leading to the need for longitudinal studies. However, this is the first study that includes only high-risk pregnant women evaluating both physical activity and exercise practice levels, and presenting a robust analysis of related factors. Thus, it brings some advance to the knowledge of this theme. 8 9 10 12 ## Conclusion It is observed that high-risk pregnant women adopt preferentially sedentary activities, with a predominance of energy expenditure in domestic activities. Also, the practice of exercise is greatly reduced among high-risk pregnant women. It is also noted that a higher level of education was the most important factor associated with practice of activities with greater energy expenditure. Additionally, nulliparity, unemployment, and low levels of anxiety were associated with the practice of exercise. In view of our findings, it is plausible to acknowledge that the reduction in the practice of physical activities and exercise throughout the gestational period is a reality that involves pregnant women at habitual risk and those at high-risk. Considering the recognized benefits of exercise on maternal and fetal health, a multidisciplinary team should be engaged in prenatal care to encourage pregnant women to practice exercise properly and safely. Therefore, recognizing the factors that facilitate and hinder the adoption of a more physically active lifestyle is fundamental for an individualized orientation, manly for high-risk pregnant women. ## References 1. Exercise during pregnancy and the postpartum period. 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--- title: Prevalence of Depression in Pregnant Women with Bariatric Surgery History and Associated Factors authors: - Andréa Christina Nowak da Rocha - Ana Cristina Barros da Cunha - Jaqueline Ferreira da Silva journal: RBGO Gynecology & Obstetrics year: 2022 pmcid: PMC10032054 doi: 10.1055/s-0042-1742682 license: CC BY 4.0 --- # Prevalence of Depression in Pregnant Women with Bariatric Surgery History and Associated Factors ## Abstract Objective To analyze the prevalence and factors associated with depressive symptoms among Brazilian pregnant women with history of bariatric surgery (BS). Methods This is a cohort study with 247 women who got pregnant after BS. Based on data collection via Google Form, the recruitment of participants occurred in Facebook groups for 13 months. All of them answered a form with Informed Consent, a general data protocol and the Brazilian version of the Depression, Anxiety and Stress Scale-21. Descriptive and inferential analysis were performed, and a binary logistic regression model was tested to predict the factors associated with depressive symptoms. Results The prevalence of depressive symptoms was $32.8\%$, noted as being higher in the first ($40.6\%$) and third ($34.3\%$) gestational trimesters. Significative associations were found between depression and marital status ($$p \leq 0.000$$), planned pregnancy ($$p \leq 0.001$$), desired pregnancy ($$p \leq 0.004$$) and psychiatric history ($$p \leq 0.000$$). Women who were not married (odds ratio, OR = 3,38; $$p \leq 0.002$$) and had a psychiatric history (OR = 2.70; $$p \leq 0.102$$) had higher chances of showing depression symptoms; while planned and desired pregnancy showed as protective factors to the symptoms of depression. Conclusion These findings highlight the importance of psychological assistance for pregnant women with history of BS, to prevent development of mental disorders and their outcomes for maternal-child health. ## Introduction Obesity is a chronic disease which involves genetic, metabolic, behavioral, environmental, cultural, and psychological factors. It is characterized by excess body fat due to energy imbalance for a long period, either by an excessive fat consumption, or by sedentarism or lack of physical activity. 1 The most recent data of Brazilian's Ministry of Health show that $55.7\%$ of the Brazilian population is overweight, in other words, closer to obesity. 2 The rise in obesity in Brazil is justified by the behavioral, social, and dietary transformation of its population. According to Garcia, 3 the urbanization and industrialization binomial is a predominant factor in the way that people eat, since demand for bad and practical eating, as well as fast foods have increased. This demand makes space for higher consumption of invariable foods, without “real food” that can be found as fast food. Bariatric surgery (BS) has been embraced as an effective option as a preventive and therapeutic intervention to obesity treatment and its related diseases, such as infertility. 4 5 In accordance with the $\frac{424}{2013}$ 4 decree of Ministry of Health and the new Brazilian Guidelines of Obesity [2016], 5 BS candidates must show a body-mass index (BMI) higher than 40 kg/m 2 or 35 kg/m 2, be associated with one or more serious comorbidities caused by obesity, and have attempted, unsuccessfully, non-surgical techniques for losing weight in a prior period of at least two years. 4 5 The greatest benefit of this surgery procedure is the reduction of comorbidities, although it emphasizes gains with regard to self-esteem, sexuality and social interaction among patients. 6 In addition to the search for better health conditions, women who underwent BS can also have the urge to conform with a cultural parameter of slimness, in other words, a female physical ideal. However, the fast physical, nutritional, and psychosocial changes which occur after BS, aside from the newest silhouette (excessive or suddenly thin) may cause psychological disorders, such as depression, anxiety, alcoholism, bulimia, and anorexia, as well as other compulsive behaviors/disorders like compulsive gambling, shopping, and hypersexuality. 6 7 Moreover, it stands out that $80\%$ of the patients who answered the survey were obese women between 18 and 45-years-old. 8 9 In this population, ovarian polycystic syndrome and infertility are usually found, causing more difficulty to become pregnant. 9 Studies reveal the weight loss resulting from BS has a positive impact to female fertility, with better obstetric prognosis and reduction of obstetric complications associated with overweight, such as gestational diabetes, hypertension, preeclampsia, thromboembolism, fetal macrosomia, urinary infection, prematurity, intrauterine fetal death, anesthetic, and surgical complications. 9 10 Nevertheless, women who did BS must be warned about the minimum time to become pregnant after surgery, with the aim to wait for weight stabilization due to deficiency of vitamin, mineral, and protein absorption resulting from digestive physiology changes. 6 9 This time varies from 12 to 18 months, and it corresponds to a minimum hiatus for a post-BS pregnancy with less maternal-fetal risks. 6 9 11 Therefore, the risks arising from physical, psychological, and metabolic alterations caused by BS add up to a high vulnerability to mental disorders during the pregnancy-puerperal cycle, especially on the first and third trimesters of pregnancy, and on 30 days postpartum (puerperium). 12 13 During gestation, this can be explained by issues and fears related to the adjustment period to pregnancy and the proximity to childbirth. 13 14 Besides that, the challenges of the physical and mental transformations related to gestation result in greater emotional ambivalence that must require the women's mental effort to take on a new role as mothers, 15 which can be harder to women with BS history because of the adaptations in physical and mental levels that result from the surgery. Despite the emphasis given to puerperal psychological disorders such as postpartum depression, mental disorders during pregnancy are not uncommon and have high prevalence, similar to the puerperal period. 16 Depressive episodes are the most frequent during gestation 12 17 and may negatively impact pre- and postnatal care. 12 Depressive mood or anhedonia is one of main symptoms of depression during pregnancy, albeit alterations in sleep and appetite, irritability, loss of libido, psychomotor retardation, and suicidal ideation are also observed and may cause psychosocial impacts on women. 12 Thus, some of these manifestations such as fatigue, alterations in sleeping, appetite, and libido patterns may cause misdiagnosis of depression on pregnancy. Studies reveal that prevalence rates of depression during pregnancy in developing countries, such as Brazil, are around $20\%$. 12 17 18 19 20 Among risk factors associated with depression during pregnancy the biggest factor seems to be a history of psychiatric conditions, especially depression. 12 20 Added to that, sociodemographic factors related to poverty, such as unemployment, low income, education, and unwanted pregnancy; and other factors like alcohol dependence and substance abuse, besides lack of social and emotional support are also predictors of depression during pregnancy. 14 18 19 20 It is necessary to stress that the presence of untreated depression during pregnancy can lead to fetal-maternal and obstetric risks like the increase of abortion rates, placenta abnormalities, hemorrhage, prematurity, fetal pain, low selfcare, low weight of the newborn, preeclampsia, and low adherence to prenatal monitoring. 12 16 21 Hence, depression during pregnancy can lead to a higher risk of postnatal depression, has an impact on the mother-child binomial, may compromise the fetal-maternal and mother-child relationship, as well as the child's psychosocial development. 18 22 23 Thus, the early assessment and treatment of depression symptoms during pregnancy is fundamental to reduce negative outcomes to maternal health, fetal development, labor, and the child's health. 12 16 Regarding women who underwent BS, there might be higher risks related to psychological disorders in the face of difficulties of adaptation to physical and mental changes resulted from surgery. The purpose of this research is analyze the prevalence of depressive symptomatology in Brazilian pregnant women with history of BS, and study factors associated with depression incidence, as well as predictive factors of depression in this type of pregnancy. ## Methods This is a quantitative cohort comprised of 247 pregnant women from different Brazilian regions, which fulfilled the following criteria: age equal or superior to 18 years old, pregnant at the moment of the survey, and who underwent BS before getting pregnant. Women who, despite having a history of BS, answered the survey after childbirth were excluded. The project was approved by the Comitê de Ética em Pesquisa da Maternidade Escola da UFRJ (CAEE: 65713417.9.0000.5275) before the beginning of data collection, which occurred for 13 months, from October 2017 to November 2018. The study was announced on Facebook groups predominantly formed by women who underwent BS and got pregnant or wanted to get pregnant, for example: Pregnancy after Bariatric Surgery, Gestation after Bariatric Surgery and Being a Mother after Bariatric Surgery. After the research was authorized and the researcher was included by group administrators, the participants were invited to the study through a link with access to a survey elaborated in Google Forms. The survey began with an Informed Consent Form (ICF), which was a necessary condition for the participant to proceed and answer the following data collection tools: 1) general data protocol for socio demographic, psychosocial and clinical data collection (physical and mental health); 2) Brazilian version of the Depression, Anxiety and Stress Scale (DASS-21) 24 to rate the occurrence of depressive symptomatology detected, with a cutoff point of ≥ 14, which would correspond to moderate levels of the disorder for DASS-21. All social demographic, psychosocial and clinical data (mental and physical health) were processed and analyzed in terms of frequency of occurrence of information collected by the General Data Protocol. Depressive symptomatology data were rated according to DASS-21 scale's instructions. Analyses, inferential and descriptive, were conducted by using Statistical Package for Social Science (SPSS, IBM Corp. Armonk, NY, USA) version 20.0. The Chi-Squared Test was adopted to investigate sociodemographic (marital status, education, labor activity, and familiar income), psychosocial (planned pregnancy, desired pregnancy, emotional support, financial support, and history of obesity) and clinical factors, as well as both physical health (gestational age and time of BS) and mental health (compulsive behaviors and history of psychiatric disorders) associated with depressive symptomatology, assuming p -values ≤ 0.05 as statistically significant. Finally, a binary logistic regression model was tested to detect predictive factors of depressive symptomatology in pregnant women with history of BS, controlling the following statistically significant variables (p ≤ 0.05): marital status, planned pregnancy, desired pregnancy, and history of psychiatry disorders. Finally, it is necessary to emphasize that the variables of history of obesity and compulsive behaviors show as missing because they were inserted after the beginning of data collection. Therefore, these two variables were collected only in 71 participants. ## Results As can be observed on Table 1, the sample was mostly from married women ($84.6\%$; $$n = 209$$), white women ($64.8\%$; $$n = 160$$), between the age of 30 to 34-years-old ($38.5\%$; $$n = 95$$), followed by women from 25 to 29-years-old ($28.3\%$; $$n = 70$$). More than a half of them had uncompleted or completed college ($68.9\%$; $$n = 124$$). Moreover, $74.5\%$ ($$n = 184$$) had labor activities, with monthly incomes centered in the group of 2 to 4 minimum wage ($43.7\%$; $$n = 108$$), followed by remunerations between 4 to 10 minimum wage ($33.2\%$; $$n = 82$$). In the most part, participants lived in the Southeast of Brazil ($53.4\%$; $$n = 132$$), and $79.1\%$ ($$n = 188$$) of them did prenatal in the private health care system. Table 1 \| Variables \| Category \| n \| % \| \| --- \| --- \| --- \| --- \| \| Marital status \| Single \| 33 \| 13.4 \| \| Marital status \| Married \| 209 \| 84.6 \| \| Marital status \| Divorced \| 5 \| 2.0 \| \| Total \| \| 247 \| 100.0 \| \| Race/Ethnics \| Yellow \| 3 \| 1.2 \| \| Race/Ethnics \| White \| 160 \| 64.8 \| \| Race/Ethnics \| Mixed race (parda) \| 61 \| 24.7 \| \| Race/Ethnics \| Black \| 14 \| 5.7 \| \| Race/Ethnics \| No answer \| 9 \| 3.6 \| \| Total \| \| 247 \| 100.0 \| \| Age group \| 20 to 24 years \| 18 \| 7.3 \| \| Age group \| 30 to 34 years \| 95 \| 38.5 \| \| Age group \| 35 to 39 years \| 54 \| 21.9 \| \| Age group \| 40 to 44 years \| 10 \| 4.0 \| \| Total \| \| 247 \| 100.0 \| \| Education \| Incomplete elementary school \| 1 \| 0.4 \| \| Education \| Complete elementary school \| 2 \| 0.8 \| \| Education \| Incomplete middle school \| 8 \| 3.2 \| \| Education \| Complete college \| 116 \| 47.0 \| \| Total \| \| 247 \| 100.0 \| \| Labor Activity \| Yes \| 184 \| 74.5 \| \| Labor Activity \| No \| 63 \| 25.5 \| \| Total \| \| 247 \| 100.0 \| \| Family income \| No income \| 7 \| 2.8 \| \| Family income \| To 2 minimum wage \| 32 \| 13.0 \| \| Family income \| From 2 to 4 minimum wage \| 108 \| 43.7 \| \| Family income \| From 4 to 10 minimum wage \| 82 \| 33.2 \| \| Family income \| From 10 to 20 minimum wage \| 13 \| 5.3 \| \| Family income \| More than 20 minimum wage \| 5 \| 2.0 \| \| Total \| \| 247 \| 100.0 \| \| Brazilian region \| North \| 2 \| 0.8 \| \| Brazilian region \| Northeast \| 33 \| 13.4 \| \| Brazilian region \| Center-West \| 16 \| 6.5 \| \| Brazilian region \| Southeast \| 132 \| 53.4 \| \| Brazilian region \| South \| 64 \| 25.9 \| \| Total \| \| 247 \| 100.0 \| \| Prenatal assistance \| Public institution \| 50 \| 20.2 \| \| Prenatal assistance \| Private institution \| 188 \| 76.1 \| \| Prenatal assistance \| No monitoring \| 9 \| 3.6 \| \| Total \| \| 247 \| 100.0 \| In relation to psychosocial data, though half of the women ($51\%$; $$n = 126$$) declared unplanned pregnancy, it is understood that gestation was predominantly desired by them ($86.6\%$; $$n = 214$$). Nonetheless, $56.3\%$ ($$n = 139$$) denied having financial and/or emotional support, $37.7\%$ ($$n = 93$$) reported having emotional support, and $3.2\%$ ($$n = 8$$) both supports. Most of the women declared being obese since childhood ($42.3\%$; $$n = 30$$), followed by adolescence ($36.6\%$; $$n = 26$$). Clinical data of mental and physical health are described in Table 2. It is noted that $25.9\%$ ($$n = 64$$) were found in the first gestational trimester, $34\%$ ($$n = 84$$) in the second trimester, and $40.1\%$ ($$n = 99$$) in the third trimester. Most participants declared being pregnant after 18 months of BS ($63.6\%$; $$n = 157$$) by Gastric Bypass technique ($87.4\%$; $$n = 216$$), and are still on medical follow-up ($47.4\%$; $$n = 117$$). Most of them ($78.9\%$) reported having some disease before pregnancy, with clinical pathologies such as systemic arterial hypertension, anemia, hypothyroidism, diabetes, musculoskeletal disorders, and hepatic steatosis in more than half of them ($53.4\%$; $$n = 132$$); followed by history of psychiatric disorders, such as depression and anxiety/panic attack ($12.1\%$; $$n = 30$$), or both ($13.4\%$; $$n = 33$$). Table 2 \| Indicators of clinical health \| Frequency \| % \| \| --- \| --- \| --- \| \| Gestational age \| \| \| \| 1 to 13 weeks (1 st Trimester) \| 64.0 \| 25.9 \| \| 14 to 26 weeks (2 nd Trimester) \| 84.0 \| 34.0 \| \| 27 to 40 weeks (3 rd Trimester) \| 99.0 \| 40.1 \| \| Total \| 247.0 \| 100.0 \| \| Time of BS \| \| \| \| Less than 6 months \| 7.0 \| 2.8 \| \| 6 to 11 months and 30 days \| 28.0 \| 11.3 \| \| 12 to 17 months and 30 days \| 55.0 \| 22.3 \| \| 18 months to 23 months and 30 days \| 35.0 \| 14.2 \| \| More than 24 months \| 122.0 \| 49.4 \| \| Total \| 247.0 \| 100.0 \| \| Type of BS \| \| \| \| Gastric bypass \| 216.0 \| 87.4 \| \| Vertical gastrectomy \| 31.0 \| 12.6 \| \| Total \| 247.0 \| 100.0 \| \| Time of medical supervisor after BS \| \| \| \| Less than 6 months \| 25.0 \| 10.1 \| \| 6 months to 1 years \| 52.0 \| 21.1 \| \| 1 year to 2 years \| 53.0 \| 21.5 \| \| Still on supervisor \| 117.0 \| 47.4 \| \| Total \| 247.0 \| 100.0 \| \| Diseases before pregnancy \| \| \| \| Clinical pathologies \| 132.0 \| 53.4 \| \| History of psychiatric disorders \| 30.0 \| 12.1 \| \| Both \| 33.0 \| 13.4 \| \| \| 52.0 \| 21.1 \| \| Total \| 247.0 \| 100.0 \| \| Compulsive behaviors \| \| \| \| Bulimia nervosa \| 8.0 \| 11.3 \| \| Anorexia nervosa \| 2.0 \| 2.8 \| \| Binge eating disorder \| 13.0 \| 18.3 \| \| Alcohol use disorder \| 2.0 \| 2.8 \| \| Disorder related to substance abuse (sedatives, hypnotics, anxiolytics or drugs) \| 1.0 \| 1.4 \| \| Other compulsions(sex, gambling, physical exercise, shopping etc.) \| 2.0 \| 2.8 \| \| No \| 43.0 \| 60.6 \| \| Total \| 71.0 \| 100.0 \| \| Didn't answer \| 176.0 \| 0.0 \| Based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 25 diagnostic criteria: $39.4\%$ ($$n = 28$$) of the women declared having compulsive behaviors during pregnancy, with $32.4\%$ ($$n = 23$$) being eating compulsions such as bulimia nervosa ($11.3\%$; $$n = 8$$), anorexia nervosa ($2.8\%$; $$n = 2$$), and binge eating disorder ($18.3\%$; $$n = 13$$). The estimated prevalence of gestational depressive symptomatology of women with history of BS on this survey was of $32.8\%$ ($M = 11.4$; DP = 11.5; AV = 0–42), with a cutoff point of ≥ 14, which is considered moderate depressive symptomatology level by DASS-21. On Table 3 were presented the Chi-Squared Test results used to test relations among depressive symptomatology in pregnant women and sociodemographic, psychosocial, and clinical variables. Variables such as marital status ($$p \leq 0.000$$), planned pregnancy ($$p \leq 0.001$$), wanted pregnancy ($$p \leq 0.004$$), and history of psychiatric disorders ($$p \leq 0.000$$) showed statistically significant association with depressive symptoms (p ≤ 0.05). Then, unmarried women, who did not plan or want the pregnancy, and had a history of psychiatric disorders (depression, anxiety, and/or panic attack) had high depressive symptomatology scores identified by DASS-21. Other variables such as education, income, social and emotional support, gestational age, labor activity, history of obesity, time of BS before getting pregnant, gestational trimester, and compulsive behaviors do not show a statistically significant association with depressive scores. Table 3 \| Variables \| No depressive symptoms \| No depressive symptoms.1 \| With depressive symptomatology \| With depressive symptomatology.1 \| Unnamed: 5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| n \| % \| n \| % \| P -value \| \| Marital status \| \| \| \| \| 0.000* \| \| Unmarried \| 16 \| 41 \| 23 \| 16 \| \| \| Married \| 150 \| 72.1 \| 58 \| 150 \| \| \| Education \| \| \| \| \| 0.062 \| \| Completed and uncompleted elementary school \| 3 \| 100 \| 0 \| 0 \| \| \| Completed and uncompleted high school \| 56 \| 75.7 \| 18 \| 24.3 \| \| \| Completed and uncompleted college \| 107 \| 66 \| 55 \| 34 \| \| \| Family income \| \| \| \| \| 0.096 \| \| No income \| 4 \| 57.1 \| 3 \| 42.9 \| \| \| Up to 2 minimum wage \| 17 \| 53.1 \| 15 \| 46.9 \| \| \| From 2 to 4 minimum wage \| 79 \| 73.1 \| 29 \| 26.9 \| \| \| From 4 to 10 minimum wage \| 52 \| 63.4 \| 30 \| 36.6 \| \| \| From 10 to 20 minimum wage \| 10 \| 76.9 \| 3 \| 23.1 \| \| \| More than 20 minimum wage \| 4 \| 80 \| 1 \| 20 \| \| \| Labor activity \| \| \| \| \| 0.177 \| \| Yes \| 150 \| 71.1 \| 58 \| 28.9 \| \| \| No \| 16 \| 41 \| 23 \| 59 \| \| \| Planned pregnancy \| \| \| \| \| 0.001** \| \| Yes \| 96 \| 77.4 \| 28 \| 96 \| \| \| No \| 70 \| 56.9 \| 53 \| 70 \| \| \| Desired pregnancy \| \| \| \| \| 0.004* \| \| Yes \| 151 \| 70.6 \| 63 \| 29.4 \| \| \| No \| 15 \| 45.5 \| 18 \| 54.5 \| \| \| Emotional support \| \| \| \| \| 0.605 \| \| Yes \| 66 \| 65.3 \| 35 \| 34.7 \| \| \| No \| 100 \| 68.5 \| 46 \| 31.5 \| \| \| Financial support \| \| \| \| \| 0.238 \| \| Yes \| 8 \| 53.3 \| 7 \| 46.7 \| \| \| No \| 158 \| 68.1 \| 74 \| 31.9 \| \| \| History of obesity \| \| \| \| \| 0.974 \| \| Obese since childhood \| 21 \| 70 \| 9 \| 30 \| \| \| Obese as of adolescence \| 18 \| 69.2 \| 8 \| 30.8 \| \| \| Obese as of adulthood \| 10 \| 66.7 \| 5 \| 33.3 \| \| \| Gestational age \| \| \| \| \| 0.122 \| \| 1 st Trimester \| 38 \| 59.4 \| 26 \| 40.6 \| \| \| 2 nd Trimester \| 63 \| 75 \| 21 \| 25 \| \| \| 3 rd Trimester \| 65 \| 65.7 \| 34 \| 34.3 \| \| \| History of psychiatric disorders \| \| \| \| \| 0.000* \| \| Yes \| 31 \| 49.2 \| 32 \| 50.8 \| \| \| No \| 45 \| 86.5 \| 7 \| 13.5 \| \| \| Compulsive behaviors \| \| \| \| \| 0.197 \| \| Eating disorder \| 11 \| 52.4 \| 10 \| 47.6 \| \| \| Disorder related to use of some substance \| 2 \| 66.7 \| 1 \| 33.3 \| \| \| Other compulsions \| 1 \| 50 \| 1 \| 50 \| \| \| No \| 35 \| 77.8 \| 10 \| 22.2 \| \| The result of binary logistic regression was significative: X 2 [5] = 276.759; $p \leq 0.001$; R 2 Nagelkerke = 0.188, and confirmed that variables like marital status and history of psychiatric disorders were predictors of depressive symptomatology's occurrence in the pregnant women with history of BS participants in this survey. As noted in Table 4, single women with history of BS proved to be three times (OR = 3.38; $95\%$ CI = 1.586–7.221; $$p \leq 0.002$$) more likely to develop depressive symptoms, when compared to married women. Similarly, pregnant women with history of psychiatric disorders showed a more than double chance (OR = 2.70; $95\%$ CI = 1.438–5.081; $$p \leq 0.102$$) to develop these symptoms. Table 4 \| Variables \| β \| OR \| 95% CI \| P -value \| \| --- \| --- \| --- \| --- \| --- \| \| Marital status \| 1.219 \| 3.38 \| 1.586–7,221 \| 0.002 \| \| Planned pregnancy \| -0.53 \| 0.59 \| 0.312–1,111 \| 0.102 \| \| Wanted pregnancy \| -0.592 \| 0.55 \| 0.234–1,305 \| 0.438 \| \| History ofpsychiatry disorder \| 0.994 \| 2.7 \| 1.438–5,081 \| 0.002 \| In contrast, planned pregnancy ($$p \leq 0.102$$) and desired pregnancy ($$p \leq 0.438$$), do not perform as significance predictions to depression occurrence in this population. On the contrary, planned pregnancy (β = - 0.530; OR = 0.59) and desired pregnancy (β = –0.592; OR= 0.55) may represent protection factors to this population, since in these cases participants had nearly $40\%$ (41 and $45\%$, respectively) less probability of having depressive symptoms. ## Discussion In order to investigate the prevalence and related factors to depressive symptomatology in pregnant women with history of BS, this study shows scientific evidence about the importance of examining depressive symptoms in this population. In accordance with the expected results, women who underwent BS are more likely to have mental disorders during the pregnancy-puerperal cycle, especially in the first and third trimesters of pregnancy. Our results confirmed that the prevalence of depressive symptomatology ($32.8\%$) among pregnant women who underwent BS is higher than in low-risk pregnant women, which is around $20\%$. 12 17 18 20 Marital status, planned and desired pregnancy, and history of psychiatric disorders are factors that must be considered in prenatal monitoring of this population because they are significantly related to depressive symptoms. Moreover, it should be noted that this study would be done on newly released Endocrine Technological Disorders Unit in Pregnancy (Unidade de Transtornos Endócrino-Metabólicos na Gestação – UTEM) at the Maternity School of Federal University of Rio de Janeiro, a public service reference center to perinatal health essential care in Rio de Janeiro, Brazil. However, participants' recruitment to the survey in this unit was not possible, bearing in mind the low demand for this service. It must be observed that the adherence of the pregnant population with BS history to public services specialized in gestational health was not frequent. In Rio de Janeiro, in addition to UTEM, only the Public Servants Federal Hospital of the State (Hospital Federal dos Servidores do Estado) offers public prenatal monitoring oriented to post-bariatric pregnant women. In other words, the women who did their prenatal on those services, which mostly focused on low-risk and typical pregnancies, did not receive the necessary care oriented to their characteristics. Although public health policies in Brazil assure BS indication in cases of severe obesity, like class III, 26 to be adopted, an as a procedure to infertility treatment among women, 27 it was observed that a large part of them sough care in private institutions, where it is not always possible to guarantee a multiprofessional, specialized service, capable of caring for and reducing risks in a pregnancy post-BS. It should also be stressed that UTEM is a unit which aims to create a multiprofessional model of prenatal service to the pregnant population with different types of endocrine metabolic disorders, such as obesity, diabetes, and history of BS. The specialized service to this type of pregnancy is important, due to the demand for attention directed to the related risk factors related to gestation after BS, whose impacts to the mother-child binomial are unquestionable. Studies reveal that these risks have different negative results for the mother, such as nutrient deficiency, 28 that may cause anemia, malnutrition, and intestinal obstruction. 28 And for fetal development, it is observed a higher incidence of congenital anomalies, such as neural tube defect and intrauterine growth restriction, 29 and the consequences to the child's health in their development, such as premature birth and low birth weight. 29 Due to the struggle to gather face-to-face data from UTEM patients, the survey's data collection happened, exclusively, in an online environment. This can, for instance, explain the participants' high level of education. Therefore, the participants were recruited in Facebook groups related to women who got pregnant and/or wanted to get pregnant after BS. In these groups they share their experiences before, during, and after gestation. In this virtual context, we observed that pregnant women seem to adopt various methods to deal with difficulties related to maternity, creating support networks and closer ties with other women in these groups. According to Frossard and Dias, 30 internet groups facilitate communication among peers and enable the gathering, organization, and circulation of information about people's needs, and focus on respective demands. In the groups where the survey was made, a huge circulation of information about various aspects involving a gestation after BS was noted. In the vast majority, the information was practical in nature, without a scientific accuracy and without any commitment to medical discourse. Still, it was possible to note that the participants, for the most part, followed medical advice because they kept up medical monitoring ($79.1\%$) and got pregnant after 18 months of BS ($63.3\%$), which is the minimum period recommended for a low-risk pregnancy 6 9 11 after BS by surgical technique gastric bypass ($87.4\%$), which is, according to Santo et al., 9 the golden pattern to this type of obesity treatment. Since the research's proposal was to recognize indicators of mental health, more specifically of mood disorders, in pregnant women after BS, we chose to call it “depressive symptomatology” and not “depression”, as depression is a psychopathological disorder which demands wider psychiatric and psychodiagnostic assessments. Likewise, DASS-21 is a scale with good psychometric properties to depression symptoms' measurement in Brazilian population. 24 Although this scale is not validated for use in the pregnant population yet, some cautions were adopted to minimize this limitation of the instrument. To identify pregnant women with depressive symptomatology, higher cutoff points were chosen. Different from cutoff < 9, that indicates there are no depression symptoms in the population in general, the chosen cutoff point was ≥ 14, which would indicate the presence of moderate symptoms in the patients. Here, we clarify that this decision was adopted after doing analyses using two cutoff points: ≥ 10, which identifies mild symptoms, and ≥ 14, for moderate symptoms. the first cutoff found a prevalence of $46.9\%$, which is higher than the prevalence of depression among low-risk pregnant women ($20\%$). 12 17 18 20 Therefore, the cutoff ≥ 14 would be more indicated to investigate the presence of depression symptoms in the population observed, even without a support of DASS-21's psychometric study of sensitivity and specificity for use in pregnant women. Thus, it is important to discuss how the prevalence of $32.8\%$ depressive symptomatology in pregnant women after BS was higher when compared to that in low-risk pregnant women. 12 17 18 Depression symptoms were more frequent in the first ($40.6\%$) and third trimesters ($34.3\%$), which reinforces the need for policies focused on pregnancy health in the most critical moments during gestation, either by issues related to pregnancy adaptation or to the fear of childbirth. 13 14 This response reinforces the idea that gestation is, indeed, a difficult and vulnerable time in a woman's life which can lead to psychiatric disorders 12 13 particularly associated with physical, hormonal, mental, and social changes, specific from this period. 8 16 Regarding the support system, although most of the participants were married ($84.6\%$), $56.3\%$ denied having any type of financial and/or emotional support. Our data indicates that single women with history of BS showed three times more chances (OR = 3.38; $95\%$ CI = 1.586–7.221; $$p \leq 0.002$$) to manifest depressive symptoms, when compared with married women, which highlights the importance of support systems (emotional and social) to face the issues and insecurities that the pregnancy-puerperal period can bring. Likewise, the presence of psychiatric history (anxiety, panic attack, and depression) found in $25.5\%$ of the participants must be considered, since psychiatric disorders previous to pregnancy may have obstetric, neonatal, and puerperal impacts. The increase of abortion rates, 12 16 18 hemorrage, 12 16 18 low birth weight, 12 16 18 and the consequences to psychosocial child development, 12 16 18 are examples of negative outcomes to the mother-child binomial related with these disorders during the pregnancy-puerperal cycle. Moreover, psychiatric backgrounds may result in a lesser adherence to prenatal care, 12 16 18 with difficulties for the woman to adopt self-care habits, 16 18 impacts to the quality of the maternal-fetal relationship during pregnancy, and the mother-child relationship on puerperium, especially for its association with postnatal depression. 12 16 18 Ratified these risks, we verified that the participants with psychiatric backgrounds demonstrate double chances (OR = 2.70; $95\%$ CI = 1.438–5.081) of manifesting depression symptoms on gestation after BS. These findings deserve attention because $39.4\%$ of the participants also report compulsive behaviors, especially disorders related to eating, such as binge eating disorder ($18.3\%$), bulimia nervosa ($11.3\%$), and anorexia nervosa ($2.8\%$). Studies suggest these behaviors are common in patients with history of BS 6 7 29 31 32 and may indicate the destination of voracity, despair, and eating impulse 31 32 in these pregnant women. Paradoxically, these behaviors were not related to depressive symptomatology in the participants of this study. Although BS has weight loss as objective, as well as mental and physical health improvement, the presence and/or conservation of compulsive behaviors in pregnant women seem to work as a strategy to relieve their anguish, with effects on mood responses such as depression during pregnancy. We may assume that compulsive behaviors involved in this period can be moderators of the anxiety typical during pregnancy, and high prevalence of depressive symptomatology during pregnancy after BS. Despite the improvement of metabolic and reproductive functioning after the surgery procedure, 5 9 11 the decision to do a BS does not seem to be a determinant to the intent of getting pregnant, as there was no significative difference between the women who planned ($49\%$) and those who did not ($51\%$) their pregnancies. Nevertheless, most of them ($86.6\%$) affirmed that the gestation was desired. It should be reinforced that neither planned pregnancy (β = -0.53; OR = 0.58; $$p \leq 0.102$$) nor desired pregnancy (β = -0.59; OR = 0.55; $$p \leq 0$$,438) were predictive factors for depressive symptoms. On the contrary, these factors appear as protectives to the occurrence of depression symptoms for this population, since women who planned and wanted to get pregnant had around $40\%$ (41 and $44\%$ respectively) less chances to manifest those symptoms. This suggests that pregnancy planning and willingness are protective factors to the mental health of pregnant people with a history of BS. Most of the women affirmed that they were obese since childhood ($42.3\%$) or adolescence ($36.6\%$). Although there is no significative association with depression indicators, we may infer that, despite weight gain before pregnancy, the pressure for a perfect body reduces during gestation due to the expected physical transformations 28 and, possibly, there is a sort of social tolerance for the pregnant body to be a nonstandard body imposed by thinness culture. In spite of that, although gestational age is not associated significantly with depression symptoms, these symptoms were more frequent in pregnant women in the first ($40.6\%$; $$n = 26$$) and third trimesters ($34.3\%$; $$n = 34$$). It seems to indicate that, as literature has already considered, there is a variation between risks for mental disorders during the pregnancy-puerperal cycle, 12 13 when the second trimester is a moment of emotional stability. Presumably, the difficulties during the adaptation to physical, mental, and social transformations, typical from pregnancy after BS, may result in a higher emotional ambivalence that require women's mental efforts to take a new role as mother. 15 And, still, it seems that due to the physical transformations during gestational trimesters, with the changes of a pregnant body and the alterations of body image, women are more vulnerable to the manifestation of depressive symptomatology, even if it is not significantly related to history of obesity. Finally, some limitations of this study must be indicated. First, while online collection via Facebook groups enabled this research and expanded its reach, by recruiting participants from different parts of Brazil, the lack of face-to-face meetings between researcher and participants may have caused difficulties to clarify doubts about the survey. Although this strategy of online survey is becoming more disseminated, especially during the Coronavirus pandemic, this type of data collection precludes capture of nuances which face-to-face collection of data may generate, although the participants might feel more comfortable answering the study online. A second important limitation involves the DASS-21 scale used to identify participants with depression symptoms. We reinforce that although it might be a limitation to this study, the choice of DASS-21 was due to it being an easy instrument of application and analysis, besides having a user-friendly language, mainly by collecting data in a virtual environment. For this reason, the studies suggest we use more tools with the purpose of expanding scientific knowledge and the recognition of risk factors and protection related to this type of gestation. In conclusion, the evaluation of possible mental disorders during pregnancy with history of BS is fundamental, since identifying the risk factors and protections associated with these cases would allow the medical staff to plan specific and preventive interventions for the negative outcomes that this condition imposes. The discoveries in this study ratify the importance of early tracking, diagnostic, and treatment to reduce the impact of perinatal mental disorders, both for the mother and child's health. 12 20 ## Conclusion Therefore, we concluded that the prevalence of depressive symptomatology in pregnant women with BS is higher than what was found in low-risk pregnancies, proving the psychological control of these women is related to prenatal factors, since this condition may show impacts to the mother-child binomial with short, medium and long-term outcomes. Some factors, such as marital status, planned pregnancy, desired pregnancy, and psychiatric history must be observed by the professionals involved. Henceforth, it is necessary to emphasize that planned and desired pregnancies seem to succeed as protection factors to depressive symptomatology, even if these topics were poorly studied into the perinatal psychology field. Considering obstetric mental disorders, serious consequences to fetal-maternal health, and the underdiagnosis of these disorders in this period, the results of this research indicate the importance of psychological support to pregnant women who underwent BS. Moreover, the training of multiprofessional teams is fundamental for the early recognition and strategic orientation for pregnant women with some symptoms of those disorders, for the sake of a better psychodiagnostic assessment and appropriate treatment, minimizing the negative impacts on obstetric and neonatal care. 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Interface Comun Saúde Educ (2016) 20 349-361. DOI: 10.1590/1807-57622014.1334 31. Edler S V. Tempos compulsivos: a busca desenfreada pelo prazer. (2017) 32. Novaes J V, Vilhena J. O corpo que nos possui: corporeidade e suas conexões. (2018)
--- title: 'Promoting and Maintaining Changes in Smoking Behavior for Patients Following Discharge from a Smoke-free Mental Health Inpatient Stay: Development of a Complex Intervention Using the Behavior Change Wheel' authors: - Emily Shoesmith - Lisa Huddlestone - Jodi Pervin - Lion Shahab - Peter Coventry - Tim Coleman - Fabiana Lorencatto - Simon Gilbody - Moira Leahy - Michelle Horspool - Claire Paul - Lesley Colley - Simon Hough - Phil Hough - Elena Ratschen journal: Nicotine & Tobacco Research year: 2022 pmcid: PMC10032184 doi: 10.1093/ntr/ntac242 license: CC BY 4.0 --- # Promoting and Maintaining Changes in Smoking Behavior for Patients Following Discharge from a Smoke-free Mental Health Inpatient Stay: Development of a Complex Intervention Using the Behavior Change Wheel ## Abstract ### Introduction Evidence suggests that smokers can successfully quit, remain abstinent or reduce smoking during a smoke-free mental health inpatient stay, provided behavioral/pharmacological support are offered. However, few evidence-based strategies to prevent the return to prehospital smoking behaviors post-discharge exist. ### Aims and Methods We report the development of an intervention designed to support smoking-related behavior change following discharge from a smoke-free mental health stay. We followed the Behavior Change Wheel (BCW) intervention development process. The target behavior was supporting patients to change their smoking behaviors following discharge from a smoke-free mental health stay. Using systematic reviews, we identified the barriers and enablers, classified according to the Theoretical Domains Framework (TDF). Potential intervention functions to address key influences were identified by consulting the BCW and Behavior Change Technique (BCT) taxonomy. Another systematic review identified effectiveness of BCTs in this context. Stakeholder consultations were conducted to prioritize and refine intervention content. ### Results Barriers and enablers to supporting smoking cessation were identified within the domains of environmental context and resources (lack of staff time); knowledge (ill-informed interactions about smoking); social influences, and intentions (lack of intention to deliver support). Potential strategies to address these influences included goal setting, problem-solving, feedback, social support, and information on health consequences. A strategy for operationalizing these techniques into intervention components was agreed upon: Pre-discharge evaluation sessions, a personalized resource folder, tailored behavioral and text message support post-discharge, and a peer interaction group, delivered by a trained mental health worker. ### Conclusions The intervention includes targeted resources to support smoking-related behavior change in patients following discharge from a smoke-free mental health setting. ### Implications Using the BCW and TDF supported a theoretically and empirically informed process to define and develop a tailored intervention that acknowledges barriers and enablers to supporting smoking cessation in mental health settings. The result is a novel complex theory- and evidence-based intervention that will be formally tested in a randomized controlled feasibility study. ## Introduction Smoking remains a leading cause of mortality and morbidity worldwide.1 Smoking prevalence in the general population in England has steadily declined from $14.1\%$ in 2019 to $13.5\%$ in 2020,2 but data indicate that prevalence remains approximately two to three times higher among people with mental health conditions in the United Kingdom.3 Smokers with mental health conditions are more likely to experience greater dependence on smoking, are more likely to develop smoking-related illnesses, and long-term quit rates among this population are lower than for the general adult population.3–5 Although people with mental health conditions are just as motivated to quit as those in the general population,,3,6 they are less likely to receive the support they require compared with smokers without mental health conditions.7 Many healthcare settings are now smoke-free by law and offer smoking cessation support for smokers during their stay.8 Evidence suggests that individuals can successfully remain abstinent during their inpatient stay when behavioral/pharmacological support is offered.9,10 Additionally, evidence from mental health settings indicates that a period of supported abstinence may promote smoking behavior change and motivation to quit.11 However, the risk of relapse postdischarge is high, with one study reporting $76\%$ relapsed to smoking behaviors within one-day postdischarge.12 Lack of support offered post-discharge and the high risk of relapse renders smoking-related resource input during the inpatient episode inefficient, as positive smoking behavior change achieved during the inpatient stay is often lost. Identifying interventions that are effective in supporting patients around the time of discharge in maintaining or achieving positive change to their smoking behaviors has been identified as an important evidence gap in this area.13 Designing interventions to support smoking cessation would benefit from drawing on theories and frameworks from behavioral science which summarize drivers of behaviors and link these to strategies for changing behavior.14 The Behavior Change Wheel (BCW)15 and Theoretical Domains Framework (TDF) are developments in the behavior change field that provide a systematic, theoretical basis for understanding and providing tools to change behavior,16 based on multiple models of health behavior. The process of intervention development using the BCW and TDF is outlined in detail15 and has been extensively applied to design smoking cessation interventions.17–19 ## Theoretical Underpinnings The BCW was developed from an extensive review of behavioral science frameworks, designed to facilitate the development of behavior change interventions.15,20 At the center of the BCW is a behavior change model known as the COM-B, which postulates that behavior change is essentially dependent on the interaction between three key determinants: The capability (C) to perform a behavior, the opportunity (O) and motivation (M) to perform.15 The TDF is an elaboration of the COM-B model and includes 14 domains based on an integration of behavioral theories.21–23 These domains prompt the consideration of a wide range of influences as they include individual-level factors, social factors, and environmental factors.16 The COM-B and TDF have been mapped to frameworks that specify different intervention strategies. The first is the BCW which specifies nine intervention types for changing behavior (e.g. education, training).15 These intervention functions are supported by seven policy categories, representing decision types that help to support the interventions (e.g. communication/marketing, guidelines).15 The second is the more granular taxonomy of Behavior Change Techniques (BCTs), which breaks down these broad intervention types into 93 discrete BCTs (e.g. goal setting, action planning, feedback on behavior).23 There are published tools that pair domains from the COM-B/TDF with intervention strategies in the BCW/BCT taxonomy (BCTv1)23 to specify which types of behavior change approaches are more likely to be relevant and effective in changing different influences on behavior.15 This provides a step-wise process from moving from an initial behavioral diagnosis to identifying “what needs to change,” to selecting specific intervention components in a theory- and evidence-informed manner.16 To the best of our knowledge, the BCW and TDF have not been applied to the context of smoking cessation following discharge from a smoke-free mental health inpatient stay. The aim of this study was to apply the BCW and TDF to systematically design evidence- and theory-based complex interventions for people with a mental health condition to support smoking-related behavior changes following a mental health inpatient stay. This process is in line with the Medical Research Council’s (MRC) guidance for developing complex interventions.14 ## Aim To develop an evidence- and theory-based complex intervention to support smoking-related behavior changes for patients following discharge from a smoke-free mental health inpatient stay. ## Research Design Intervention development involved two sequential phases: [1] synthesizing findings from existing evidence and mapping these onto the BCW, and [2] using qualitative and consensus methodologies to explore acceptability and feasibility of the prototype intervention (Figure 1). Figure 1.: Intervention development process. ## Phase 1: Content Development The process of intervention development using the BCW and TDF is outlined in detail elsewhere,15 and a detailed explanation of how the process was followed is presented in Supplementary Material 1. A summary is provided: ## Stage One: Understanding the Behavior and Identifying “What Needs to Change” The BCW was used to understand the healthcare problem and the behaviors that could be targeted by an intervention.15 To identify what was influencing behaviors and the barriers to/enablers of change, we conducted a systematic review of the published qualitative and quantitative literature investigating smoking cessation interventions in mental health settings. We extracted and thematically synthesized barriers and enablers. These were then coded to the domains of the COM-B and TDF. The detailed methods are published elsewhere.24 ## Stage Two: Identifying Intervention Options We mapped identified influences on potential intervention strategies using the aforementioned mapping tools which pair COM-B and TDF domains to the BCW and BCTs.15 This resulted in a long list of potentially relevant intervention functions. The APEASE criteria were used to guide judgments as to what functions may be the most appropriate for the intervention.15 To prioritize further, and identify what interventions have been done to date in this context, we also conducted a systematic review of behavioral and pharmacological interventions that maintain abstinence following a smoke-free stay and determined their effectiveness.10 We then mapped the identified intervention functions to potential policy categories that are likely to be appropriate and effective in supporting each function.15 The APEASE criteria were used to help prioritize amongst these potential categories. The COM-B/TDF domains were mapped to the nine intervention functions identified within the BCW (Supplementary Material 3). The intervention functions of education, training, and enablement were included, based upon an evaluation of their affordability, practicability, effectiveness, acceptability, side-effects and safety, and equity (APEASE).15 Details of each intervention function against the APEASE criteria are provided in Supplementary Material 4; Table 4a. The intervention functions were then mapped to the policy categories identified within the BCW (Supplementary Material 3). The policy categories of communications/marketing, guidelines, and service provision were included, based on an evaluation of the APEASE criteria. Details of the evaluation of each policy category against the APEASE criteria are provided in Supplementary Material 4; Table 4B. ## Stage Three: Identifying Content and Implementation Options We considered all BCTs that could be considered for any particular function, using the aforementioned mapping tools.15 This resulted in a long list of potentially appropriate BCTs. The APEASE criteria were used to select those most likely to be appropriate. To further guide selection, we coded existing intervention descriptions into component BCTs using the BCTTv1 and identified which BCTs were defined as “promising” in terms of probable effectiveness and feasibility. We have previously reported these methods in detail.10 We determined the most appropriate mode of delivery. We selected potential intervention strategies and produced a description of a prototype intervention which was then presented to key stakeholders and an expert group of academics and clinicians (Supplementary Material 2; Table 2A). The intervention functions identified were then mapped to a potentially long list of BCTs using the BCTTv115 (Supplementary Material 3). The APEASE criteria and findings from the second systematic review10 were used to guide the identification of the most appropriate BCTs to use within the intervention (Supplementary Material 5). The full findings of the systematic review have been published elsewhere.10 In summary, nine BCTs (including “pharmacological support,” “goal setting (behavior),” and “social support”) were characterized as “promising” in terms of probable effectiveness and feasibility. A review of the full BCTTv1 identified additional BCTs: “social award,” “self-reward,” “pharmacological support,” and “restructuring the social environment,” which also met the APEASE criteria. A blended approach encompassing face-to-face and remote delivery was chosen, as the predischarge evaluation session and provision of resources is planned to occur on the inpatient ward predischarge. Postdischarge, the intervention will be delivered remotely, including behavioral support calls via telephone/video call and optional text message support. This approach was considered in terms of the evidence in relation to remote interventions and in the context of Covid-19-related restrictions. Both delivery modes met the APEASE criteria. A prototype intervention strategy was drafted (Supplementary Material 6), based on the existing evidence and selections made throughout the BCW process. ## Phase 2: Iterative Refinement Based on Stakeholder and Expert Group Input Consultations were conducted with stakeholders and an expert group of academics and clinicians to identify potential problems with the prototype intervention, develop solutions and assess the perceived acceptability, practicability, and ease of integration. ## Settings and Participants Consultations were conducted with four North of England NHS Trusts. Stakeholders included: [1] users of mental health services, [2] informal caregivers, and [3] mental health workers in acute adult mental health inpatient and community mental health services. The term mental health worker (MHW) refers to any mental healthcare professional or healthcare associate role, and the worker did not have to be professionally qualified. ## Recruitment MHWs were invited by their service leads or through direct contact with the research team in liaison with Trusts. Patient and caregiver stakeholders were invited to volunteer through direct contact by the Trust Patient and Public Involvement (PPI) lead; by responding to advertisements in Trust newsletters. ## Methods Consultations took place remotely with individuals or groups. The prototype intervention was presented to stakeholders, and a guided discussion informed by the BCW elicited detailed feedback. Additionally, online presentation software was used during the MHW consultations. This software used 5-point Likert scales to obtain ratings for individual intervention components in relation to practicality, acceptability to staff, patients, and caregivers, and the perceived ease of integration into services. The 5-point Likert scale included response anchors at either end of the scale (1 = low; 5 = high). ## Analysis Intervention component ratings were summarized using descriptive statistics. Qualitative data from the guided discussions were exported to NVivo 12 software25 and analyzed using thematic analysis,26 employing an inductive approach, in which coding and theme development were driven by the content of the responses. One author (E.S) familiarized herself with the data and notes were made of any potential codes by identifying recurring words or units of meaning. The same author generated initial codes from the data and organized them into meaningful groups. Codes were then organized into potential themes and all relevant coded responses were collated within the identified themes. Three authors (E.S, L.H, and J.P) independently reviewed the construction of themes and relevant quotations to agree to the assignment of themes. Inductive thematic analysis was used to identify and categorize potential barriers and enablers in relation to the development and implementation of the intervention, which was subsequently mapped to the COM-B model and facilitated further intervention refinement. The findings from this process were used to refine the prototype intervention (Supplementary Material 2; Table 2B). ## Expert Advisory Group Assessment of Prototype Intervention The prototype intervention was presented during expert panel consensus meetings. The expert panel involved involving academic experts in tobacco control and intervention design ($$n = 13$$), and clinicians from participating Trusts ($$n = 4$$). All experts in the advisory group had been approached to be collaborators/advisors or co-applicants for the research before it commenced, and all accepted and provided feedback. Feedback was elicited through discussion and topics related to overall perceptions, delivery pathways, and potential implementation issues. During the meetings, notes of key points were taken, and written summaries of each meeting were shared with the expert panel to ensure they were accurate overviews of the discussions. Final summaries were discussed in detail to support the refinement of the intervention. The feedback from this process was used to further refine the prototype intervention (Supplementary Material 2; Table 2C). The expert advisory group perceived the intervention to be acceptable and feasible but highlighted that it would be critical to identify an appropriate person to deliver it (Supplementary Material; Table 2C). Group members acknowledged that when identifying MHW to deliver the intervention, this should not be restricted by role, but rather those who fulfill a certain criterion. For example, experience in working with mental health and have a positive outlook on supporting people with mental health conditions to change their smoking-related behaviors. It was highlighted that there is no evidence to suggest certain job roles are better than others in terms of their effectiveness in helping individuals to positively change their smoking behaviors. The expert group indicated that it would not be advisable to exclude potential facilitators due to their job titles when they may be experienced and willing to deliver the intervention effectively. A document was created in collaboration with the expert panel to provide an overview of the MHW who would deliver the intervention, outlining the role description, person specification, and training requirements. In the systematic review, “pros and cons” and “framing/reframing” were identified as “promising” in terms of likely effectiveness, but not in terms of likely feasibility.10 Clinicians from the expert group discussed the importance of including these BCTs, as they appeared particularly important in a mental health inpatient context to help the individual reflect on their smoke-free experience and explore their motivation to remain smoke-free or positively change their smoking behavior. During the predischarge evaluation, the MHW could provide motivational enhancement and strategies for managing temptations, considering the pros and cons of change. Additionally, the patient could be presented with alternative ways of thinking and counterarguments to their belief barriers and discuss behavioral self-management strategies to counter triggers. The expert group also discussed the opt-in/-out process for the text-messaging component that was included in line with the feedback from the stakeholder consultations. The expert group acknowledged the decision to make this component optional but highlighted how text-messaging support has been found to be effective for smoking cessation. Therefore, they suggested that participants should be automatically enrolled in this component unless they express a negative preference during the pre-discharge evaluation. Participants should also be able to opt out of the component at any time by replying “STOP” to any of the messages or by contacting the individual delivering the intervention (Supplementary Material; Table 2C). ## Expert Advisory Group and PPI Group Final Review Following stakeholder consultations and expert group meetings, the revised prototype was re-presented to the expert advisory panel for final feedback. The revised prototype was also presented to members of the research program PPI group. This PPI group was formed following the stakeholder consultations and includes current and ex-smokers who have experienced an inpatient mental health admission. The materials required for the intervention were developed and reviewed by our PPI group and an expert panel, to ensure these materials were appropriately worded, engaging, and ease to use. ## Stage One: Understanding the Behavior and Identifying What Needs to Change The target behavior of interest was defined as: Adult smokers in mental health settings changing their smoking-related behaviors following discharge from a smoke-free mental health inpatient stay. The findings of the systematic review to identify the barriers and enablers to influence the behavior of interest have been published elsewhere.24 In summary, the key barriers to patients making smoking-related behavior changes in mental health settings fell within the following TDF domains: Environmental context and resources (e.g. lack of staff time); knowledge (e.g. interactions were ill-informed); social influences (e.g. smoking norms), and intentions (e.g. lack of positive intentions). Key enablers mainly fell within the domains: Environmental context and resources (e.g. use of appropriate support materials) and social influences (e.g. pro-quitting social norms) (Supplementary Material 3). ## Stakeholder Consultations Twenty-five stakeholders participated to assess the practicality, acceptability, and ease of integration of the prototype intervention. Key stakeholders included MHWs, patients with mental health conditions, and one informal caregiver (Table 1). Table 1. \| Mental health workers (n = 17) \| Mental health workers (n = 17).1 \| N (%) \| \| --- \| --- \| --- \| \| Gender \| Female \| 10 (58.8) \| \| Setting \| Inpatient \| 11 (64.7) \| \| Setting \| Community \| 6 (35.3) \| \| Job role \| Nurse \| 8 (47.0) \| \| Job role \| Healthcare assistant \| 3 (17.6) \| \| Job role \| Occupational therapist \| 1 (5.9) \| \| Job role \| Unit manager \| 1 (5.9) \| \| Job role \| Smoking cessation advisor \| 2 (11.8) \| \| Job role \| Healthy living advisor \| 2 (11.8) \| \| Patients (n = 7) \| Patients (n = 7) \| N (%) \| \| Gender \| Female \| 2 (28.6) \| \| Smoking status \| Recently quit smoking \| 4 (57.1) \| \| Admission/discharge status \| Currently using acute inpatient service \| 2 (28.6) \| \| Admission/discharge status \| Community-based, and have had previous experience of using acute inpatient services \| 5 (71.4) \| \| Caregiver (n = 1) \| Caregiver (n = 1) \| N (%) \| \| Gender \| Female \| 1 (100) \| \| Smoking status \| Never smoker \| 1 (100) \| The prototype intervention (Supplementary Material 2; Table 2A) was well-received, and most individual components were rated highly for practicality, acceptability, and ease of integration (mean scores ranging between 4 and 4.8). The identification of a “buddy” to provide social support postdischarge. This component had a lower score in terms of practicality, acceptability, and ease of integration compared to other components (mean scores ranged between 3.2 and 4.6) and alternative components were explored. The MHWs ratings for individual intervention components are provided in Supplementary Material 7. Overall, four themes were identified from the guided discussion with stakeholders (Supplementary Material 8). The largest number of barriers for MHWs were classified under physical opportunity, including beliefs about limited staff time and resources. From a patient and caregiver perspective, barriers were classified under reflexive motivation, which included beliefs about MHWs not holding positive intentions to support smoking cessation. ## Theme One: Enablers of Patient Engagement and Intervention Delivery In relation to current support, many MHWs identified the absence of a strong link between the inpatient ward and the community. They highlighted that to provide effective support post-discharge, there needed to be improved communication between inpatient/community teams and a seamless process of transferring patient information. For many patients, starting their journey during their stay and continuing this progress into the community was perceived as an important factor in their motivation to continue engaging with smoking cessation support. Patients expressed the importance of this continuum, as opposed to inpatient support and “separate” community support. There was a consensus that the intervention would facilitate a smoother transition from inpatient to community and would enhance patient engagement. All MHWs acknowledged that the predischarge assessment was critical, and it was important that the information collated in this session had to be fed into the community to avoid duplication. All stakeholders believed providing the resource pack to patients predischarge would “bridge the gap” between inpatient and community support, as patient information would be in one place from inpatient to community. All stakeholders also reported that pre-discharge provision was advantageous as the patients would have a plan in place predischarge that would continue in the community. All stakeholders believed that setting personalized goals and the provision of a resource pack at discharge would be beneficial. All stakeholders believed the motivational and practical content included in the pack would facilitate participant engagement. Finally, remote support postdischarge was frequently met with approval, as receiving remote behavioral support calls quickly and easily would facilitate accessibility. It was important to all stakeholders that the behavioral support was delivered by the same MHW to build a good relationship with the patient. ## Theme Two: Potential Barriers to Intervention Delivery MHWs frequently mentioned that capacity and resources may be limited, as staff might often lack the time or opportunity to deliver support to patients alongside their existing workload. Additionally, there was a consensus across stakeholder groups that staff was often resistant to the idea of engaging in smoking cessation support, especially when staff members smoked themselves. ## Theme Three: Overall Perceptions Most MHWs agreed that the pre-discharge session designed for goal setting should be a critical component of smoking cessation support. This session is designed to tap into an individual’s wishes and needs to offer patients the most appropriate and tailored support. Patients also indicated the importance of flexibility within the goal-setting session as every individual would have unique goals and motivations that would require regular review. Stakeholders believed that the resource pack was an innovative and novel idea that would be well-received by patients, as patients are provided with little written information. MHWs felt that including resources on the benefits of quitting within the pack would be advantageous. Telephone behavioral support was identified as an important, essential component by all stakeholders. Many stakeholders believed this would be well-received if the support was individualized. Finally, the text message component was well-received by all stakeholders, with most individuals suggesting these would maintain motivation during a quit attempt. ## Theme Four: Practical Considerations and Suggestions The prototype intervention included a “buddy” who could offer social support to individuals. However, most stakeholders highlighted that patients may not be able to identify a relative or friend who is able or willing to adopt this role. All stakeholders suggested it would be beneficial to replace this component with the formation of a social support group (Supplementary Material 2; Table 2B). By offering this opportunity, stakeholders believed this would enhance the support received from others experiencing a similar situation in an informal, supportive environment. One patient suggested that this group should be organized to encourage peer-to-peer support but have MHW present to facilitate interactions. For the resource pack, most stakeholders suggested that the inclusion of “success stories” which illustrated positive behavior changes made by others in similar situations would provide motivational support. Two patients suggested additional content could be included to outline alternative activities that may offer a distraction (Supplementary Material 2; Table 2B). It was noted this information should be presented in a creative, user-friendly manner. All stakeholders were positive about the acceptability and feasibility of using motivational text messages, but with the option to opt-in or -out should they be seen as overwhelming (Supplementary Material 2; Table 2B). ## The Final Prototype Intervention The development process of identifying key domains through intervention content is presented in Supplementary Material 9. The final prototype intervention consists of components that aim to support smoking-related behavior change among patients following discharge from a smoke-free mental health inpatient setting. Intervention components include: [1] pre-discharge reflection and evaluation, [2] a personalized resource folder (My Try folder), [3] tailored behavioral support (via telephone or video call), [4] optional text-messaging support, and [5] optional opportunity for peer interaction (Supplementary Material 10). The intervention will be delivered by a trained MHW (named the “My Try Specialist,” to link with the name of the personalized resource folder). The name “My Try” was discussed and agreed upon with the stakeholders, expert group, and PPI group, and was identified as appropriate as it indicated flexibility dependent on individual patient motivations. Supplementary Material 11 provides a detailed description of the intervention using the 12-item Template for Intervention Description and Replication (TIDieR) checklist4.27 ## Discussion The intervention consists of five components that together aim to support smoking-related behavior change among patients following discharge from a smoke-free mental health inpatient setting. This intervention draws on behavioral science to specifically target the barriers/enablers to supporting smoking cessation in mental health settings and draws on existing evidence. A large body of literature recognizes the challenges that people with mental health conditions experience when making smoking-related behavior changes, including low self-efficacy,28,29 smoking norms,24,30 widely held misconceptions about the links between mental health and smoking,31,32 and a lack of support offered compared to that available to the general population.33 Therefore, it is important that interventions delivered to people with mental health conditions provide flexible support tailored to individual needs and are delivered by MHWs who are experienced in assisting people with mental health conditions.34,35 By examining the literature, the current prototype incorporates content that has been reported as successful for this population group,10 while addressing commonly cited barriers.24 Stakeholder consultations ensured that the prototype intervention met the target population’s needs and preferences. This in-depth approach to intervention development increases the likelihood that it will achieve smoking-related behavior change post-discharge. The next stage is to deliver the prototype intervention in a small-scale pilot study to test the intervention and processes for the fitness of the purpose. A process evaluation will highlight the need for further refinements. Subsequently, the intervention will be tested in a randomized controlled feasibility study. The prototype intervention includes several remote components. Literature indicates that telephone support increases the chances of stopping smoking, whether the individuals are motivated to quit or not, or if they are receiving other support.36 The COM-B model supports remote smoking cessation treatment as this approach may provide the opportunity to access support by removing barriers (e.g., travel and cost), particularly for those where accessing face-to-face services may be challenging.37,38 Text messaging is also an established cessation modality39 and has been shown to promote smoking cessation in the short term40 and long term.41 The opportunity for a remote peer interaction group was selected as a component to replace the identification of a support “buddy.” There is strong evidence to suggest that social networks play an important role in an individual’s quit attempt,42,43 and we found all “social support” BCTs were identified as “promising” both in terms of likely effectiveness and feasibility.10 Therefore, it was important to include an intervention component that aims to enhance social support, and evidence suggests remote social support can enhance smoking cessation outcomes.44 Although the remote support was well-received by the stakeholders, it is important to consider this mode of delivery may present challenges if participants have restricted access to technology and/or limited IT skills. Research has demonstrated that people with mental health conditions are at an increased risk of digital exclusion.45,46 However, this was not identified as a potential barrier during our consultations, and the increasing use of remote communication to provide health services during the Covid-19 pandemic illustrates the advantages of remote counseling.47 ## Limitations During stakeholder consultations, we collected patient and caregiver demographics including gender and smoking status. However, we did not collect demographics such as ethnicity and socioeconomic status, which would have further helped to contextualize the findings. Secondly, informal caregivers are under-represented in the stakeholder consultations. Recruitment of informal caregivers would have provided a valuable perspective for those who have supported relatives or friends who are current or ex-smokers and have experience using acute inpatient services. However, recruiting informal caregivers of smokers with a mental health condition is acknowledged as difficult,24 and consultations were conducted at the beginning of the pandemic so recruitment was conducted remotely, restricting the opportunity to meet with potential caregivers in inpatient wards. This highlights the need for further investigation into the role of informal support networks, and the development of targeted strategies to recruit informal caregivers. ## Conclusions People with mental health conditions are more likely to smoke than the general population, which contributes to widening tobacco-related health inequalities. This intervention aims to support smoking-related behavior change in patients discharged from a smoke-free mental health inpatient stay, encompassing components that have been guided by existing evidence and appropriate BCTs. It includes resources and approaches that have been developed in collaboration with stakeholders to ensure they appeal to patients with mental health conditions following a smoke-free stay. Using the BCW and TDF supported a theoretically and empirically informed process to define and develop a tailored intervention that acknowledges barriers and enablers to supporting smoking cessation in mental health settings. The result is a novel complex theory- and evidence-based intervention that will be formally tested in a randomized controlled feasibility study. ## Supplementary Material A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr. ## Funding This study is funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme (reference: NIHR200607). Elena Ratschen and Simon Gilbody are supported by the NIHR Yorkshire and Humber Applied Research Collaboration https://www.arc-yh.nihr.ac.uk. Lion Shahab is supported by a programme grant from Cancer Research UK (PRCRPG-Nov21\100002) and is member of SPECTRUM, a UKPRP-funded consortium (MR/S$\frac{037519}{1}$). Peter Coventry is supported by the UK Research and Innovation Closing the Gap Network + (ES/S$\frac{004459}{1}$) https://www.york.ac.uk/healthsciences/closing-the-gap/ and by the National Institute for Health Research (NIHR) Yorkshire and Humber Applied Research Collaboration https://www.arc-yh.nihr.ac.uk/. Tim Coleman is an NIHR Senior Investigator. The views expressed are those of the authors, and not necessarily those of the NIHR or the Department of Health and Social Care. ## Declaration of Interest L.S. is a HEFCE-funded member of staff at University College London. He has received honoraria for talks, an unrestricted research grant, and travel expenses to attend meetings and workshops from Pfizer and an honorarium to sit on an advisory panel from Johnson&Johnson, both pharmaceutical companies that make smoking cessation products. He has acted as paid reviewer for grant awarding bodies and as a paid consultant for healthcare companies. Other research has been funded by the government, a community-interested company (National Center for Smoking Cessation), and charitable sources. He has never received personal fees or research funding of any kind from alcohol, electronic cigarette, or tobacco companies. All other authors declare no conflicts of interest. ## Data Availability Data is available at: https://osf.io/jdw9y/. ## References 1. Peacock A, Leung J, Larney S. Global statistics on alcohol, tobacco and illicit drug use: 2017 status report.. Addiction. (2018) 113 1905-1926. PMID: 29749059 2. 2. ONS. 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